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Intermodal Passenger Flows on London's Public Transport Network

Intermodal Passenger Flows on London's Public Transport Network

Intermodal Passenger Flows on ’s Automated Inference of Full Passenger Journeys Using Fare-Transaction and Vehicle-Location Data

by Jason B. Gordon B.A., University of California, Berkeley

Submitted to the Department of Civil and Environmental Engineering and the Department of Urban Studies and Planning in partial fulfillment of the requirements for the degrees of Master of Science in Transportation and Master in City Planning at the Massachusetts Institute of Technology September 2012

© 2012 Massachusetts Institute of Technology. All rights reserved.

Signature of Author ...... Department of Civil and Environmental Engineering Department of Urban Studies and Planning August 10, 2012

Certified by Nigel H.M. Wilson Professor of Civil and Environmental Engineering Thesis Supervisor

Certified by Harilaos Koutsopoulos Professor of Transport Science, Royal Institute of Technology Thesis Supervisor

Certified by John P. Attanucci Lecturer & Research Associate, Department of Civil and Environmental Engineering Thesis Supervisor

Accepted by Alan Berger Professor of Urban Studies and Planning Chair, Master in City Planning Committee

Accepted by Heidi M. Nepf Professor of Civil and Environmental Engineering Chair, Departmental Committee for Graduate Students

Intermodal Passenger Flows on London’s Public Transport Network Automated Inference of Full Passenger Journeys Using Fare-Transaction and Vehicle-Location Data by Jason B. Gordon Submitted to the Department of Civil and Environmental Engineering and the Department of Urban Studies and Planning on August 10, 2012 in partial fulfillment of the requirements for the degrees of Master of Science in Transportation and Master in City Planning Abstract

Urban public transport providers have historically planned and managed their networks and services with limited knowledge of their customers’ travel pat- terns. While ticket gates and fareboxes yield counts of passenger activity in specific stations and vehicles, the relationships between these transactions—the origins, interchanges, and destinations of individual passengers—have typically been acquired only through costly and therefore small and infrequent rider sur- veys. Building upon recent work on the utilization of automated fare-collection and vehicle-location systems for passenger-behavior analysis, this thesis presents methods for inferring the full journeys of all riders on a large public transport network. Using complete daily sets of data from London’s Oyster farecard and iBus vehicle-location system, and alighting times and locations are inferred for individual bus passengers, interchanges are inferred between passenger trips of various public modes, and full-journey origin–interchange–destination matrices are constructed, which include the estimated flows of non-farecard passengers. The outputs are validated against surveys and traditional origin– destination matrices, and the software implementation demonstrates that the procedure is efficient enough to be performed daily, enabling transport provid- ers to observe travel behavior on all services at all times.

Thesis Supervisor: Nigel H.M. Wilson Title: Professor of Civil and Environmental Engineering

Thesis Supervisor: Harilaos Koutsopoulos Title: Professor of Transport Science, Royal Institute of Technology

Thesis Supervisor: John P. Attanucci Title: Lecturer & Research Associate, Department of Civil and Environmental Engineering

Acknowledgements

The research that culminated in this thesis evolved over a three-year pe- riod, during which I was extremely fortunate to have the guidance of multiple faculty members. Nigel Wilson and John Attanucci guided this work from its inception. Their intuitive knowledge of transit, standard of academic excellence, and complementary analytical styles guaranteed stimulating and fruitful discussion at each weekly meeting. Haris Koutsopoulos brought a similarly valuable perspective to the latter half of the project: his expertise and ingenuity strengthened the meth- odologies of Chapters 3 and 4 while providing the guidance to overcome the challenges addressed in Chapter 5. Haris and Nigel’s detailed reviews of the manuscript greatly strengthened it—their interest and close atten- tion are sincerely appreciated. Jinhua Zhao played a key role in guiding the early stages of this work, and the vision that he instilled was in mind throughout the project. Fred Salvucci kindly reviewed the manuscript as well. His policy insights were invaluable as always, and I’ll miss his thoughtful weekly provisioning of yucca donuts. This work would not have been possible without the support of (TfL). Shashi Verma, Lauren Sager Weinstein, and John Barry provided the invaluable opportunity to work with and learn from their exceedingly talented teams, while their own feedback and support for the project were truly encouraging. Andrew Gaitskell an- swered countless arcane technical questions and extracted mountains of data. James McNair explored the algorithms and the code itself, lending a welcome (and clever) second perspective in what had previously been a solitary endeavor. Malcolm Fairhurst and Tony Richardson presented an opportunity to test this research with a real-world problem, while Duncan Horne, Mark Roberts, Carol Smales, Mark Butlin, David Warner, Liam Henderson, Geoffrey Maunder, Dale Campbell, and Tim Cooper shared expert insights about all aspects of TfL and its data. The staff of London were equally instrumental in this project. Steve Robinson shared his unparalleled iBus expertise and, along with Nigel Hardy, Lisa Labrousse, Aidan Daly, and George Marcar, gener- ously and patiently answered complex technical questions. Steve and Kevin Field tediously extracted large volumes of data, Annelies de Koning created indispensable data extraction tools, and Bob Blitz and Fergus McGhee shared their expert knowledge of the bus network. Alex Phillips and Keith Gardner developed a user-requirements framework which strengthened and refined the tools developed in this research. Special thanks to Rosa McShane for her friendship and hospitality, and for al- ways handling logistic and bureaucratic issues. Additional support was provided by the Department of Civil and Environmental Engineering through a Schoettler fellowship and a teach- ing assistantship, in which I had the rewarding experience of teaching with and learning from George Kocur. Tomer Toledo gave valuable feed- back on the scaling problem, while Ginny Siggia and Kris Kipp guided me expertly through all manner of institutional hurdles. This project grew from an initial collaboration with Albert Ng, whose talents and creativity are as valued as his friendship. Mike Frumin gener- ously sacrificed hours of thesis-writing time to share his (locally) unparal- leled knowledge of London’s transport and information systems, while Winnie Wang and Yossi Ehrlich similarly lent their code, data, and meth- odological insights. Kevin Muhs exemplified the virtue of patience with his cheerful (and brave) de facto alpha testing, which yielded many valu- able discoveries and suggestions (among many mutual tangential but wel- come diversions). Gabriel Sánchez-Martínez shared his IPF source code, and helped vet scaling algorithms through numerous stimulating white- board discussions (which also benefitted from the insights of Sam Hickey, Nihit Jain, and Kari Hernandez). These and the other transit-lab students of 2010–2013 made the countless hours in the office far more enjoyable. Lastly, thanks to my family for their encouragement and support, and to Dini for absolutely everything. The work in these pages was sustained by your love and sacrifice. Contents

List of Figures 11 List of Tables ...... 14 List of Acronyms ...... 15 1 Introduction ...... 17 1.1 Motivation ...... 18 1.1.1 Full Journeys and Intermodal Travel 19 1.1.2 The State of the Art ...... 19 1.1.3 Application to London ...... 20 1.2 Objectives 21 1.3 Thesis Organization ...... 22 2 Public ...... 23 2.1 London’s Public Transport Network ...... 23 2.1.1 23 2.1.2 The 24 2.1.3 ...... 24 2.1.4 ...... 25 2.2 Data Sources ...... 25 2.2.1 Oyster 25 2.2.2 iBus ...... 29 2.2.3 Gateline and ETM ...... 29 2.2.4 Spatial Data ...... 30 3 bus Origin and Destination Inference 31 3.1 Previous Research ...... 32 3.1.1 Inferring Bus Origins Using AFC and AVL Data . . . . . 32 3.1.2 Inferring Bus Destinations Using AFC and AVL Data 33 3.2 Origin Inference ...... 34 3.2.1 Exploratory Analysis of Input Data ...... 35 3.2.2 Origin-Inference Methodology ...... 38 3.2.3 Origin-Inference Results and Sensitivity Analysis . . . . 40 3.3 Destination Inference 43 3.3.1 Input Data ...... 44 3.3.2 Destination-Inference Methodology ...... 47 3.3.3 Destination-Inference Results and Sensitivity Analysis 52 3.4 Summary ...... 58 4 Interchange Inference ...... 59 4.1 Concepts and Terminology ...... 60 4.2 Previous Research ...... 62 4.2.1 Discerning Activities Using AFC Data ...... 62 4.2.2 Interchange Inference Using AFC Data 62 4.3 Methodology ...... 64 4.3.1 Binary Conditions ...... 66 4.3.2 Temporal Conditions 68 4.3.3 Spatial Conditions ...... 70 4.4 Results and Sensitivity Analysis ...... 73 4.4.1 Sensitivity Analysis 73 4.4.2 Results ...... 80 4.4.3 Comparison with the London Travel Demand Survey . . .82 4.5 Summary ...... 83 5 Full-Journey Matrix Expansion 85 5.1 Problem Definition 85 5.2 Previous Research ...... 88 5.2.1 Iterative Proportional Fitting on Closed Networks 88 5.2.2 Application of IPF on London’s Public Transport Network 90 5.3 Input Data ...... 94 5.4 Methodology ...... 95 5.4.1 Problem Definition Revisited ...... 95 5.4.2 Problem Formulation and Solution ...... 98 5.5 Example: Test Network ...... 101 5.5.1 Validation 101 5.5.2 Comparison to Iterative Proportional Fitting 105 5.6 Application to the London Network ...... 108 5.6.1 Results ...... 109 5.6.2 Validation Against Iterative Proportional Fitting on the London Network 112 5.7 Summary ...... 114 6 Implementation 115 6.1 Overview ...... 116 6.2 Process ...... 118 6.2.1 Step 1: Preprocessing and Nearest-Stop Calculation . . . 118 6.2.2 Step 2: Origin Inference and Daily History Reconstruction ...... 120 6.2.3 Step 3: Destination and Interchange Inference . . . . . 122 6.2.4 Step 4: Full-Journey Construction ...... 123 6.2.5 Step 5: Full-journey Matrix Expansion ...... 124 6.3 Results and Performance 126 6.4 Summary ...... 126 7 Applications 127 7.1 Bus Applications ...... 128 7.1.1 Boarding/Alighting/Flow Profiles and Route- OD Matrices ...... 128 7.1.2 Bus Passenger Travel Time and Distance ...... 130 7.2 Cross-Modal and Full-Journey Applications ...... 132 7.3 Observing Changes in Rider Behavior and Demand . . . . . 134 7.4 Summary ...... 136 8 Conclusion ...... 139 8.1 Summary and Findings 139 8.2 Recommendations ...... 141 8.3 Future Research ...... 143 References ...... 147 List of Figures

2-1 Schematic travel-zone map of London rail services ...... 26 2-2 Geographic map of London rail services ...... 27 2-3 Underground, National Rail, and DLR services in . . 28 3-1 Oyster, BCMS, and iBus data for a portion of a single bus route . . . 36 3-2 Activity diagram of the origin-inference process ...... 39 3-3 Distribution of origin-inference error for all Oyster bus boardings . 42 3-4 Letter-designated bus stops near Tube station 45 3-5 Elephant and Castle stops S and T ...... 45 3-6 Stop and station locations in ...... 46 3-7 Activity diagram of the destination-inference process 49 3-8 Example of destination inference 50 3-9 Distribution of average out-of-system speeds ...... 53 3-10 Distribution of Euclidean distances between candidate alighting stops and next fare transaction ...... 55 4-1 Activity diagram of the interchange-inference process ...... 67 4-2 Two potential interchange locations yielding equal cumulative Euclidean distances ...... 71 4-3 Interchange boundaries for various circuity ratios 72 4-4 Time between cardholders’ successive journey stages ...... 74 4-5 Distribution of elapsed times between candidate bus-stop arrivals and subsequent bus boardings...... 77

11 4-6 Distribution of circuity ratios for potential interchanges . . . . . 78 4-7 Park to Bond Street via ...... 79 4-8 to Angel via Hackney or Liverpool Street 79 4-9 Distribution of potential full-journey distances ...... 80 4-10 Distribution of inferred journey durations 82 4-11 Full journeys per passenger per day 83 4-12 Stages per journey 83 5-1 The test transit network ...... 86 5-2 IPF contingency table for the test network ...... 89 5-3 Example of the control-total adjustment process ...... 93 5-4 Example of estimated passenger flow on the test network . . . . .97 5-5 Formulation and results of the full-journey scaling process, as applied to the test network 102 5-6 Convergence of scaled transaction-node flows toward control totals ...... 104 5-7 Comparison of actual and estimated itinerary flows 104 5-8 Comparison of passenger flows for the test network ...... 107 5-9 Hourly control totals for Victoria Underground Station . . . . . 108 5-10 Histograms of scaling factors ...... 110 5-11 Comparison of sample (or seed) flows to scaled flows ...... 111 5-12 Distributions of OD-pair scaling factors on London's rail network . .112 5-13 Comparison of full-journey scaling and IPF by OD pair ...... 113 6-1 Overview of processes, inputs, and outputs ...... 116 6-2 Class diagram of origin-, destination-, and interchange-inference package ...... 117 6-3 Activity diagram of origin-, destination-, and interchange-inference processes 119 6-4 Activity diagram of full-journey matrix-expansion process . . . 125 7-1 Boarding/alighting/flow profile for Route 488 inbound . . . . . 129 7-2 Origin–destination matrix for Route 488 inbound ...... 129

12 7-3 Comparison of average journey-stage lengths: GLBPS vs. OD-inferred Oyster ...... 130 7-4 Comparison of average journey-stage lengths by bus route: GLBPS vs. OD-inferred Oyster ...... 131 7-5 Origins of full journeys ending at Circus station . . . . . 133 7-6 Frame from a time-lapse animation of a full day’s inferred Oyster activity 134 7-7 First origins of day for riders of route 205x ...... 135 7-8 Boarding/alighting/flow profile for route 488, before and after its extension to Junction 137 7-9 Origin–destination matrix for the extended Route 488 inbound 137

13 List of Tables

3-1 Detailed origin-inference results, ten-day average 41 3-2 Summary of origin-inference results 43 3-3 Cumulative frequencies of observed inter-stage distances 55 3-4 Destination-inference results ...... 57 4-1 Median elapsed times between Oyster cardholders’ consecutive bus boarding transactions ...... 64 4-2 Tests applied during the interchange-inference process ...... 66 4-3 Interchange-inference results: ten-day average ...... 81 5-1 London Underground weekday time periods ...... 91 5-2 Satisfaction of farebox and station-gate control totals 109

14 List of Acronyms

AFC Automatic fare collection APC Automatic passenger counter ATOC Association of Train Operating Companies AVL Automatic vehicle location BCMS Bus Contract Management System BODS Bus Origin–Destination Survey DLR ELL Line ETM Electronic GLBPS Greater London Bus Passenger Survey IPF Iterative proportional fitting LATS London Area Travel Survey LTDS London Travel Demand Survey MBTA Massachusetts Bay Transportation Authority MLE Maximum likelihood estimate NLC National location code OD Origin–destination ODX Origin–destination–interchange OSI Out-of-station interchange RODS Rolling Origin–Destination Survey TfL Transport for London

15 16 Introduction 1

Urban public transport providers have historically planned and managed their networks and services with limited knowledge of their customers’ travel patterns. Unlike airlines or interurban rail providers, who typically issue tickets specifying distinct origins, destinations, and transfer points— often in designated vehicles and seats—the nature of urban transit opera- tions has necessitated fare-payment schemes which yield only aggregate ridership data, at resolutions no finer than that of the station gate or bus farebox.1 Put simply, transit agencies have not been able to easily observe where their passengers travel. Farebox and station-gate data are useful for describing ridership from the perspective of the transit system, but passenger-centric data, such as the origins, destinations, and transfer points of individual customers, have traditionally been collected only through costly surveys (which are therefore conducted infrequently using small rider samples). The grow- ing adoption of automated data-collection systems, however, is provid- ing transit operators with vast stores of disaggregate data, which can be manipulated to reveal travel information from a rider-focused perspective. But the distillation of passenger-centric information is not trivial, as most automated data-collection systems—namely automated fare collection (AFC), automated vehicle location (AVL), and automated passenger coun-

1 High peak-period passenger volumes, high but often variable service frequencies, and dense, complex networks have led most transit agencies to adopt flat or zonal fare policies, designed to expedite fare processing and maximize passenger throughput.

17 ter (APC)—have been designed for other purposes: their applicability to rider-perspective analysis is mostly serendipitous.2 This thesis builds upon recent work in the synthesis of passenger-cen- tric public transit information and demonstrates the feasibility of pro- cessing the complete set of data for a large multi-modal transit provider in a way that is suitable for execution on a daily basis. Using London’s transit network as an example, origins and destinations are inferred for bus journey stages, transfers between stages of various modes are inferred, and a multimodal origin–interchange–destination matrix of full journeys is constructed, including estimated flows for non-AFC passengers. Ap- plications are presented at various spatial and temporal scales from both the customer and system perspectives, including applications for service planning, rider behavior analysis, and project impact analysis. The remainder of this chapter elaborates on the motivation for this research, presents its specific objectives and the approach taken, and out- lines the structure of this document.

1.1 Motivation

Passenger-centric transit information has historically been costly to cap- ture. Origin–destination (OD) information for an the individual bus route could be estimated at the stop level from manual boarding counts com- bined with a sample of rider surveys (Ben Akiva et al 1985) or, at the expense of accuracy, with boarding counts alone (Simon and Furth 1985, Mishalani et al 2011). As survey response rates continue to decline, how- ever, increasing their cost and bias (Stopher 2008, qtd. in Simon 2010), AFC data (supplemented in many cases by AVL data) have been shown to provide similar OD information at larger scales and at lower cost (Park et al 2008). A similar approach can be taken for rail networks requiring both exit and entry transactions, since AFC transactions can be associated by card number (Gordillo 2006, Chan 2007).

2 AFC systems, which read magnetic-stripe or RFID “smart” cards containing transit passes or credit, were designed to streamline fare collection. AVL systems track vehicle locations for the onboard announcement of stop information and to provide control centers with real- time fleet-management capability. APC systems track aggregate boardings and alightings but are not directly linked to specific passengers or origin–destination pairs.

18 1.1.1 Full Journeys and Intermodal Travel

In addition to relating entry and exit records, AFC data can be used to as- sociate all of a cardholder’s daily transactions, enabling transit providers to more easily discern interchanges from activities in order to infer cus- tomers’ true origins and destinations. While analysis of the resultant full journeys and daily travel histories can provide valuable information for transport planners and operators, interchanges themselves are worthy of study because of their influence on customers’ travel decisions (Wardman and Hine 2000, Hine and Scott 2000, Guo et al 2007, Eom et al 2011) and their role in enabling intermodal travel (Institute of Logistics and Trans- port 2000, Seaborn et al 2009, Transport for London 2002 & 2009). The acceptance of AFC cards on multiple modes allows the observa- tion of intermodal transit journeys, which are becoming increasingly im- portant to transit providers and policy makers as a means of improving mobility through the elimination of barriers to cross-modal travel. De- scribing London’s public transport strategy at the turn of the twenty-first century, then-mayor argued that “[modal] integration is not just about making the public transport network more attractive to existing and potential passengers, it is also about how the transport sys- tem can contribute to the achievement of broader economic, social, and environmental objectives” (Transport for London 2001). The economic benefits of increased mobility are supported by the work of Krizek (2003) and by Anas (2007), who notes that time savings from chained trips tend to be reinvested in additional travel and activities, yielding an overall in- crease in utility. London’s exceptionally large, dense transit network and the acceptance of the Oyster on the region’s several historically independent but now unified transit modes provides a valuable opportu- nity for intermodal integration and its monitoring through automatically collected data.

1.1.2 The State of the Art

Several scholars and practitioners have advanced the application of auto- matically collected transit data in the past decade. Building upon earlier work, Chu and Chapleau (2008) infer full passenger journeys for a bus network, then analyze multiple days of AFC data to estimate general activ- ity types for cardholders’ recurrent journeys (2010). Farzin (2008) auto-

19 mates the majority of the destination-inference process on a portion of a large bus network and constructs a zonally aggregated OD matrix. Mu- nizaga et al (2011) automate the inference of origins and destinations for a large multi-modal network and, using a simple interchange-inference algorithm, construct a full-journey origin–destination matrix for AFC cardholders. The synthesis and refinement of previous research, coupled with more robust and efficient processing algorithms, holds promise for the prospect of implementing passenger-centric data analysis in ways that can be ap- plied by transit operators on a daily basis.

1.1.3 Application to London

Utsunomiya et al (2006) conclude that the market penetration of AFC cards is critical to the accuracy of passenger-centric analyses (2006). With pen- etration rates of over 90 percent on buses and 80 percent on the London Underground system, Oyster’s six million daily bus boardings and five million station entries provide an exceptionally large sample from one of the world’s foremost transit networks. Transport for London (TfL), the transportation agency for the Greater , oversees the city’s transit system and maintains complete databases of recent AFC, AVL, and ancillary transit data. TfL’s data have been used to construct OD matrices for the gated Underground system (Gordillo 2006, Chan 2007), one of its ungated systems (Henderson 2010), and for individual bus routes (Wang et al 2011). The data have also been used to track service reliability and to inform service-planning proposals (Chan 2007, Uniman et al 2010, Ehrlich 2010, Frumin 2010). TfL’s data sources have recently been used to assess the impacts of new capital projects (Ng 2011, Muhs 2012), fare policy changes (Jain 2011), and service-reliability metrics (Schil 2012).3 The incorporation of previous London research with methodologies from other case studies presents an opportunity to extend traditional analyses to the observation of travel patterns before and after the introduction of new (or the alteration of existing) public transport services in London. And by developing an ef- ficient and flexible way to repeat and extend such analyses, TfL can be

3 Muhs (2012) and Schil (2012) used the tools and algorithms developed in this thesis for part of their analyses.

20 equipped to internally analyze rider behavior during upcoming events such as the city’s hosting of the 2012 Olympic and Paralympic Games and the launch of service later in the decade.

1.2 Objectives

This research tests the hypothesis that a flow matrix of full intermodal passenger journeys can be estimated flexibly and efficiently using entire populations of automatically collected data from a large public transit network, in a way that can be performed by a transit provider on a daily basis. More specifically, this thesis seeks to fulfill the following objectives:

·· Infer boarding and alighting locations and times for bus journey stages. Bus AFC transactions typically include service information and boarding times but lack spatial information, while alighting information is not recorded at all. Error-correction algorithms should be applied to ensure the highest possible origin- and destination-inference rates.

·· Infer interchanges between journey stages of any AFC-enabled mode. Infer in- terchanges as accurately as possible, taking into account observed or in- ferred access and egress distances, observed bus headways, path circuity, and transfer time.

·· Generate an intermodal full-journey origin–interchange–destination matrix cover- ing all modes in the transit network for which data are available. Generate counts of each unique itinerary observed to have been taken by one (or more) passengers, where itinerary is defined as a unique combination of origin, destination, and transfer locations. Expansion factors, which are used to scale itinerary flows to account for unobserved travelers, should be esti- mated in such a way that the counts of their constituent journey stages satisfy the various control totals that are available for each transit mode.

·· Conduct all inference and expansion processes at the finest possible level of disag- gregation. Doing so will afford downstream processing and reporting tools the flexibility to choose a level of aggregation appropriate for a given task.

21 ·· Allow parameters to be adjusted at runtime. Bagchi and White (2003, 2004) note the importance of calibrating the parameters of rule-based processes in accordance with empirical observations or surveys, while Timmermans et al (2002) show that travel parameters vary across networks and societies. Parameters should be adjustable by the user and should not be embedded in code.

·· Streamline the processing tools so that they can be run on a daily basis. Tools should be able to complete their processing overnight, and should ideally run in less than 30 minutes so that users can more easily experiment with differ- ent parameter values.

·· Demonstrate applications. Applications of each process should be demon- strated (e.g., bus journey stages, full journeys, and the expanded OD ma- trix), from both the operational and passenger perspectives, and at various levels of aggregation.

1.3 Thesis Organization

The methods developed in this thesis can be grouped into three catego- ries: bus origin and destination inference, interchange inference, and full- journey matrix expansion. Since each category has a distinct method- ological background, each is presented in its own chapter. Following an overview of London’s public transport system and operations in Chapter 2, the three phases of the problem are presented in chapters 3, 4, and 5, with each including a review of the relevant literature and a discussion of their validation and results. The technical implementation of the meth- ods is discussed in Chapter 6 followed by a demonstration of applications in Chapter 7. Chapter 8 reflects on the study’s findings and presents its conclusions and recommendations.

22 Public Transport in London 2

The methods presented in this thesis were developed and tested using data from London’s public transport network. This chapter describes the network’s structure and services, and introduces the data sources used to conduct this research.

2.1 London’s Public Transport Network

London’s transit network is planned and managed—and partially oper- ated—by Transport for London (TfL), a government body serving the Greater London metropolitan area and led by a 17-member board ap- pointed and chaired by the (Transport for London 2011). TfL oversees most transportation services in Greater London, in- cluding public transport, taxis, traffic management (including congestion charging in the city center), river services, bicycle sharing, and .

2.1.1 London Buses

London Buses, a subgroup of TfL’s Surface division, plans and man- ages the metropolitan area’s network of over 800 routes and 21,000 stops, which provide roughly six million passenger trips daily. London’s fleet of over 8,500 iconic red double-decker (and single-deck) buses is operated by private companies, who bid for contracts awarded by the agency (Trans- port for London 2011, 2012).

23 Bus riders are charged a flat fare, regardless of travel location or time of day (although some riders are eligible for discounts). Fares can be paid in cash on board (or compulsorily at ticket vending machines at some Cen- tral London bus stops) or riders can tap their Oyster cards upon boarding, to deduct credit or validate travel passes stored on the card.

2.1.2 The London Underground

The Underground, or Tube, is London’s metro system, offering high fre- quency service on 11 lines, several of which include multiple branches (see figures 2-1 and 2-2).1 Underground fares are assessed according to the geographic travel zone of the rider’s starting and ending locations (Figure 2-1), requiring users to validate their fare media upon both entry and exit. The Underground system is often at capacity during peak hours, and relieving this congestion is a major goal of many of TfL’s fare policies and capital projects. Higher fares are charged during peak periods to incen- tivize off-peak travel, while recent and current projects such as the Extension and Crossrail were designed in part to provide alternatives to Underground service at key bottlenecks.

2.1.3 National Rail

National Rail is the brand name of the Association of Train Operating Companies (ATOC), a group of 24 private firms that provide commuter and rail services throughout Great Britain. Several National Rail lines serve London, but all terminate outside (or slightly within) the bounds of the Circle Line, the quasi-orbital Underground line that loosely defines the city’s center (see Figure 2-3). Much of the congestion on the Underground is due to passengers transferring between National Rail and the Tube during peak periods, and stations serving both Un- derground and National Rail lines exhibit some of the highest passenger volumes in London.

1 Although the term Tube originally referred to the seven deep-bore rail lines drilled into the earth (thus exhibiting the cylindrical and passageways that inspired the nickname), the term is now typically used to refer to both the deep-bore lines and the four “subsur- face” lines, which were excavated directly from the streets above using the “cut-and-cover” technique.

24 2.1.4 London Rail

In addition to the Underground, TfL provides rail service on the , Docklands Light Railway (DLR), and . All three modes are planned and managed by TfL’s London Rail group, which also liaises with ATOC to coordinate National Rail services within Greater Lon- don (Transport for London 2011). The Overground system comprises a series of rail lines primarily in travel zones two through four, which are being developed into a subur- ban orbital network encircling most of the Underground system. Some tracks are shared with National Rail and Freight services, and many sta- tions were acquired from National Rail.2 The Docklands Light Railway is an automated system operating on dedicated rights of way in the redeveloped Docklands area, one of Greater London’s three primary commercial cores. DLR services connect with Un- derground and Overground lines in East London, and one branch extends into another commercial core in the . The Tramlink system consists of three light rail lines serving , the southernmost borough in Greater London. Tramlink connects with Underground and National Rail services and, with the opening of the extended East London Line, the .

2.2 Data Sources

2.2.1 Oyster

The is accepted on most TfL transport modes and typically registers 16 million transactions daily, or roughly 10 million passenger trips. Oyster use is incentivized by reduced fares, designed to increase passenger throughput by streamlining fare validation at station gates and bus fareboxes.

2 A notable exception is the East London Line, a former Underground service that was ex- tended along a combination of disused above-grade rail viaducts and newly acquired rights of way (Ng 2011, Muhs 2012).

25 Towards Towards Towards Towards Towards Towards Towards Towards Towards Towards Towards Towards Towards Towards Airport Parkway North Cheshunt, Hertford East and Stansted Airport Aylesbury Chesham Hemel Hempstead Welwyn Garden City Bakerloo ZONE ZONE ZONE ZONE ZONE ZONE Parkway Hertford North Cheshunt Cheshunt, Hertford East and Stansted Airport Junction Crews Hill Epping ZONE ZONE ZONE ZONE ZONE ZONE Central & Hadley Wood Street En eld Lock Bakerloo 9 8 8 8 7 6 Watford Junction Crews Hill Epping Circle Central Elstree & Borehamwood Hadley Wood Turkey Street En eld Lock ZONE Gordon Hill Theydon Bois limited service Watford High Street En eld Amersham 9 8 8 8 7 6 District Watford En eld Chase Town ZONE Circle Gordon Hill Theydon Bois Hammersmith & City High Barnet Oakwood Debden ZONE En eld Chalfont 7 Grange Park limited service Watford High Street Cockfosters & Latimer District Town Jubilee 5 Bush Hill Watford ZONE En eld Chase Croxley Southgate Brimsdown & Whetstone Park High Barnet New Barnet Oakwood Debden Metropolitan Southbury Hammersmith & City Chalfont 7 Towards Grange Park Carpenders Park & Latimer Bushey Northern High Towards Jubilee 5 Bush Hill Woodside Park Wycombe Moor Park Shenfield and Croxley Southgate Brimsdown Loughton Ponders End Totteridge & Whetstone Park Southend Airport Metropolitan Southbury Victoria West Roding Towards Chorleywood Carpenders Park Winchmore Hill Northwood Valley Arnos Grove ZONE Mill Hill Broadway Northern High Oakleigh Park Chingford Buckhurst Hill Waterloo & City West Edmonton Green Highams Park Woodside Park Towards Northwood Headstone Lane Wycombe Rickmansworth Moor Park Shenfield and Hillingdon Hills Mill Hill East Piccadilly Docklands Light Railway 4 Bowes Park Angel Road Bounds Green Ponders End Woodford Hatch End Palmers Green Southend Airport Ruislip Silver Street Grange Hill Edgware Roding London Overground Ickenham Harrow & Finchley Central Victoria West Finchley Harold Wood Northwood Valley Chigwell London Tramlink Ruislip Queensbury Wood Street ZONE Mill Hill Broadway Manor Waterloo & City West Ruislip Stanmore New Southgate Edmonton Green White Hart Lane Hainault Highams Park Emirates Air Line Kenton East Finchley Northumberland Northwood Headstone Lane Park Hillingdon Mill Hill East Eastcote Kingsbury Hills Central 4 Bowes Park Angel Road Ruislip Hornsey Towards Docklands Light Railway Turnpike Lane Bruce Grove Canons Park Woodford Gardens Harrow- Northwick Preston Southend and Silver Street Grange Hill on-the-Hill Park Ruislip Harrow & Wealdstone Burnt Oak Park Road Hendon Brent Cross London Overground Uxbridge Ickenham Finchley Central limited service Harringay Blackhorse Barkingside Pinner Harold Wood ZONE Archway Ruislip Wood Street Golders Green Seven Sisters Hale Road London Tramlink Queensbury Harringay Manor Alexandra Palace Wood Green First Great Western White Hart Lane Hainault South Kenton Crouch Hill Green Lanes Central Newbury Park Kenton East Finchley Northumberland 3 Emirates Air Line North Harrow Colindale Greater Anglia Kingsbury Park Wembley Manor South Emerson Eastcote Harrow Green Hendon Central Stadium House Tottenham Redbridge Park Ruislip Hornsey Fairlop Towards Chiltern Railways Highgate Turnpike Lane Gospel Oak Walthamstow Gardens Harrow- Northwick Preston Bruce Grove South Woodford Southend and Park Kilburn St. James Queen’s Road Rayners Lane Gidea Park Northolt Sudbury Hill Sudbury & Wembley Central Street Upminster on-the-Hill c2c Park Road Hendon Brent Cross Shoeburyness Finchley Road Kentish Town Upper Holloway Chadwell Bridge limited service & Frognal Leyton Leytonstone limited service Harringay Tottenham Blackhorse Barkingside Southern Stonebridge Park West SPECIAL FARES APPLY Midland Road Heath First Capital Connect South Ruislip Neasden ZONE Archway Road limited service Oyster PAYG and West Harrow Golders Green Seven Sisters Hale are not Harringay Walthamstow Southeastern Sudbury Town Rectory Road South Harrow Kentish Town valid on Southeastern Ockendon First Great Western Dollis Hill Crouch Hill Green Lanes Central Southeastern high speed Belsize Park Arsenal high speed. South Kenton Newbury Park Willesden Junction Park Stratford 3 Cricklewood Hampstead Tufnell Park Holloway Road For details, visit International Greater Anglia Chalk Farm southeasternrailway.co.uk Clapton Leyton Leytonstone Sudbury Hill Willesden High Road Wembley Manor Stamford Hill South Snaresbrook Romford Emerson Kensal Rise Brondesbury East Harrow North Wembley Green Finchley Road Camden Heathrow Connect Stadium Hampstead Heath House Tottenham Park Upminster Interchange stations ZONE Hackney Redbridge Road Caledonian Road Downs Chaord Northolt Walthamstow Drayton Dalston Hackney Hackney Dagenham Hundred Heathrow Express Kilburn Gospel Oak St. James Airport Park Kingsland Central Wick Heathway Park Queen’s Road Queen’s Park 2 Mornington Lakeside Northolt Sudbury Hill Sudbury & Wembley Central Street Wanstead Gants Hill Upminster Crescent Riverboat services Manor Park London Midland Harrow Road Finchley Road Stoke Newington Bridge Caledonian Stratford Kentish Town Upper Holloway Leyton Chadwell St. Pancras London Fields Becontree limited service & Frognal SPECIAL FARES APPLY Leytonstone Emirates Air Line Road & Highbury & Southern Stonebridge Park West Midland Road Heath (opening summer 2012) Kilburn Park Kilburn South Inter- Dalston Maryland Forest St. John’s Wood King’s Barnsbury Islington Junction Woodgrange Oyster PAYG and Hornchurch South Greenford Maida Vale High Road Hampstead national Cross Gate limited service Archway Park Southeastern Travelcards are not Station in both fare zones Stratford Towards Sudbury Town Harlesden Rectory Road Warwick Edgware Great Kentish Town valid on Southeastern Ockendon Avenue High Street Southend Brondesbury Belsize Park Finsbury Park Tramlink fare zone Road Marylebone Portland Euston Angel Southeastern high speed West Hampstead Arsenal high speed. Elm Park Street Willesden Junction Park Stratford Emirates Air Line fare zone Road Upney Holloway Road For details, visit International Castle Bar Oak Pudding Dagenham Rainham Pureet Grays South West Trains Greenford Chalk Farm southeasternrailway.co.uk Clapton Leyton Leytonstone Farringdon Mill Lane Abbey Road Dock High Road Goodmayes Edgware Warren Euston Bethnal Alperton Kensal Rise Brondesbury Camden Town Dagenham East ZONE ZONE ZONE Park Royal ZONE ZONE ZONE Road Street Finchley Road Square Barbican Green Bow Upton ZONE Camden Hackney Regent’s Liverpool by-Bow Plaistow Park East Ham Interchange stations Kensal Green Seven Kings Chaord West Westbourne Park Russell Street Road Road Caledonian Road Downs Drayton Green Acton Park Drayton Dalston Hackney Hackney Dagenham Hundred 6 5 4 North 3 2 Bayswater 1 Goodge Square Barking Airport Perivale Park Canonbury Kingsland Central Wick Wanstead Park Ilford Heathway Latimer Street High Street Bow Queen’s Park 2 Swiss Cottage Mornington Lakeside Road Church Crescent Ealing North Shepherd’s Notting Lancaster Bond Oxford Chancery West Ham Riverboat services Acton ZONE Green ZONE ZONE ZONE ZONE ZONE Stratford Manor Park West West Broadway Main Acton White City Bush Hill Gate Gate Street Circus Holborn Lane Caledonian Homerton Hayes & Devons Road London Fields Becontree Drayton Harlington Ealing Line Aldgate Emirates Air Line St. Pancras Road & Tottenham St. Paul’s Kilburn Park Kilburn South Highbury & Dalston East Holland Marble City 1 East 2 Star Lane 3 4 5 6 (opening summer 2012) Inter- King’s Maryland Forest Wood Park Court Road Langdon Park St. John’s Wood Barnsbury Islington Junction Woodgrange Towards Acton Arch Covent South Greenford Maida Vale High Road Hampstead national Cross Cambridge Heath Gate Acton Lane Garden Bank Aldgate Archway Station in both fare zones Park High Street All Saints Warwick Stratford Towards Central Kensington Mansion Canning Royal Custom House Edgware Great Haggerston Bethnal Green Leicester High Street Southend Shepherd’s Kensington House Shadwell Westferry Blackwall Town Victoria for ExCeL Tramlink fare zone Avenue Road Marylebone Baker Street Portland Angel Ealing Bush Market Square Euston Common (Olympia) Hyde Park Corner Piccadilly Street South Blackfriars Prince Regent Hanger Lane Essex Road Circus Cannon Poplar East Emirates Emirates Air Line fare zone Upney Acton Knightsbridge Monument Tower Tower Royal Oak Pudding Dagenham Rainham Pureet Grays SPECIAL FARES APPLY Goldhawk Road Charing Street Hill Gateway India Royal Albert Castle Bar Park Farringdon Abbey Road Oyster PAYG and Acton Cross Hoxton Mill Lane Dock Travelcards are not South Town Barons Gloucester Temple West Park Edgware Warren Bethnal valid on Hammersmith Sloane Fenchurch Street Euston Old Street Ealing Court Road West India Silvertown ZONE ZONE ZONE Park Royal ZONE ZONE ZONE Road Street Mile End Heathrow Connect Square Victoria Westminster Cyprus Paddington Square Barbican Green Bow Bromley Upton between Quay Liverpool North elds Regent’s by-Bow Plaistow Park East Ham Hayes & Harlington North Pontoon Dock Gallions Reach West Westbourne Park Russell Street Road and Turnham Stamford Ravenscourt West Earl’s South St. James’s Embankment Drayton Green Acton Park Shoreditch or on Heathrow Express. Beckton 6 5 4 3 2 Bayswater Goodge Square Green Brook Park Kensington Park 1 Court Kensington for The O North Ealing Ladbroke Grove 2 Latimer Street High Street Bow Road Moorgate Church East West Ealing North Notting Lancaster Bond Oxford Chancery West Ham Brompton Pimlico Acton Shepherd’s ZONE Stepney Green ZONE ZONE ZONE ZONE ZONE Hounslow Canada Water Heron Quays Broadway Acton White City Hill Gate Gate Street Circus Holborn Lane Waterloo Emirates West Hayes & West Main Bush Central South Quay Devons Road and Greenwich Drayton Harlington Southall Hanwell Ealing Line St. Paul’s Aldgate Hounslow Peninsula Whitechapel Broadway Waterloo East London Crossharbour King George V East Holland Queensway Marble Tottenham City East West 1 2 Star Lane 3 4 5 6 Bridge Quays Mudchute Court Road Langdon Park Bridge Wood Park Arch Covent Thameslink Hatton Towards Imperial Lane Garden Bank Aldgate Cross Chiswick Borough Acton High Street Wharf Slough Central All Saints Vauxhall Kensington Green Park Mansion Canning Royal Custom House Leicester Park Cutty Sark for Westcombe Shepherd’s House Shadwell Westferry Blackwall Town Victoria for ExCeL Heathrow Parsons Green Elephant & Castle Ealing Kensington Square Terminals Syon Lane Maritime Dockyard Bush Market (Olympia) Hyde Park Corner Piccadilly 1, 2, 3 Common South Prince Regent Bridge Belvedere Circus Blackfriars Poplar East Emirates Greenwich Maze Charlton Abbey Acton Cannon Monument Tower Tower Limehouse Barnes Bridge ZONE Woolwich SPECIAL FARES APPLY Goldhawk Road Knightsbridge Charing Royal Docks River Thames Hill Arsenal Wood Street Hill Gateway India Royal Albert Queenstown Queen’s Road Deptford Bridge Oyster PAYG and Acton Cross Road (Battersea) Oval North 2 Travelcards are not South Town Barons Gloucester Temple Fenchurch Street West Beckton Park Sheen Barnes Junction valid on Hammersmith Sloane Road Elverson Road Ealing Court Road West India Silvertown Heathrow Heathrow St. Johns Towards Heathrow Connect Square Victoria Westminster Wapping Cyprus Terminal Terminal 4 Richmond Mortlake Putney Wandsworth Clapham High Street Loughborough Ebbsfleet between Quay New Cross Gate Boston Manor 5 St. Margarets Town Junction Blackheath International Hayes & Harlington North elds Pontoon Dock East Putney Clapham North River Thames North Gallions Reach Hounslow and Heathrow Airport Embankment Chiswick Turnham Stamford Ravenscourt West Earl’s South St. James’s Greenwich South elds Earls eld Clapham or on Heathrow Express. Osterley Beckton Park Green Brook Park Kensington Court Kensington Park Rotherhithe for The O 2 Honor Oak Park Common Towards Haydons Clapham South East Hounslow Bermondsey Road Canary Wharf Staines Forest Hill East West Wimbledon London City Airport North Dulwich Brompton Pimlico Canada Water Whitton Sydenham Hounslow Southwark Heron Quays Lee Park Central Waterloo Emirates Bec West Sydenham South Quay Greenwich Dundonald Dulwich Hill Catford Bridge and Road Fulham Strawberry Tooting Hounslow Waterloo East London Crossharbour Peninsula King George V Broadway Towards Gunnersbury Broadway Hill ZONE Bellingham West Kew Lambeth Grove Park and Bridge Mudchute Crystal Bridge North Towards Park Tulse Lower Sydenham Hill Hatton Hill West 3 Palace Elmstead Imperial Island Gardens Hill Norwood Gipsy Hill Woods Cross Chiswick Borough Wimbledon Tooting East Sundridge Park Wharf New Beckenham Brentford Hampton Fulwell Hampton Kingston Chase House Bromley North Battersea Vauxhall Ravensbourne St. Mary Cray Wick Towards Heathrow Park Cutty Sark for Westcombe Woolwich Penge West Parsons Green Elephant & Castle Streatham and Terminals Syon Lane Deptford Maritime Greenwich Park Dockyard Plumstead Motspur Bromley Kew Gardens Park South 1, 2, 3 South Bermondsey Belvedere Road Streatham Petts Putney Bridge Common Birkbeck Avenue Beckenham Beckenham Wood Greenwich Maze Charlton Woolwich Abbey Morden Road Road Barnes Bridge ZONE Phipps Bridge Junction Isleworth River Thames Hill Arsenal Wood Harrington Queenstown Malden ZONE Queen’s Road Peckham New Cross Deptford Bridge Erith Hampton Thames East elds Norwood Road Oval Court Ditton Manor Belgrave Walk Road (Battersea) Kennington Junction Clock House North 2 Chels eld Mitcham 4 Sheen Barnes Elverson Road South Merton Wandsworth Road Stockwell St. Johns Slade Green Heathrow Heathrow Towards Arena Knockholt Terminal Terminal 4 Putney Loughborough Lane Marsh Richmond Mortlake Wandsworth Clapham High Street Ebbsfleet Morden South 5 Junction Denmark Hill Nunhead New Cross Gate Blackheath Kidbrooke Falconwood Bexleyheath Wandle Park Woodside Eden Park St. Margarets East Putney Town International Mitcham Therapia Ampere Clapham North Junction Reeves Blackhorse Lane Hounslow Worcester St. Helier Lane Way Corner West Croydon Peckham Rye Brockley Lewisham Eltham Welling Barnehurst Park South elds Earls eld Clapham Towards Common Wellesley Road Hayes Sevenoaks Wandsworth Honor Oak Park Lebanon Common North Towards Twickenham Wimbledon Park Haydons Clapham South George Street Road Road Staines Feltham Balham Brixton Forest Hill Hither Green West Sutton East Ladywell Stoneleigh Church Sandilands Wimbledon Crofton Park Towards Street Croydon North Dulwich and Waddon Lloyd Park Whitton Sydenham Albany ZONE Tooting Herne Hill Lee Park Crayford Chessington South Wallington Coombe Lane West Sydenham New Eltham Dundonald Bec Sutton Carshalton Purley Oaks Dulwich Hill Catford Catford Bridge Beeches 5 Gravel Hill Road Strawberry Tooting Mottingham Sidcup Bexley Purley Towards Belmont Addington Village Hill Broadway ZONE Bellingham Towards Raynes Park Grove Park Dartford and Fieldway Crystal Guildford Town Reedham Tulse Gravesend West Towards Palace Lower Sydenham Beckenham Hill ZONE King Henry’s Drive New Malden Streatham Hill West Elmstead Chipstead 3 Renamed Shepperton Riddlesdown Colliers Wood Hill Norwood Gipsy Hill Sundridge Park Woods Ewell East Coulsdon Town Wimbledon Tooting Penge East Kingswood on 12th April. 6 New Beckenham Hampton Fulwell Teddington Hampton Kingston Norbiton Chase Kent House Bromley North Chislehurst Coulsdon South Upper Ravensbourne Bickley St. Mary Cray Whyteleafe South Wick South Wimbledon Towards Downs Streatham Penge West Swanley and Towards Figure 2-1. Schematic travel-zone map of London rail services (source: TfL). Motspur Shortlands Bromley Epsom and Towards Sevenoaks Towards Berrylands Park Morden Road South and Uckfield Streatham Anerley Petts Avenue Beckenham Wood Morden Common Birkbeck Beckenham Phipps Bridge Road Road Junction Mitcham Norbury Harrington Malden ZONE Orpington Hampton Thames Surbiton East elds Norwood Road Court Ditton Manor Belgrave Walk Junction Clock House Thornton Heath Chels eld Mitcham 4 South Merton Selhurst Elmers End Beddington Waddon Arena Knockholt Lane Marsh Tolworth Morden South Wandle Park Woodside Eden Park Mitcham Therapia Ampere Junction Reeves Blackhorse Lane Worcester St. Helier Lane Way Corner West Croydon West Wickham Park Addiscombe Towards Centrale Wellesley Road Hayes Sevenoaks Sutton Common Hackbridge Lebanon Chessington North George Street Road West Sutton Stoneleigh Carshalton Church East Sandilands Towards Street Croydon Woking and Waddon Lloyd Park ZONE South Croydon Guildford Chessington South Wallington Coombe Lane Sutton Carshalton Purley Oaks Beeches 5 Gravel Hill Purley Belmont Addington Village Towards Guildford Sanderstead Fieldway Ewell West Cheam Woodmansterne Coulsdon Town Reedham ZONE King Henry’s Drive Chipstead Renamed Riddlesdown Ewell East Banstead Coulsdon Town Kenley New Addington Kingswood on 12th April. 6 Whyteleafe Tadworth Coulsdon South Upper Warlingham Whyteleafe South Epsom Downs Tattenham Corner Caterham Towards Epsom and Towards Towards East Grinstead Dorking Gatwick Airport and Uckfield Southern London Midland London Midland First Capital Connect First Capital Connect First Capital Connect East Anglia Milton Keynes St. Albans Abbey St. Albans, Luton Airport, Welwyn Garden City, Hertford North , Hertford East, and Luton and Bedford , , and Stevenage , Bishops Stortford, How Wood Cambridge, Kings Lynn, Stansted Airport and Cambridge and M25 Peterborough Jn22 Chesham M25 Cuey Jn21 Jn21a Cheshunt Southern London Midland London Midland First Capital Connect First Capital Connect First Capital Connect National Express East Anglia Milton Keynes Milton Keynes St. Albans Abbey St. Albans, Luton Airport, Welwyn Garden City, Hertford North Broxbourne, Hertford East, and Northampton Luton and Bedford Stevenage, Letchworth, and Stevenage Harlow, Bishops Stortford, Bricket Wood Cambridge, Kings Lynn, How Wood A5 Stansted Airport and Cambridge Epping Huntingdon and M25 PeterboroughPotters Bar Jn22 Theobalds Chesham M25 Cuey Grove Jn21 Jn21a R D S H I R E Cheshunt O A1(M) Jn20 T F R A10 E A121 Bricket Wood H M25 M25 A5 M25 Epping Jn24 Crews Hill Waltham Kings Langley Jn23 Theobalds Cross GarstonM1 Jn25 Chiltern Railways Grove Jn26 Aylesbury I R E A41 R D S H Jn27 O A1(M) Jn20 T F Theydon Bois R A10 Hadley A1005 E A121 A121 M11 H Wrotham Wood M25 M25 Amersham Turkey Eneld Waltham M25 Chalfont Park Jn24 A111 Crews Hill Jn19 A1 Jn23 Street Lock A1010 Cross A121 E & Latimer Garston Radlett A1000 Jn25 Figure 2-2. Geographic map of London rail services A1081 Chiltern Railways A1055 Jn26 Aylesbury A404 S M25 A41 Watford Gordon Hill Lee Valley A112 Jn27 Grove A411 North Country Theydon Bois S Park Hadley A1005 Park A121 Wrotham Wood A104 (source TfL). Amersham Turkey Eneld E Chalfont Watford Park A111 Jn19 M1 A1 Country Street BrimsdownLock Junction A1010 A121 E & Latimer A1000 N F I E L Park E D Epping M25 X A1081 Eneld A1055 A404 A110 A110 Forest S Park Watford A41 EneldGordon Hill Town Lee Valley A112 M25 A411 A411 High Cockfosters National Express East Anglia Grove North Chase Country A113 Southend, , Colchester, Watford A5 S Park Aldenham Barnet Southbury Park Ipswich and Norwich Jn18 Park Elstree & Borehamwood Oakwood Debden M11 Watford A104 Loughton Trent Park A105 Chorleywood A404 Watford Ponders E High Street New A10 Brimsdown M1 A110 Country Grange End Junction Barnet Park E N F I E L X Park Eneld D A110 Epping M25 E Croxley CassioburyA412 A411 Bush Hill A111 A110 A110 Forest Park A41 A411 Eneld TownPark Sheneld A411 A411 High Cockfosters National Express East Anglia Chase A113 Southend, Chelmsford, Colchester, R Watford A5 Oakleigh Park Winchmore A1010 A1069 Aldenham Barnet Southbury Ipswich and Norwich I Jn18 Watford Bushey Park ElstreeElstree & Borehamwood Oakwood Hill Loughton Debden M11 A1112 A105 Chorleywood A404 Open Space Moat Mount Ponders High Street New A10 A1 Chingford H Totteridge & A110 Grange 1 Open Space Barnet End M A5109 Whetstone A109 Southgate Park A110 A1010 E A412 Bush Hill A1055 Jn17 Croxley A411 A105 S Rickmansworth A111 A1037 A413 A411 A111 ParkEdmonton Sheneld Moor A411 Buckhurst R 0 Oakleigh Park Green A1010 A112 A1069 Park 0 Winchmore Hill M I Carpenders 0 A1112 Bushey A4140 Elstree T 1 Hill A A409 E A404 Park Open Space A4125 Moat Mount N W A1009 A1 Palmers Silver ChingfordA1009 A H A41 Open Space R Totteridge & Arnos Grove 1 Green Angel Country Park Brentwood A410 M A A5109 WoodsideWhetstone Park A109 Southgate Street A1010 Roding Road A406 Chigwell A12 A1055 Jn17 A406 A105 A S Rickmansworth Open Space B A1037 Valley M25 H A413 Moor A1 A111 Edmonton F Buckhurst Weald Stanmore Mill Hill New Bounds Grange 0 A1003 Green A112 Country

0 Park Broadway 0 L Hill Chiltern Railways Edgware Southgate Green O M11

0 G M Carpenders West Finchley 1 Highams Hill Park A4140 1 , Aylesbury, Hatch T A A410 A White Hart A Banbury and Birmingham A409 E T Park Woodford A404 Park Bowes A1112 H 128 A4125 End Mill Hill East N W A1009 Epping Hainault Forest Jn28 A41 ParkPalmers SilverLane R A1009 N A R Arnos Grove Angel Forest Country Park Brentwood Bentley Priory Canons A410Park Woodside Park Green Street Northumberland H Roding M25 A Northwood A406 A12 I A404 A5 A406 Road Chigwell A Open Space B Park A E Valley Hainault M25 H A1 F Headstone A4140 A104 Weald Beaconseld A412 Stanmore Mill Hill New Bounds Wood Green A R K Lane W A1003 S Grange CountryA12

A404 Broadway 0 Burnt Oak M1 L V Chiltern Railways Northwood Edgware Southgate Green Bruce O A406 M11 G O West FinchleyFinchley 1 Highams Hill Fairlop Park High Wycombe, Aylesbury, Hatch A105 A Grove M E Hills A410 A Banbury and Birmingham R A41 Central White Hart T T Park Woodford C Mill Hill East AlexandraBowes A1080 A1112 H 128 EndA404 Lane R Epping A406 Jn28 A1 PalacePark Blackhorse A503 South E N R Turnpike Lane Forest D A12 Harrow & Canons Park Colindale A504 NorthumberlandRoad H Woodford M25 Northwood A564 U I A404 A5 A504 Tottenham E A A Harold Wood Seer Pinner A Wealdstone Queensbury Park Walthamstow Hainault 1 E Y Wood 1 A4140 G R Hale A104 Headstone N 1 Green I CentralA A412 Seven R B Beaconseld A4180 A504 R Wood Green Street A1400 Barkingside 2 Hornsey S A118 H W East Finchley A A1199 A12 B K A404 Lane Northwood Burnt Oak M1 Hendon H SistersBruce St. James Street A406 A12 A125 V O Finchley Harringay A123 Fairlop A409 A4006 Central A1 A103 A105 Grove WalthamstM ow E R I Hills A598 North Harrow R A41 Central Green T Snaresbrook C A4006 Alexandra A1080 Queens Road A404 A404 A502 Harringay Lanes South Blackhorse South A406 A12 Gerrards R Kingsbury A1 Palace Turnpike Lane A503 A12 E Gidea A127 Harrow & Colindale A504 Tottenham Road Woodford D I N Cross Kenton Hendon A564 A12 Newbury Park Park Northwick A U A504 Crouch Stamford Tottenham Leyton Harold Wood Seer Pinner A Wealdstone Queensbury Walthamstow 1 Romford Park G E YHill Hale Wood D 1 R Preston Brent Highgate Hill N Central Midland Road Redbridge 1 A118 A127 Green I Seven 0 B A4180 A504 R Street A1400 Gants Barkingside 2 Jn29 Rayners Hornsey 1 A118 H A4140 A1199 G Denham East Finchley A A1006 Wanstead Road A406 B HendonCross A503Sisters A Hill H A1201Harringay A107 St. James Street A12 A125 Emerson Lane A123 A123 Golf Club Harrow- A41 A104 Denham West A409 A4006 A103 Walthamstow G Central A1 A103 Manor House R I North Harrow GoldersA598 Green Stoke A118 Park Harrow on-the-Hill A406 Queens Road Snaresbrook Seven A40 A312 A404 A4006 Hampstead SouthNewington Leytonstone Wanstead Gerrards Eastcote A404 Kingsbury A502 Green Harringay Lanes Kings A12 Gidea UpminsterA127 West South A502 Heath Archway Finsbury Park Tottenham Park Chadwell A124 N c2c B Leytonstone I E Park Upminster Horndon , Cross A4090 Northwick KentonKenton Hendon A12 Newbury ParkGoodmayes Heath Ruislip R Crouch StamfordA105 Rectory Leyton Wembley ParkE A5 Tufnell ISL INGTO N High Road A124 Romford Bridge Southend and A4005Park N C AMDEN Upper Clapton Hackney D Shoeburyness Manor South Preston Brent A407 Highgate Hill Hill Road Midland Road WansteadRedbridge Ilford A1083 A118 A127 Ruislip 0 Gants North T Gospel Park Marsh A11 Jn29 West Ruislip 1 Denham Rayners A4140 Holloway A1083 G Road Cricklewood Arsenal A1006 WansteadFlats A406 Hornchurch A124 Harrow Cross Hampstead A Oak A503 A106 Hill Sudbury Hill Wembley A1201 A107 B A RKING Emerson Golf Club Lane A41 A104 Leyton Manor A123 A1112 Eastbrookend Denham West Harrow- Heath Holloway A112 Wanstead G Neasden A41 Hampstead A103 Manor House Stoke H A C K NEY A118 Elm Park Harrow Wembley Dollis Golders Park Seven Country Park M25 M South Harrow on-the-Hill A406 Park A4040 A312 A404 Sudbury & Hampstead Road Newington Leytonstone Wanstead A118 AND Northolt Park Stadium Hill Willesden Green Finchley Road Drayton Park Dalston Hackney Kings Park West A40 EastcoteRuislip South WestA502 Kentish Finsbury Park A123 A124 Upminster Ickenham Harrow Road & Frognal Heath Archway Park Woodgrange Park A124 Chadwell c2c B Green Hampstead Town Canonbury Kingsland Downs Homerton LeytonstoneForest Gate E DAGEN HAM Dagenham Upminster Horndon Basildon, Ruislip A4090 Sudbury Hill Kenton Stratford Goodmayes Heath Ruislip R Kentish A105 Rectory Wembley ParkE A5 CaledonianISL INGTO N High Road Bridge Southend and Wembley Kilburn Belsize Park Tufnell International A124 Dagenham East A124 Gardens A4005 N C AMDEN Town Upper Highbury & Islington Road Clapton Hackney Maryland Ilford Shoeburyness Jn16 Ruislip Manor South Central A407 Road Wanstead A1083 A4020 West Ruislip North T Gospel Park Marsh A11 Heathway Finchley Chalk Farm West CamdenHolloway Dalston Hackney Hackney A1083 Hornchurch A124 Harrow Sudbury Cricklewood Hampstead Arsenal Junction Flats Becontree A312 Wembley Stonebridge Park Road Oak Road A106 A118 Barking B A RKING Sudbury Hill Swiss Cottage Caledonian Essex Road Central Wick Leyton Manor A1112 Eastbrookend Brondesbury Heath 0 A112 Wanstead Town Neasden A41 Hampstead Holloway A106 Stratford East Ham 1 H A C K NEY Elm Park Hillingdon Harrow Wembley Dollis Brondesbury Road & London Park Upney Country M25 A355 South A M A4127 Park Camden A1200 Park AND 40 Sudbury & A404 A407 Kilburn RoadBarnsbury Victoria A118 NortholtNortholt Park Stadium Hill Willesden Finchley Road Town Drayton Park Dalston FieldsHackney Stratford Park A40 Ickenham Ruislip A40 HighW Roadest Kentish A123 Harrow Road & FrognalSouth HomertonPark High Street Woodgrange Park A124 Hornchurch Harlesden KensalGreen Hampstead Town Canonbury HaggerstonKingsland Downs Forest GateUpton Park DAGEN HAM Dagenham Ruislip Sudbury Hill Hampstead Kentish Mornington Stratford Alperton Queen’s Caledonian A1205 Country Uxbridge Wembley Rise Kilburn St. John’s Wood Crescent International East Willesden Belsize Park King’s Cross Maryland A124 Dagenham A125 Jn16 GardensN A406 Town RoadSt. AngelHighbury & Islington Park Greenford Central Park A5 Pudding Plaistow M25 A4020 Kilburn Cambridge A406 A437 Junction West Pancras Dalston Abbey Heathway Park Finchley Chalk Farm CamdenInternational Hackney Hackney Mill Lane A13 Dagenham A312 Sudbury Road JunctionHoxton Heath Bethnal West Road A118A112 A117 Becontree A1306 O A312 Perivale Stonebridge Park Road Maida Swiss Cottage Regent’s Caledonian Essex Road Central Wick Barking Dock Brondesbury 0 A124 Town Vale GreenA106 Stratford East Ham A4020 Hillingdon South Road & 1 LondonA1208 Mile Ham Upney A355 A A4127 ParkKensal Green CamdenEuston King’s Cross Bow Church A404 A407 A1200 VictoriaEnd D Northolt Kilburn GreatTown BarnsburySt. Pancras Bethnal Green Greenford North High Road Old Street Fields Stratford A40 WESTMSouthINSTER Baker Portland Park High Street Ockendon Hanger Lane Harlesden Kensal Street Shoreditch Bow Road Upton Park Hornchurch Acton WarwickHampstead Avenue Street HaggerstonHigh N Marylebone Mornington Euston Russell E W Uxbridge G Alperton A40 Rise Queen’s Square Farringdon Street A1205 H Rainham Country St. John’s Wood Crescent Square N Park Royal Willesden Edgware King’s Cross Stepney Green Devons Bromley- A A125 A1306 G A406 Park Road St. Angel Moorgate Plaistow M Park Greenford A5 Goodge Street Road Pudding M25 Wormwood Kilburn Warren Cambridge A406 A437 N Junction Royal Oak Regent’s Pancras Whitechapel by-Bow Star Lane Park Street Tottenham Liverpool Mill Lane Abbey A13 Dagenham I International A1020 Park Beckton A13 N A312 West Scrubs Hoxton Street Heath Bethnal Road A112 A117 A1306 A412 O A4020 L North ERegentdgware ’s Oxford Court Road Holborn Aldgate West Maida Barbican A312 Perivale Ealing Road Bond A13 TOW E R A124 Dock A4020 CastleA Bar Park Acton Westbourne Vale Circus East Green Langdon Park Ham St. Paul’s I South Ealing Park Bayswater ParkStreet Chancery Aldgate A1208 Mile E Broadway Kensal Green Euston King’s CrossLane Bank Bow Church A408 Paddington Bethnal Green End H AMLETS Ri D Greenford Ladbroke Great Leicester St.Cov Pancrasent City Old Street Fenchurch St. ve A13

7 Beckton r Burnham North Grove MarbleBa Akerrch Portland Thameslink Mansion Tower Limehouse Custom House Prince T Ockendon 2 Royal Gallions h Taplow L Drayton West Hanger Lane WESTM INSTER Square Garden Shoreditch Shadwell Poplar All Saints Royal Park am 1 Temple House Hill Bow Road es

Acton Latimer Warwick Avenue Lancaster Street Street Victoria for ExCeL Regent Reach 4 High N Acton A40 Russell E Albert Green Ealing White City Marylebone Euston Charing Westferry W G A Gate Square Farringdon Street Blackwall East H Rainham QueensEwdgayware Square Tower Cyprus A437 Park Royal Main Line Wood Lane Cross Blackfriars Stepney Green Devons Bromley- A A1306 G Notting Cannon Moorgate Gateway A1203 West India Quay India L Acton Road Piccadilly M Hyde Warren Goodge Street Monument Road N Wormwood RHilloyal Gate Oak Regent’s Embankment Street Whitechapel by-Bow Star Lane London A4 Green Park West Street CiTottenhamrcus Liverpool A13 City Airport I A1020 Ealing Central Park Canary Wharf Beckton N West ScrubsShepherd’s Bush Court Road Street A1020 A412 I A4020 L North Kensington Edgware Oxford Holborn Waterloo East A100Aldgate West A2016 Jn30 Barbican Crossrail Drayton A312 Hanwell Ealing A4020 Market Park Road Bond Westminster London Bridge Wapping A13 TOW E R Silvertown Slough CastleA Bar Park Common Acton East Acton Shepherd’s Westbourne Gardens Circus East Langdon Park Heron King Ealing Street Chancery St. Paul’s A200 A2016 A13 I Southall Park Quays A George V

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0 C HELSEA U Vauxhall A102 Wood and Town A209 and Bedwyn Langley A408 North elds Gloucester T Bermondsey Woolwich G 0 Brentford Chiswick A3220 eSloaner A2 Crossharbour Road Battersea Maze Hill Southend 3 A306 iv Fulham Broa dwEarayl’s R Square Elephant & Castle W Deptford H A312 A Chiswick HammersmithH Victoria Oval C Belvedere Windsor A4 A219 A M M Court Park H Surrey Quays Greenwich A206 Chaord Grays M25 A4127 Ravenscourt Battersea W Stamford Barons Mudchute Woolwich Jn31 & Eton A4 Court South T Queens Road Island Gardens A207 Woolwich I Slade Hundred A315 Park Greenwich Park Plumstead A220 Kensington Dockyard A2041 E S M A

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2 Queenstown 2 A206 Southeastern R Jn15 Parsons Green K ENSINGTON New Bridge Y

0 3 Erith i O Kew A316 New Park v M4 WI est South Bermondsey Charlton Pureet Ashford International and Kent e Hounslow Gunnersbury T Road Pimlico K 6 West W 5 r Kew Imperial Cross H AND Stockwell SO Peckham A2 A2 Datchet Barnes Denmark Cross 2 T Syon GarA4dens Kensington A Brompton s R Cutty Sark A207 4 A3044 e h M A408 East c2c L Bridge F N WharfA3220 m Rye for Maritime Greenwich Elverson Road H M Windsor & a A3044 M4 Bridge U C HELSEA ha LoughboroughA3 Hill A m Park Royal Gate E Heathrow L D T Vauxhall A102 N A209 Bexleyheath Tilbury and A3220 r Welling Eton Central e Isleworth Brentford Chiswick H A e Battersea A3 A2 St. Johns Maze Hill Southend s v Terminals Botanic A306 PutneyFulham Bridge Broa dway R i Junction W Deptford Lewisham Barnehurst Heathrow A312 S H M Clapham Junction Clapham Oval C Windsor A4 Gardens A219 A M M ParkWandsworth Brixton H Nunhead Greenwich E Grays M25 1, 2, 3 Hounslow W Barnes Battersea North Blackheath L R & Eton TA4erminal 5 HounslOsterleyow Mortlake Park Road T Queens Road Greenwich Park A207 I Slade West RichmondA315 Brockley A220 Key to lines

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S Brixton Kidbrooke A A2 and Chatham East 2 E Feltham Whitton Sheen Common W A20 A205 R A2 X A207 Jn1a Central DD Sunnymeads Twickenham Clapham A215 Catford 2 N I Jn14 Hatton U Dulwich Crofton 1 IA A315 Hounslow A3 South elds Common Clapham Common Catford Bridge I Hither Mottingham LondonGreenhithe Underground lines Cross East Putney A205 Park G Eltham WM D D RICH M O N D Richmond Balham A205 A21 S A20 E Bexley Dartford Circle A218 Green Ladywell Crayford M25 Stone N A Wraysbury O Park Earlseld L A MBETH NorthWest Dulwich A205 Swanscombe Heathrow A312 St. Margarets W A N DSWOR T H Herne H Crossing Northeet A E Tulse Dulwich Honor L Lee New Terminal 4 UPO N Clapham South Hill Forest B BaDistrictkerloo A244 A Southeastern A2 H A219 Streatham Hill Oak Park Eltham Albany A30 H A214 A23 Hill E Gravesend N Jn13 Strawberry Hill TootingW andsworthBec Hill Sidcup Park A2 E Ashford Whitton M A2 and Chatham D Feltham THAMES Twickenham Common Sydenham CatfordBellinghamW A205 Jn2 CentralHammersmith & City I Wimbledon Park A217 Hill Grove Park A A313 A3 South elds A24 West Catford Bridge I Mottingham M RICH M O NFulwellD Richmond Wimbledon BalhamA214 NorwoodA205 A21 S A20 Bexley CiJubileercle Wraysbury Park A218 Haydons Gipsy Sydenham M25 A307 Common Earlseld West Dulwich A205

A312 A312 A3 BeckenhamH Hill Staines Road Tooting Tulse Hill Forest Lower New UPO N A214 Crystal A DistrictMetropolitan A2 Teddington A219 Streatham Hill Eltham Albany Jn13 A30 A308 Wimbledon Broadway Streatham Hill Sydenham Egham A316 Strawberry Hill Palace Sidcup A2 Ashford A308 Hill Penge M Elmstead Park THAMES A219 Colliers Sydenham East Bellingham A20 Jn2 HammersmithNorthern & City M25 Woods Hampton Wimbledon Park WoodA217 Tooting Streatham A215 Hill Grove Park Kempton Park A313 Wick A24 Common West Sundridge A222

A311 Kent A30 Fulwell Kingston Wimbledon Dundonald Road A214 New

A238 Norwood Penge Park A225 JubileePiccadilly Common Haydons Gipsy SydenhamHouse Ravensbourne Hampton A307 Beckenham A312 A3 West Beckenham Hill Sunbury Raynes RoadSouth Wimbledon Norbury Hill Lower Staines A308 Norbiton Merton Tooting A214 Crystal Farningham Metropolitan Teddington Park Park Morden Road Beckenham Road Victoria A308 Wimbledon Broadway Streatham Palace Anerley Sydenham Bromley North Road Egham A316 A24 A308 A23 Penge Upper A3050 New Wimbledon MER TON Beckenham Elmstead A308 A219 Colliers East A20 Northern M25

A21 WChislehurstoods Waterloo & City Halliford Hampton A2043 Malden Chase Tooting Streatham A215 Avenue Clock A222 A298 Wood Phipps Bridge Mitcham Birkbeck Junction Shortlands A222 Southeastern Kempton Park Wick Common Road House Sundridge Chatham, , A30 A311 Eastelds Kent Kingston Dundonald Road New 25 Canterbury and Dover Hampton A238 Morden Belgrave Walk Penge House Park A225 PiccadilInterchangesly A240 South Merton Beckenham Ravensbourne St. Mary M South West Trains A244 Hampton Bushy ParkHampton Court Raynes Norbury West Bromley South Bickley Ascot, , Sunbury Berrylands Merton South Wimbledon Thornton Harrington Cray A308 Court A307 Norbiton Park Mitcham Swanley Farningham Victoria Reading and Weybridge M3 Park Morden Road Mitcham Heath Road Beckenham ElmersRoad End A3 Anerley Bromley North Jn3 Road A24 Mitcham Shepperton A23 Upper A3050 New MotspurWimbledon MER TON Junction Petts A20 A308 Morden South Common Beckenham National Rail lines

Norwood A21 Chislehurst Waterloo & City Virginia Halliford s A2043 Malden Park Chase Avenue Clock Junction Shortlands A222 Wood Southeastern e South West Trains A298 Phipps Bridge Mitcham Selhurst Birkbeck A214 Waters m Surbiton K I N G STON Beddington Lane Junction Arena Road House Chatham, Margate, (cased in black. All routes are shown M3 a , , Eastelds A23 h A217 Eden Park 25 Canterbury and Dover T Salisbury, Exeter, Hampton Morden Belgrave Walk M20 Interchanges A30 Thames A240 South Merton St. Mary M excluding limited services) South West Trains A244 r Southampton, Hampton UPON St. Helier Therapia Lane BickleyA21 e Bournemouth,Poole, Ditton Court Woodside Bromley South Ascot, Bracknell, iv Berrylands Thornton Harrington Cray Court A307 Mitcham West Swanley Reading and Weybridge R Weymouth, Guildford, RoadBlackhorse LaneElmers End West Havant, Portsmouth THAMES A3 Mitcham CroHeathydon Shepperton Mitcham Wickham Jn3 Chiltern Railways (for Isle of Wight), WMotspurorcester Junction Ampere Way Petts A20 Chertsey and Staines Malden Morden South Common Wellesley Addiscombe Y National Rail lines s Park Road Norwood Virginia M25 e Manor Park Selhurst A232 Wood South West Trains Surbiton A24 Beddington Lane Centrale East A214 c2c (cased in black. All routes are shown Waters m K I N G STON Waddon Marsh Junction Arena M20 M3 a Aldershot, Basingstoke, N A23 Hayes E h Esher A2043 A217 T O Hackbridge Reeves Croydon Sandilands Eden Park Orpington M20 A30 T Salisbury, Exeter, Thames Sutton Common S U T Corner excluding limited services) A21 L r Southampton, A3 UPOTolworthN St. Helier Therapia Lane A232Woodside First Capital Connect e Bournemouth,Poole, DittonHinchley Wandle Park Church Lebanon iv West George Road A232 R Weymouth, Guildford, Wood Street Street Blackhorse Lane West M Hersham Havant, Portsmouth THAMES A217 Carshalton A232 Croydon A232 ChilternFirst Gr Raileat wWaesternys (for Isle of Wight), Worcester West Ampere Way N Wickham O Chertsey and Staines Malden Addiscombe M3 Chertsey Park A2043 Wellesley Y Walton-on- A244 Sutton Waddon Road A212 Lloyd A2022 A232 A233 M25 A243 Manor O R Stoneleigh A24 Centrale East Park c2cHeathrow Connect Thames Waddon Marsh South E M20 Esher A240 A2043 A232 O N Reeves Croydon Sandilands D Hayes B A21 Orpington Eynsford Chessington North S U T T WallingtonHackbridge Corner A317 Sutton Common Croydon Y L A3 Tolworth Wandle Park Lebanon A232 FirstHeath Capitalrow ExpConnectress Hinchley Church 5 O

George3 Road Coombe Addington Village A232 Chelseld Street 2 R Gravel M A307 Wood A217 Street Sutton A Lane A232 Hersham Carshalton A232 C Hill First Great Western Carshalton A23 London Midland Chessington West A217 Beeches N Fieldway O Addlestone A2043 M3 Walton-on- A244 South Ewell CheamSutton Waddon A212 Lloyd A2022 Weybridge A243 Stoneleigh Park O King Henry’s Drive R A233 HeathNationalrow ConnectExpress East Anglia Thames West South A232 A21 Eynsford Chessington North A240 A2022D B A317 Wallington Croydon New Addington A232 Sanderstead Y M25 HeathSoutheasternrow Express EPSOM 5 O A21 Jn4 T ClaygateA3 3 Coombe Addington Village Chelseld A319 A2022 2 R Gravel A307 Belmont Purley Sutton A Lane Knockholt Ewell Carshalton C Hill A23 London Midland A217 Oaks Southern Byeet & Chessington A N D East Beeches Fieldway N New Haw Ewell M25 South Cheam King Henry’s Drive Weybridge EWELL West A237 E NationalSouth W Expestr Tessrains East Anglia M25 A240 Purley A2022 New Addington West A245 A3 Epsom A232 Sanderstead M25 Shoreham Oxshott A2022 A2022 Reedham A21 K T SoutheasternInterchanges Byeet A3 EPSOM Riddlesdown Jn4 A319 A307 BansteadBelmont A2022 Purley Knockholt Ewell Smitham (Coulsdon Town) Oaks Southern Byeet & A N D East A2022 N A244 A243 New Haw M25 A22 Crossrail EWELL A237 E South West Trains M25 A240 A217 Woodmansterne under construction South West Trains Epsom Purley West A245 A3 A245 Guildford Epsom Kenley Shoreham Oxshott A2022 Downs A2022 Reedham K Interchanges Byeet Riddlesdown S A307 Banstead Coulsdon Smitham (Coulsdon Town) Overground U A240 A2022 A244 R A243 Crossrailunder construction A245 A22 Upper 0 2 4 Miles R A217 Woodmansterne under construction M25 South West Trains Jn9 TattenhamEpsom Whyteleafe Warlingham Otford E A245 Guildford Corner Kenley Cobham & Ashtead A24 Downs Chipstead Y A3 M25 Woking S Stoke D’Abernon Coulsdon 0 3 6 Kilometres Docklands Light Railway M26 A240 South Southeastern London Overground U A23 Whyteleafe and South Ashford International under construction R A245 0 2 4 Miles Upper A233 Dunton R M25 Jn9 Jn9 Tattenham Whyteleafe Warlingham Green Otford Tramlink E Corner Cobham & A24 Tadworth Chipstead Y A3 M25 Woking Stoke D’Abernon Kingswood 0 3 6 Kilometres Jn5 Bat & Docklands Light Railway A22 M26 Ball Southeastern A23 Whyteleafe Maidstone and E ngham A217 South Ashford International

Caterham A233 M25 Dunton Juction South West TrainsLeatherhead Bookham Guildford Jn9 Green Tramlink M23

Tadworth A23 Woldingham London Connections Kingswood Bat & Jn5 September 2011 Jn8 Jn7 A22 Jn6 Ball M25 Southern and Southern A217 Southeastern E ngham South West Trains M25 A22 Sevenoaks Guildford, Havant, First Capital Connect Redhill, Gatwick Airport, M25 Southern , , Juction South West Trains Dorking and Gatwick Airport , Eastbourne Caterham East Grinstead M25 Ashford, Canterbury, Portsmouth and Isle of Wight Bookham Guildford and Brighton and Worthing and Uckeld Folkestone and Dover © Transport for London M23

A23 London Connections Jn8 Jn7 Jn6 September 2011 M25 Southern and Southern Southeastern South West Trains M25 A22 Sevenoaks Guildford, Havant, First Capital Connect Redhill, Gatwick Airport, M25 Southern Tonbridge, Hastings, Dorking and Gatwick Airport Brighton, Eastbourne East Grinstead Ashford, Canterbury, Portsmouth and Horsham Isle of Wight and Brighton and Worthing and Uckeld Folkestone and Dover © Transport for London Southern London Midland London Midland First Capital Connect First Capital Connect First Capital Connect National Express East Anglia Milton Keynes Milton Keynes St. Albans Abbey St. Albans, Luton Airport, Welwyn Garden City, Hertford North Broxbourne, Hertford East, and Northampton Luton and Bedford Stevenage, Letchworth, and Stevenage Harlow, Bishops Stortford, How Wood Cambridge, Kings Lynn, Stansted Airport and Cambridge Huntingdon and M25 Peterborough Jn22 Chesham M25 Jn21 Jn21a Cheshunt

Bricket Wood A5 Epping Kings Langley Potters Bar Theobalds M1 R D S H I R E Grove O A1(M) Jn20 T F R A10 E A121 M11 H M25 M25 M25 Jn24 Crews Hill Waltham Jn23 Cross Garston Radlett Jn25 Chiltern Railways Jn26 Aylesbury A41 Jn27 Theydon Bois Hadley A1005 A121 Wrotham Wood Turkey Enfield Park Amersham Chalfont A111 Jn19 A1 Street Lock A1010 A121 E & Latimer A1000 A1081 A404 A1055 S Watford Lee Valley A112 M25 Gordon Hill Grove A411 North Country S Park Park

A104 Watford Trent Park E M1 Country Brimsdown Junction Park E N F I E L X D Epping M25 Cassiobury Enfield A110 Forest Park A41 A110 Enfield Town A411 High Cockfosters National Express East Anglia Chase Southend, Chelmsford, Colchester, Watford A5 A113 Aldenham Barnet Southbury Ipswich and Norwich Jn18 Watford Park Elstree & Borehamwood Oakwood Loughton Debden M11 A105 Chorleywood A404 Ponders High Street New A10 Barnet A110 Grange End Park A110 E A412 Bush Hill Croxley A411 A111 A411 Park A411 R Oakleigh Park Winchmore A1010 A1069 I Bushey Elstree Hill A1112 Open Space Moat Mount Chingford H A1 Open Space Totteridge & 1 M A5109 Whetstone A109 Southgate A1010

A1055 Jn17 A105 S A1037 A413 Rickmansworth Moor A111 Edmonton Buckhurst Green Park A112 Hill M Carpenders A4140 T E A1000 A404 Park A409 A4125 N W A1009 Hainault Forest A41 Palmers Silver A1009 A R Arnos Grove Green Angel Country Park Brentwood Bentley Priory A410 A Woodside Park Street Roding 12 A406 Road A406 Chigwell A Open Space B A Valley M25 H A1 F Stanmore Mill Hill New Bounds Weald Grange Country Broadway A1003 Southgate L Chiltern Railways G Edgware West Finchley Green O Highams M11 Hill Park High Wycombe, Aylesbury, Hatch A10 Banbury and Birmingham A410 White Hart T Park Woodford A Mill Hill East Bowes A1112 H 128 End Lane R Epping Jn28 N Park Northumberland H Forest

M25 Canons Park I Northwood A5 A404 Park E Hainault A Headstone A4140 A A104 A412 Wood Green R K Lane W S A12 A404 Burnt Oak M1 V Northwood O Finchley Bruce A406 Fairlop A105 Grove M E Hills R A41 Central T C Alexandra A1080 A404 Blackhorse South A406 R A1 Palace Turnpike Lane A503 A12 E Harrow & Colindale D A564 A504 Road Woodford A504 A1112 U Seer Pinner A Wealdstone Queensbury Y Tottenham Walthamstow Wood Harold Wood Green I N G E Hale Central B R A4180 A504 R Seven Street A1400 Barkingside Hornsey A118 B H East Finchley A A1199 Hendon H Sisters St. James Street A125 Harringay A123 A12 A409 A4006 Central A1 A103 Walthamstow R I North Harrow A598 Green A4006 Queens Road Snaresbrook A404 Kingsbury A502 Harringay Lanes South A12 Gidea A127 Gerrards Tottenham I N Cross Northwick Kenton Hendon A12 Newbury Park Park Crouch Stamford Leyton Romford Park Hill D Preston Brent Highgate Hill Midland Road Redbridge Gants A118 A127 Denham Rayners A4140 G Jn29 A1006 Wanstead A406 Road Cross A10 A1201 A503 A107 Hill Emerson Golf Club Lane A123 Denham West Harrow- A41 A104 G Golders A103 Manor House Stoke Seven A118 Park Harrow on-the-Hill A406 A40 A312 A404 Leytonstone Wanstead Eastcote Green Hampstead Newington Kings West South A502 Heath Archway Finsbury Park Park Chadwell A124 Upminster c2c B Leytonstone E Upminster Horndon Basildon, A4090 Kenton Goodmayes Heath R A105 Ruislip Rectory Southend and Wembley ParkE A5 Tufnell ISLINGTON High Road A124 Bridge A4005 N CAMDEN Upper Road Clapton Hackney Ilford Shoeburyness Ruislip Manor South A407 Wanstead A1083 West Ruislip North T Gospel Park Marsh A11 Harrow Cricklewood Holloway Arsenal Flats A1083 Hornchurch A124 Hampstead Oak A106 BARKING 5 Sudbury Hill Wembley Leyton Manor A1112 Eastbrookend Heath A112 Wanstead 2 Neasden A41 Hampstead Holloway HACKNEY Elm Park South Harrow Wembley Dollis Park Country M M40 Sudbury & Road Park A118 AND Northolt Park Stadium Hill Willesden Finchley Road Drayton Park Dalston Hackney Park A40 West Kentish A123 Ickenham Ruislip Harrow Road & Frognal Woodgrange Park A124 Green Hampstead Town Canonbury Kingsland Downs Homerton Forest Gate DAGENHAMDagenham Ruislip Sudbury Hill Kentish Caledonian Stratford Wembley Kilburn Belsize Park International Maryland A124 Dagenham East Jn16 Gardens Town Road Highbury & Islington A4020 Central Heathway Finchley Chalk Farm West Camden Dalston Hackney Hackney Sudbury Junction A312 Stonebridge Park Road Road A118 Barking Becontree Town Brondesbury Swiss Cottage Caledonian Essex Road Central Wick Stratford East Ham Hillingdon Brondesbury Road & London A106 Upney A355 A10 A4127 Park Camden A404 A407 A1200 Victoria Northolt Kilburn Town Barnsbury Fields A40 High Road Stratford South Park High Street Hornchurch Harlesden Kensal Haggerston Upton Park Hampstead Mornington Uxbridge Alperton Rise Queen’s A1205 Country N Willesden St. John’s Wood Crescent King’s Cross A125 A406 St. Angel Park Park A5 Plaistow M25 Greenford Kilburn Cambridge Pudding A406 A437 Junction Pancras Abbey Park International Mill Lane A13 Dagenham A312 Hoxton Heath Bethnal West Road A112 A117 A1306 O Perivale Maida Regent’s Dock Vale Green A124 A4020 South Park A1208 Mile Ham Kensal Green Euston King’s Cross End Bow Church D Greenford Great St. Pancras Old Street Bethnal Green North Portland Ockendon Hanger Lane WESTMINSTER Baker Shoreditch Warwick Avenue Street Street Bow Road N Acton High E Marylebone Euston Russell W G Farringdon Street H Rainham Park Royal A40 Edgware Square Square Bromley- A A1306 Stepney Green Devons G Road Moorgate M Goodge Street Road N Wormwood Regent’s Warren Whitechapel Star Lane Royal Oak Street Tottenham Liverpool by-Bow A13 I A1020 N West Scrubs Park Street Beckton A412 L North Edgware Court Road Holborn Aldgate A4020 Oxford A312 Barbican A13 TOWER CastleA Bar Park Ealing Acton Westbourne Road Bond Circus East Langdon Park East Acton St. Paul’s I Ealing Park Bayswater Street Chancery Aldgate E Broadway Lane Bank HAMLETS R A408 Paddington City Canning Town iv Ladbroke Leicester Covent Fenchurch St. er A13 Burnham Grove Marble Arch Thameslink Mansion Tower Limehouse Beckton T Taplow West Square Garden Shadwell Royal Custom House Prince Park Gallions ha L Drayton Temple House Hill Poplar All Saints for ExCeL Regent Royal mes Acton Latimer Lancaster Victoria Albert Reach Green Ealing A40 White City Charing Westferry A4127 Road Queensway Gate Tower Blackwall East Cyprus A437 Main Line Wood Lane Cross Blackfriars Notting Cannon Gateway A1203 West India Quay India L Acton Hyde Piccadilly Street Monument A4 Hill Gate Green Park Embankment London West Circus City Airport Ealing Central Shepherd’s Bush Holland Park Canary Wharf A1020 Kensington Waterloo East A100 West A2016 Jn30 Crossrail IDrayton Market Park London Bridge Hanwell Common A4020 Westminster Wapping Heron Silvertown King Maidenhead Slough Shepherd’s Gardens A200 A2016 A13 Southall Quays A1206 Bush A315 A1206 Pontoon George V South Acton Southwark Hayes & Goldhawk High Street Knightsbridge Hyde Park Rotherhithe Dock First Great Western H South Road Kensington Kensington Corner Waterloo South North Thames S Harlington Quay (Olympia) Borough Reading, Oxford, L Iver Ealing Acton Turnham St. James’s Lambeth North Greenwich Flood Abbey O Canada Water A102 Banbury, Worcester, M4 U Green A4 Park Barrier Wood Langley A408 Town Gloucester Bermondsey Woolwich and Bedwyn G Northfields Sloane Crossharbour Road Earl’s Square H A3005 Chiswick Hammersmith Victoria Elephant & Castle Belvedere Court A206 Chafford A4127 Ravenscourt Surrey Quays Boston Manor Park Stamford Barons Mudchute Woolwich Jn31 Park Court South Island Gardens Woolwich Plumstead Hundred M4 Brook Kensington Westcombe Dockyard A2041 A215 Arsenal Jn15 KENSINGTON Kennington A200 Park A206 Erith M4 West South Bermondsey Charlton Kew Gunnersbury West AND Pimlico 4 A4 Kensington Brompton es Cutty Sark M A408 c2c Bridge A3220 m H M25 A3044 M4 CHELSEA ha A3 for Maritime Greenwich r T Vauxhall A102 A209 Tilbury and Brentford Chiswick A3220 ve Battersea A2 Maze Hill Southend A306 Fulham Broadway Ri Deptford A312 H A M M E S M I T H Park Oval C Windsor A4 A219 Greenwich Grays M25 W Battersea & Eton A4 Osterley Queens Road Greenwich Park A207 I Slade A315 Park A220

A206 Riverside A4 Syon Lane Queenstown A23 A202 Peckham Deptford A205 Green R Parsons Green New Bridge Y Southeastern i O New v Kew A316 Ashford International and Kent e Hounslow Road W r Cross Datchet Barnes Imperial Stockwell DenmarkSOUTHWARKPeckham A2 A2 T Syon Gardens A N D Cross A207 h A3044 L East F U L H A M Rye Elverson Road Windsor & a Park Royal Bridge Wharf Loughborough Hill m Heathrow Gate N Welling Bexleyheath E Eton Central e Isleworth A3 St. Johns s Botanic Junction Lewisham Barnehurst Terminals S Putney Bridge Clapham Heathrow Hounslow Gardens Clapham Junction Wandsworth Brixton Nunhead E L R Terminal 5 1, 2, 3 Hounslow Barnes Road North Blackheath West Richmond Mortlake Brockley E Key to lines O N Central North A205 Putney Wandsworth Town Clapham High Street Falconwood S A30 Brixton Kidbrooke A221 Sheen East A20 R A2 X Jn1a D Sunnymeads Clapham A215 A207 Jn14 Hatton U Dulwich Crofton IN A315 Hounslow Common Clapham Common Hither LondonGreenhithe Underground lines Cross East Putney A205 Park G Eltham E Dartford W D D Ladywell Green N LAMBETH North Crayford Stone Swanscombe A Heathrow O St. Margarets WANDSWORTH Herne Crossing Northfleet A E Dulwich Honor L Lee Terminal 4 Clapham South Hill B Bakerloo A244 Southeastern H Oak Park E N H A214 Wandsworth A23 Gravesend E Whitton A2 and Chatham I D Feltham Twickenham Common Catford W A205 Central A A3 Catford Bridge I Mottingham M RICHMOND Richmond Balham A205 A21 S A20 Bexley Circle Wraysbury Park A218 M25 West Dulwich A205 H A312 Tulse Forest New UPON A District A219 Streatham Hill Eltham Albany A2 Jn13 A30 Strawberry Hill Hill Sidcup Ashford Tooting Bec Hill M Park A2 THAMES Sydenham Bellingham Jn2 Hammersmith & City Wimbledon Park A217 Hill Grove Park A313 A24 West Fulwell Wimbledon A214 Norwood Haydons Gipsy Sydenham Jubilee A307 Common

A3 Beckenham Hill Hill Lower Staines Tooting A214 Crystal Teddington Sydenham Metropolitan A308 Wimbledon Broadway Streatham Palace Egham A316 A308 Penge Elmstead A219 Colliers A20 M25 East Woods Northern Hampton Wood Tooting Streatham A215 Kempton Park Wick Common Sundridge A222

A311 Kent A30 Kingston Dundonald Road New A238 Penge Park A225 Piccadilly House Beckenham Ravensbourne Hampton Bushy Park Raynes Norbury West Sunbury Norbiton Merton South Wimbledon A308 Park Morden Road Beckenham Road Farningham Victoria M3 Park Bromley North Road A24 Anerley Upper A3050 New Wimbledon MERTON A23 Beckenham

A308 A21 Chislehurst Halliford Malden Chase Avenue Clock A222 Waterloo & City A2043 Phipps Bridge Mitcham Birkbeck Junction Shortlands Southeastern A298 Road House Chatham, Margate, Eastfields Canterbury and Dover Hampton South Merton Morden Belgrave Walk 25 Interchanges A240 St. Mary M South West Trains A244 Hampton Court Bromley South Bickley Ascot, Bracknell, Berrylands Thornton Harrington Cray Court A307 Mitcham Swanley Reading and Weybridge Road A3 Mitcham Heath Elmers End Shepperton Junction Mitcham Jn3 Motspur Morden South Common Petts A20 National Rail lines s Norwood Virginia e Park Selhurst Wood m South West Trains Surbiton Beddington Lane A214 Waters a KINGSTON Junction Arena (cased in black. All routes are shown M3 Aldershot, Basingstoke, A23 h Salisbury, Exeter, A217 Eden Park M20 A30 Camden Town T Thames excluding limited services) r London Southampton, UPON St. Helier Therapia Lane A21 Essex Road e Bournemouth,Poole, Ditton Woodside iv Fields West R Weymouth, Guildford, Blackhorse Lane West South Central London Havant, Portsmouth THAMES Croydon (for Isle of Wight), Worcester Wickham Chiltern Railways Kilburn Hampstead A1200 Ampere Way

A41 Malden A5205 Chertsey Chertsey and Staines Wellesley Addiscombe Y Park Road High Mornington M25 Connections Manor A232 A24 Centrale East c2c Road Crescent N Waddon Marsh Hayes E M20 Esher A2043 T O Hackbridge Reeves Croydon Sandilands Orpington St. John’s Sutton Common S U T Corner St. A3 Tolworth L First Capital Connect Wood King’s Cross Hinchley Wandle Park Church Lebanon A232 Pancras Angel A10 George Road A232 A5 Street M Cambridge Wood A217 Street A232 A4210 International Hersham Carshalton A232 First Great Western Hoxton A1208 Heath West N O Maida Regent’s Addlestone A2043 M3 Walton-on- A244 Sutton Waddon A212 Lloyd A2022 A243 Stoneleigh Park O R A233 Heathrow Connect Vale Park A501 Thames South Bethnal A232 D A21 Eynsford Chessington North A240 Wallington B A317 Green Croydon Y Euston King’s Cross O Heathrow Express A41 Bethnal Claygate Coombe St. Pancras Old Street R Gravel Addington Village A307 Sutton A235 Lane A5205 Green Carshalton C Hill A1 A217 A23 London Midland Great Chessington Beeches Fieldway Baker Portland South Ewell Cheam WeybridgeShoreditch King Henry’s Drive Street Street A4200 A5201 West National Express East Anglia A404 Warwick Russell High Street Marylebone Euston A2022 New Addington Square A232 Avenue Square Farringdon Sanderstead M25 EPSOM A21 Jn4 T Southeastern A40 Edgware Whitechapel A3 A319 Warren Moorgate Belmont A2022 Purley Knockholt Road Goodge Street A5201 Ewell Oaks Regent’s Street Byfleet & Liverpool AND East N Southern A4210 Park Chancery New Haw M25 Royal Oak Edgware Tottenham Street A11 EWELL A237 E Lane M25 South West Trains Road Court Road Barbican Aldgate A240 East Purley Bond West A245 A3 Epsom Shoreham Oxshott A2022 A2022 Reedham K Interchanges Street Oxford Holborn A40 Byfleet St. Paul’s Riddlesdown Bayswater A5 Aldgate A307 Banstead Circus Smitham (Coulsdon Town) A4206 Bank Fenchurch A2022 Paddington City A244 Marble Arch Leicester Thameslink Street A243 Crossrail A4 Shadwell A22 Square A217 Mansion Tower Woodmansterne under construction Lancaster Temple House South West Trains Epsom Hill A245 Guildford Kenley A40 Gate Charing Ashtead Downs Queensway A4202 Cross S Tower A1203 Coulsdon Blackfriars South Notting U Cannon Gateway A240 London Overground Piccadilly Street Monument Hill Gate Circus Embankment R under construction A3 0 2 4 Miles Kensington Hyde Park Green Park A245 Upper WaterlooR Gardens M25 Jn9 Tattenham Whyteleafe Warlingham Otford East E Corner London Bridge WappingCobham & A24 Chipstead A3 M25 Y A100 WokingWestminster Stoke D’Abernon 0 3 6 Kilometres Docklands Light Railway M26 Southeastern A23 Whyteleafe A315 Hyde Park Southwark A200 South Maidstone and High Street Knightsbridge Corner Waterloo Ashford International Rotherhithe A233 Dunton Kensington A302 Leatherhead Green Tramlink St. James’s Lambeth North Borough Jn9 A201 Canada Water Tadworth Woldingham Park Kingswood Bat & A4 Jn5

A302 A22 A2 Ball South A3216 Bermondsey Gloucester A3212 Earl’s Kensington

A3036 A201 Road Effingham A217 Court Caterham M25 A3217 Victoria Juction South West Trains Elephant & Castle Bookham Guildford M23

A23 London Connections A202 Jn8 Jn7 Jn6 September 2011 Sloane M25 Square Southern and Southern Southeastern A2 Sevenoaks National Rail South South West Trains M25 First Capital Connect Redhill, Gatwick Airport, M25 A22 Southern Tonbridge, Hastings, Guildford, Havant, Kennington Dorking and Portsmouth and Bermondsey Gatwick Airport Brighton, Eastbourne East Grinstead Ashford, Canterbury, main line connections Horsham and Worthing and Uckfield Folkestone and Dover Isle of Wight Pimlico and Brighton © Transport for London

Figure 2-3. Underground, National Rail, and DLR services in Central London (see Figure 2-2 for legend; source: TfL).

Oyster cards can store prepaid weekly, monthly, or annual Travelcards valid for unlimited use on various modes within specified travel zones, and can also store monetary credit, which can alternatively be used to pay for each trip individually (referred to as pay as you go). All Oyster users are required to tap their cards when boarding buses or when entering or ex- iting station gates, but some stations are ungated. holders are not required to tap at ungated stations,3 but Oyster readers are provided for pay-as-you-go users, who are required to tap their cards in order to deduct the correct fare. Oyster data is retained by TfL for eight weeks before being archived4 and the complete population of Oyster data for one week of every month

3 Travel beyond the zones (or time periods) covered by a Travelcard requires the customer to pay an additional fare (the difference in fare between travel covered by the card and the trip taken by the customer) using pay as you go. 4 For privacy, identifying information is removed prior to archival.

28 was made available for this research. Card identifiers are encrypted for privacy, and data are exported from a TfL database in plain-text format. Oyster entry and exit data are available for Underground, Over- ground, and DLR transactions at gated stations (and at ungated stations for pay-as-you-go transactions), while boarding (but not alighting) in- formation is available for buses and .5 The Oyster card was recently made available for National Rail travel, and its use on the mode has been steadily increasing (Muhs 2012).

2.2.2 iBus

iBus is TfL’s AVL system, and is installed on the entire fleet of buses. While several data sets are recorded by the system, this research uses a set con- taining a record for each stop event: an instance of a vehicle serving (or passing) a bus stop (TfL 2006, Hardy 2007, Robinson 2010, Continental Automotive 2011, Hounsell et al 2012). iBus tracks each vehicle’s location using a combination of GIS, tachom- eter, speedometer and gyroscope data. The system attempts to record in- formation about the time at which each bus neared a stop, opened its doors, closed its doors, and pulled away from the stop. If all four data are recorded internally, it reports the door opening time as the stop arrival time and the door closing time as the stop departure time. If either door event is unavailable, the approaching or departing timestamp is used. If only one of the four events are recorded, the same time is written to both the arrival and departure fields. iBus typically records approximately 5 million transactions daily. Data are provided to MIT researchers through a secure database report- ing interface, so that data can be obtained for the same periods for which Oyster data are collected.

2.2.3 Gateline and ETM

Aggregate counts of passenger activity are provided by rail station gates, or gatelines, and at bus fareboxes, or electronic ticket machines (ETMs). Rail

5 Modes requiring both entry and exit taps store a record for each. Entry records contain entry information (as expected), while exit records store both entry and exit information in order to assess the correct fare.

29 counts are aggregated by date, hour, station, direction (entry versus exit), and ticket type. ETM data are similarly aggregated by date, hour, and ticket type, plus the bus’s direction (inbound versus outbound) and its route. ETM data were available for all bus routes, but gateline data for several gated National Rail and Overground stations were unavailable (although these data are recorded and may be available for future analyses).

2.2.4 Spatial Data

In addition to Oyster, iBus, gateline, and ETM data, some of the methods presented in this thesis require spatial information. A list of bus stops was acquired from TfL, which includes spatial coordinates. A similar table of rail stations was also acquired, although it lacked spatial information. Using each station’s unique identifier, the data were joined to a similarly identified GIS layer to obtain spatial coordinates.

30 Bus Origin and Destination Inference 3

In order to observe entire passenger journeys using fare-transaction data, time and location information must be obtained or inferred about the start and end of each journey stage.1 The zonal fare-pricing structure of most rail modes in Greater London necessitates fare transactions at both the start and end of each stage, yielding time and location data for both the entry and exit stations.2 But London Buses, like many other urban bus systems, charges flat rather than zonal fares, enabling fare transactions to be completed at the time of boarding and therefore requiring no alighting transaction. Oyster bus records contain information about the time of the transac- tion and about the vehicle, route, and vehicle-trip number, but informa- tion about the alighting location and time must be inferred. Furthermore, the onboard AFC equipment does not obtain location data from the bus’s

1 The concept of a journey stage is defined in Chapter 4, where it is relevant to the inference of interchanges. In the context of bus travel, however, a bus journey stage can be con- sidered one cardholder’s ride on a single vehicle, regardless of whether that person made another bus (or rail) trip immediately before or after it. 2 As discussed in Chapter 2, all London Underground, most London Overground, and many National Rail stations are gated, requiring transactions for both entries and exits. Ungated stations, including the remaining Overground and National Rail stations, and most DLR platforms, require Oyster users to tap their cards only if they are paying a fare using stored credit. Customers with travel passes valid for the journey stage are not required to tap their cards at either end.

31 AVL system.3 The boarding location must therefore also be inferred, by merging the two data sets offline. This chapter describes the process by which boarding and alighting locations and alighting times are automatically inferred for the popula- tion of Oyster bus transactions for a given day. The resultant data are a required input to the interchange-inference process, which in turn is an input to the full-journey matrix estimation process (both of which are de- scribed in subsequent chapters). In addition to their applicability to full- journey analysis, OD-inferred bus data are useful for a number of bus-only other applications, which are presented in Chapter 7.

3.1 Previous Research

3.1.1 Inferring Bus Origins Using AFC and AVL Data

On bus systems for which AFC and AVL data are available, passenger board- ing locations are typically inferred by matching each AFC record to an AVL record, then using the AVL location as the passenger’s boarding location. In most cases the records can be matched by first selecting the subset of AVL records that share the same vehicle, route, or vehicle trip as the AFC record, and then by selecting from that subset an AVL record that has a timestamp closely matching that of the fare transaction. Zhao et al (2007) propose this approach in their study of the Chicago Transit Authority’s bus and rail system, and Cui (2006) applies the meth- odology to the city’s bus network. Chicago’s AFC data uniquely identify specific vehicles, and each AVL record indicates the time at which a bus opened its doors. By including only those AFC transactions that occur within five minutes after an AVL event, Cui infers the origins of approxi- mately 90 percent of all bus boardings. Wang et al (2011) apply a similar approach to five TfL bus routes, matching AFC transactions to the (temporally) closest AVL record, regard-

3 TfL’s new fare-payment system, currently in development, will record location data with each fare-transaction record.

32 less of whether it occurs before or after the fare transaction.4 Following Cui, Wang limits matching to within a five minute range (in this case either before or after the time of the AFC transaction), yielding a similar origin-inference rate of 90 percent. Munizaga et al (2011) infer origin loca- tions for 98.5 to 99.9 percent of all bus boardings on Chile’s Transantiago network, but it is unclear whether a time limit is being applied.

3.1.2 Inferring Bus Destinations Using AFC and AVL Data

The absence of AFC alighting transactions is common to many bus systems. Lacking this information, previous studies have inferred alighting loca- tions under the assumption that the most likely place for a passenger to alight a bus is the stop closest to her next bus boarding (or station entry) location. Barry et al (2002) apply this assumption in their study of New York’s subway system, and later on the city’s combined bus and subway network (2009), both of which record fares only upon entry. By mak- ing the additional assumption that a rider’s initial origin of the day is a close approximation of her final daily destination, they are able to infer destinations for passenger journey stages regardless of a stage’s sequence within a rider’s daily travel history. Barry et al (2002) tested these assumptions against a travel-diary sur- vey, showing that the assumptions were valid in 90 percent of the cases observed. Additionally, Navick and Furth (2002) applied the same as- sumptions in their study of five Los Angeles bus routes, validating the assumptions against ride-check data which showed that the assumptions were valid on four of the five routes studied.5 The two aforementioned destination-inference assumptions have since been applied in several other studies of transit networks with entry- only AFC data. Zhao et al’s (2007) work on the CTA rail system inferred destinations for 71 percent of AFC passenger trips, inferring destinations only when the candidate alighting location is within a 400-meter Euclid-

4 Multiple event types can trigger the recording of an AVL event in TfL’s system, which can sometimes lead to ambiguity about whether a bus was arriving at or departing from a stop (see section 2.2.2 for details). 5 Navick and Furth infer the validity of these assumptions by testing the symmetry of each route’s boarding profile—the pattern of daily boardings in one direction should match the pattern of daily alightings in the other. They hypothesize that the lack of symmetry on the one route that failed their test was due to its sharing of a corridor with other routes.

33 ean distance of the rider’s next origin. Cui (2006) applied a 1,110-meter rectilinear distance limit to the CTA’s bus network, achieving a destina- tion-inference rate of 67 percent. Trépanier et al (2007) applied similar logic to the bus network of Gatineau, Québec, using a two-kilometer Euclidean distance limit and inferring destinations for 66 percent of the observed transactions. Wang et al (2011) apply the closest-stop and last-of-day assumptions in their London study, in which AVL data are used to infer passenger alight- ing times after the inference of alighting locations. As in the origin-infer- ence process, AVL and AFC data are matched by route and vehicle trip, but the AVL record is chosen from the resultant subset by matching its bus stop code to that identified using the two destination-inference assumptions. Studying five routes and imposing a one-kilometer distance limit around each route, Wang et al infer destinations for 57 percent of the observed AFC data. Finally, the inference process is validated against TfL’s Bus Ori- gin–Destination Survey (BODS), which exhibits a similar distribution of alighting locations for the observed routes. While most previous studies inferred destinations by using SQL queries that required manually generated lookup tables or spatial buffers, Muni- zaga et al (2011) automate the process by integrating a database with cus- tom software.6 To account for bus-route circuity and for the absence of trip direction information in Transantiago’s AVL and AFC data, the closest- stop rule was modified to minimize a generalized time value, which ac- counts for both the distance between stops as well as the time savings real- ized by alighting at an earlier stop and walking. With roughly six million fare transactions per weekday on the combined bus and metro network, the process took several hours to complete but inferred approximately 83 percent of bus destinations.

3.2 Origin Inference

The origin-inference process builds upon previous research by incorpo- rating and refining some of the aforementioned assumptions while taking advantage of additional features in the iBus data set. Performance is ad-

6 Barry et al (2009) automate an OD-inference process which does not use AVL data.

34 dressed by implementing the process entirely in an object-oriented pro- gramming language and tuning the code for speed and memory efficiency. To enhance compatibility with databases and analysis tools, data are input and output in an easily parsable comma-separated text format. Although the three inference processes are conceptually performed in series, they are combined in a single application which performs some parts of each in parallel for efficiency. For clarity, however, the origin- inference algorithm is treated in this section as a discrete process.

3.2.1 Exploratory Analysis of Input Data

Following Zhao et al (2007), Cui (2006), Wang et al (2011), and Munizaga et al (2011), this methodology infers bus origins by matching AFC and AVL data. While Oyster and iBus are the only data sets used in the origin- inference process, additional data from TfL’s Bus Contract Management System (BCMS) are used in this section to empirically demonstrate the rela- tionship between the AFC and AVL data. Wang (2010) notes that Oyster timestamps are truncated to the minute, and that their seconds component can be treated as a random variable uniformly distributed between 0 and 59, inclusive. By adding 30 seconds to each Oyster timestamp, an approximation of the expected time value can be obtained. The BCMS system records fare-transaction data in tandem with the Oyster system, but contains only a subset of Oyster’s fields. The BCMS data do, however, retain the seconds component of the transaction time, and can be used to illustrate the error introduced by the truncation of Oyster timestamps.7 Figure 3-1 shows the effect of this truncation, as all fare transactions are effectively shifted to the midpoint of the minute in which they occur.8 The figure also illustrates the times recorded by the iBus system, which usually include both arrival and departure timestamps (Wang et al had access only to departure times). In most cases BCMS shows fares be- ing paid between an iBus event’s arrival and departure times, and these

7 Although the matching of BCMS and Oyster data would improve the resolution of Oys- ter timestamps, the inefficiencies of obtaining and processing complete daily sets of BCMS data (in addition to the large Oyster and iBus data sets) do not justify this relatively minor improvement. 8 Data displayed for a single bus serving a portion of route 38 eastbound, on Saturday, 26 February 2011

35

Bus arrival Hyde RED LION SQGRUAARYE 'S INN ROROSEAD MBOERUNTY A VPLEENTYAUSS OEAENT S TREEHT ARSDAWDILERCK 'SST WREEELLT S THEATREST JO HN STREETA /N GGOESLW STATIONELL ROADISLINGTON <> ISLI GRNGTEPENAOC NKC IGRNROSGETENOS NSTRE STREET ET ESSEX ROANORTD STATIONHCHURCO C# KHEN RODONMIAD LD ROADMAY PARK / SOU1T6H GATE ROAD 16 16 16 16 16 16 16 16 16 16 16 16 16 Grosvenor Wilton Park Old / Green Park Bus departure Old Bond Street/ Piiccccaadilillly Gerrand Place/ Tottenham Court Gardens Street Corner Hard Rock Café Station Royal Academy Ciirrcucuss Trocadero/Haymarket Chinatown Denmark Street Road Station BCMS Transaction Bus arrival Hyde Oyster transaction RED LION SQGRUAARYE 'S INN ROROSEAD MBOERUNTY A VPLEENTYAUSS OEAENT S TREEHT ARSDAWDILERCK 'SST WREEELLT S THEATREST JO HN STREETA /N GGOESLW STATIONELL ROADISLINGTON <> ISLI GRNGTEPENAOC NKC IGRNROSGETENOS NSTRE STREET ET ESSEX ROANORTD STATIONHCHURCO C# KHEN RODONMIAD LD ROADMAY PARK / SOU1T6H GATE ROAD 16 16 16 16 16 16 16 16 16 16 16 16 16 Grosvenor Wilton Park Old Park Lane/ Green Park Bus departure +  seconds Old Bond Street/ Piiccccaadilillly Gerrand Place/ Tottenham Court Victoria Bus Station Gardens Street Corner Hard Rock Café Station Royal Academy Ciirrcucuss Trocadero/Haymarket Chinatown Denmark Street Road Station BCMS Transaction Oyster transaction +  seconds

: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 19:30 19:31 19:32 19:33 19:34 19:35 19:36 19:37 19:38 19:39 19:40 19:41 19:42 19:43 19:44 19:45 19:46 19:47 19:48 19:49 19:50 19:51 19:52 19:53 19:54 19:55 19:56 19:57 19:58 19:59 20:00 20:01 20:02 20:03 20:04 20:05 20:06 20:07 20:08 20:09

Bus arrival Hyde RED LION SQGRUAARYE 'S INN ROROSEAD MBOERUNTY A VPLEENTYAUSS OEAENT S TREEHT ARSDAWDILERCK 'SST WREEELLT S THEATREST JO HN STREETA /N GGOESLW STATIONELL ROADISLINGTON <> ISLI GRNGTEPENAOC NKC IGRNROSGETENOS NSTRE STREET ET ESSEX ROANORTD STATIONHCHURCO C# KHEN RODONMIAD LD ROADMAY PARK / SOU1T6H GATE ROAD 16 16 16 16 16 16 16 16 16 16 16 16 16 Grosvenor Wilton Park Old Park Lane/ Green Park Old Bond Street/ Piiccccaadilillly Gerrand Place/ Tottenham Court Bus: departure: : : : : : : : : : : : : : : : : : : : : : : : : : : : : Victoria Bus Station Gardens Street Corner Hard Rock Café Station Royal Academy Ciirrcucuss Trocadero/Haymarket Chinatown Denmark Street Road Station 19:30 19:31 19:32 19:33 19:34 19:35 19:36 19:37 19:38 19:39 19:40 19:41 19:42 19:43 19:44 19:45 19:46 19:47 19:48 19:49 19:50 19:51 19:52 19:53 19:54 19:55 19:56 19:57 19:58 19:59 20:00 20:01 20:02 20:03 20:04 20:05 20:06 20:07 20:08 20:09 BCMS Transaction Oyster transaction +  seconds

: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 19:30 19:31 19:32 19:33 19:34 19:35 19:36 19:37 19:38 19:39 19:40 19:41 19:42 19:43 19:44 19:45 19:46 19:47 19:48 19:49 19:50 19:51 19:52 19:53 19:54 19:55 19:56 19:57 19:58 19:59 20:00 20:01 20:02 20:03 20:04 20:05 20:06 20:07 20:08 20:09 Figure 3-1. Oyster, BCMS, and iBus data for a portion of a single bus (route 38 eastbound).

transactions are often concentrated near the arrival. This coincides with the expected behavior of customers waiting at a bus stop and then pay- ing their fares in succession after the door opens. Some of these clusters of transactions begin a second or two before the arrival event (e.g., Tro- cadero, Gerard Place), possibly due to a difference between the Oyster reader’s internal clock and that of the iBus system, which is synchronized to GPS satellites. These small differences are unlikely to have any effect on the origin-inference process, but two of the stops shown have a more significant offset. Two BCMS transactions occur approximately 40 seconds before the iBus record at the Piccadilly Circus stop, which contains the same time for the bus’s arrival and departure. As noted in section 2.2.2, iBus will use the same time as a stop’s arrival and departure if one or the other are missing or if the bus passed the stop without opening its doors (Robinson 2007). It is therefore unclear whether this iBus record marks the time at which the bus drew near to the stop, opened or closed its doors, or departed. Since separate arrival and departure times were recorded for the previous stop, it is perhaps most likely that the Piccadilly event represents the door clos-

36 ing or departure time, and that the customers in question boarded at that stop after an unrecorded arrival or door-opening event. The other anomaly in the example is that two customers appear to tap their cards between the Grosvenor Gardens and Wilton Street stops, ap- proximately 20 seconds before the latter. Since distinct arrival and depar- ture times are recorded for both stops, iBus is assumed to have recorded— at a minimum—the times during which the doors were open, or an even earlier arrival (or later departure) time if the system used GPS or odometer data in the absence of door events. In either case, the AVL system indicates that the customers tapped their cards between the two stops. It then fol- lows that either the customers boarded at Grosvenor Gardens (or earlier) and did not tap their cards until after the bus was in transit, or that there was an unexplained error in the recording of one or more iBus events or both Oyster events. The first step in Wang’s (2010) origin-inference method, in which Oys- ter and iBus records are matched if they occur during the same minute, is accurate only if no more than one iBus event occurs during that minute. This criterion is satisfied in the cases shown in Figure 3-1, but the Wilton Street and Hyde Park Corner stop events are very close to sharing the same minute, and it should be expected that multiple stops would oc- cur during the same minute when viewing a large sample of iBus data. That condition is therefore not applied in this methodology: instead, all available data are matched using Wang’s second criterion, in which Oyster transactions are assigned to the iBus event closest in time. But an additional test can be applied before testing for temporal prox- imity. The AVL data used in Cui’s (2006) study contained door opening times while the data used by Wang contained only departure times, al- though iBus departure times can in some cases be ambiguous. Since the iBus data used in this study contain both arrival and departure times (notwithstanding the occasional ambiguities), Oyster transactions can in many cases be observed to fall between an iBus record’s arrival and depar- ture times, obviating the need for further temporal tests. Almost all BCMS records in Figure 3-1 fall between iBus arrival and departure times, but a degree of error is introduced by the use of Oyster data. The fare transactions shown by BCMS to have occurred while the bus was stopped at Victoria, Old Park Lane, Gerrard Place, and Tottenham Court Road still occur during these stops when viewed through their corresponding Oyster records. But other transactions recorded within

37 stops by BCMS are shifted between stops, such as the taps at Grosvenor Gardens or Hyde Park Corner. For the Oyster transactions that do not occur within an iBus event, applying Wang’s rule of matching Oyster data to the temporally closest iBus event would result in almost the same result as if the Oyster data contained the BCMS timestamps. The boardings at Green Park, for ex- ample, would be shifted from within their stop event to a time before it, but would still be matched to the same event due to their temporal prox- imity. In the example, the sole difference between the Oyster and BCMS approaches occurs between Grosvenor Gardens and Wilton Street. The two fare transactions, shown by BCMS to occur only a few seconds apart, occur during two different minutes. The earlier transaction is shifted back roughly 30 seconds, assigning it to Grosvenor as the closest stop rather than Wilton, while the later transaction is shifted past Wilton, which con- tinues to be its closest match. Based on the example in Figure 3-1, it appears reasonable to assume that the correct stop can often be inferred using Oyster and iBus data, but that in some cases the previous or next stop will be chosen instead.

3.2.2 Origin-Inference Methodology

Central to the origin-inference process is the matching of AFC and AVL data. Since there is a many-to-one relationship between fare transactions and stop events (each boarding is associated with a single stop event but each stop event can be associated with multiple passenger boardings), the relationship can be specified by the fare transaction. The following meth- odology is therefore performed once for each Oyster record. The origin-inference process is described in Figure 3-2. If an Oyster record represents a bus transaction, and if iBus data exist for the speci- fied route and trip number, 30 seconds are added to the Oyster start time, which is compared to the list of stops for the specified route and vehicle trip. If the offset Oyster time falls between a stop event’s arrival and de- parture time (as observed at Gerrard Place in Figure 3-1), that stop event is selected. If the Oyster transaction occurred between two stops, it is assigned to the closer of the previous stop’s departure time and the next stop’s arrival time. If the Oyster transaction occurs before the first stop, the first stop is chosen. If the Oyster transaction occurs after the final stop, or if the final stop was chosen because the Oyster transaction preceded it

38 Visual Paradigm for UML Community Edition [not for commercial use] Activity Diagram1

Is the record a duplicate?

[no] [yes]

Determine mode [rail]

[]

[rail or tram] Is start time recorded? [bus]

Do iBus data exist for the route and trip [yes] specified by the Oyster record? [no]

Is the Oyster time greater than the final stop's arrival time? [no] [yes]

[yes] [no]

Is the Oyster time less than the first stop's arrival time?

[yes] [no]

Does Oyster time fall between one Select first stop stop's arrival and departure times?

[yes] [no]

Select Oyster tap occurred between two stops. Alighting time and current stop Which one was it closest to in time? location unknown (cannot be inferred) [prev] [next] Select Select next stop Destination and previous stop end time already known

Discard record

Is the time between the Oyster end time and the selected iBus stop event time greater than the [yes] boarding error tolerance?

[no]

Origin already Origin unknown Origin inferred known

Figure 3-2. Activity diagram of the origin-inference process.

39 and was closest to it, no boarding location is inferred because it is assumed that riders do not board at the final stop of a vehicle trip. If a stop event was chosen during the previous step, an origin-inference error is calculated. This value is defined as the difference between the offset Oyster time and the closest iBus time: the arrival time if the next stop is chosen, the departure time if the previous stop is chosen, or zero if the Oyster transaction falls within a stop event. The origin-inference error is appended to the Oyster record, and if the magnitude of the error is greater than the user-defined maximum, the boarding location is consid- ered to be unknown. Otherwise, the bus stop ID of the chosen stop event is appended to the Oyster record as the inferred boarding location.

3.2.3 Origin-Inference Results and Sensitivity Analysis

The origin-inference processes was implemented as a Java application (dis- cussed in Chapter 6) and was tested using complete daily sets of Oyster and iBus data for all of Greater London over ten consecutive weekdays (the 6th through 10th and 13th through 17th of June, 2011). Each day’s data consisted of 6.1 to 6.5 million bus transactions, with a daily average of 6,295,634. The software performs the origin-, destination-, and inter- change-inference processes on one day’s worth of data in less than 30 min- utes, although the origin-inference portion is typically finished within the first five minutes.9 The program keeps a running total of origin-infer- ence statistics, which are written to a report after all Oyster records have been processed. The contents of this report are shown in Figure 3-3 and in tables Table 3-1 and Table 3-2 Table 3-1 shows the rates at which the various origin-inference rules were used to match or discard Oyster data.10 Less than two percent of transactions were discarded because of incomplete or missing data, while nearly 28 percent of all Oyster bus transactions, after timestamp trunca- tion and the 30-second offset, fell between a bus’s arrival and departure time.

9 The software was run on a 2.8 gigahertz Intel Core i7 machine with 8 gigabytes of RAM run- ning the Windows 7 operating system. 10 The statistics in Table 3-1 were averaged over the ten-weekday period, and were measured before the application of the maximum origin-inference error parameter.

40 Table 3-1. Detailed origin-inference results, ten-day average.

Result Count Percentage Cannot infer because bus route data not included in 36,210 0.57% Oyster record Cannot find route in iBus data 24,490 0.39% Cannot find trip in iBus data 61,123 0.96% Within stop event 1,764,673 27.82% Between stop events; closer to previous 1,632,375 25.73% Between stop events; closer to next 2,238,488 35.29% Before first stop event 487,865 7.69% After last stop event 98,561 1.55% Total 6,343,784

Figure 3-3 shows the distribution of origin-inference error for the ten-weekday period (the daily average distribution can be observed by simply dividing the frequencies by ten).11 The distribution is discontinu- ous at zero because it is piecewise defined: as discussed in section 3.2.2, the negative portion of the distribution is calculated using iBus arrival times, the positive portion is calculated using departure times, and the frequency of 18.4 million at zero accounts for the Oyster transactions that occurred between iBus arrival and departure times. Beyond its discontinuity, the origin-inference error histogram exhib- its two other notable traits. First, the distribution is visibly skewed to the left. Since negative error values indicate Oyster transactions that occurred before iBus arrival times, this rise in the left tail fits the expected result of more Oyster transactions occurring near a bus’s arrival time than its departure time, as illustrated with the Victoria stop in Figure 3-1. Second, the distribution rises sharply near -30 seconds and falls sharply again at 30. This resembles the expected result of adding the aforementioned skewed, discontinuous distribution to a uniformly distributed function on the in- terval [-30, 30]. The uniformly distributed component of the curve in

11 Negative errors in the histogram denote Oyster transactions that occurred before the clos- est bus arrival, while positive errors indicate transactions that occurred after the closest bus departure. Oyster transactions that occurred between a bus’s arrival and departure times are assigned an error of zero.

41

please, let me do it this way. Like most urban public bus networks, London Urban is here. Here is some e ng text dude, I’m going to write it here be- cause I can. So, please, let me do it this way. Like most urban public bus networks, London Urban is here. Here is some e ng text dude, I’m going to write it here because I can. So, please, let me do it this way. Like most urban public bus networks, London Urban is here. Here is Frequency 18,400,0001,400,000 somFrequencye e ng text dude, I’m going to write it here because I can. So, please, let me do it this way. Like most urban public bus networks, London Urban 18,200,0001,200,000 is here. Here is some e ng text dude, I’m going to write it here because I can. So, please, let me do it this way. Like most urban public bus networks, 1,000,0001,000,000 London Urban is here. Here is some e ng text dude, I’m going to write it here because I can. So, please, let me do it this way. Like most urban public 800,000800,000 bus networks, London Urban is here. Here is some efng text dude, I’m

600,000600,000

400,000400,000 Figure 2.4. Distribution of error between Oyster and iBus timestamps: e error is the minute of the Oyster transaction, plus 30 seconds, minus the iBus time (which contains 200,000200,000 seconds).

0 -180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180 Origin-inference error (seconds) London Urban is here. HeOreri giisn -sinofemrenec e e rronrg (s etceonxdt s)dude, I’m going to write it Figurehere b 3-3.eca uHistogramse I can .of S origin-inferenceo, please, let m errore d ofor i tall th Oysteris wa ybus. L boardingsike mos t urban public bus networks, London Urban is here. Here is some efng text dude, I’m going to write it here because I can. So, please, let me do it this way. Like most urban public bus networks, London Urban is here. Here is some e- turning t ematchesxt dude, anI’m expected going to sidewrit eeffect it her eof b eusingcause truncated I can. So, plandea soffsete, let m eOyster do timeit thi svalues way. Lratherike m thanost u BCMSrban values.public Ifbu as ridernetw otapsrks, his Lo cardndon between Urban i sa bus’shere. arrivalHere is andsom departuree e ng te xtime,t dud ebut, I’ mif gthatoing stop to w eventrite it doeshere notbec aspanuse I the can mid. So,- pointplease ,of let a m minute,e do it t thehis w30-seconday. offset of the minute-resolution Oyster timestamp will occur up to 30 seconds before the arrival time or up to 30 seconds after the departure time. For example, Figure 3-5 shows that a rider tapped after the bus’s arrival at Grosvenor Gardens but before its departure. Since the stop event did not span the midpoint of the minute (18:54:30), the offset Oyster record appears a few seconds before the ar- rival time, introducing a small error. In rare cases, bus route or trip data are recorded incorrectly, causing some Oyster records to be erroneously matched to iBus records several hours away. This noise adds long tails to the origin-error distribution, resulting in 96.0 percent of origin-inference error values falling within a tolerance of ±2 minutes. Increasing this threshold to ±5 minutes raises the 3 proportion by only 1.4 percent, illustrating that this choice of thresholds sits well beyond the sensitive portion of the parameter’s range. While the choice of this five-minute margin can be seen as arbitrary, it seems reason- able to expect an occasional five minute difference between AFC and AVL times, especially since buses sometimes stop for several minutes at busy

42 stations or signals during peak hours, and because customers might in some cases tap their cards after the bus has left the stop. The five-minute boarding-error tolerance (also used by Cui [2006] and Wang et al [2011]) is therefore applied in this study. This value, however, can be easily changed by the user at runtime. The results of the origin-inference process on the ten-day sample, af- ter the application of the five-minute error threshold, are summarized in Table 3-2. The process was unable to infer boardings for 1.36 percent of bus transactions, while an additional 2.61 percent were matched but excluded because their origin-inference errors exceeded the five-minute limit. The origins of the remaining 96 percent of Oyster bus trips are inferred, for an average of approximately six million bus stages per day.

Result Count Percentage Not inferring bus boarding because Oyster 85,441 1.36% record was not matched to an iBus record Not inferring bus boarding because the time 164,566 2.61% between its boarding tap and the nearest iBus event is beyond ±5 minutes Bus stages with origin inferred within ±5 6,045,627 96.03% minutes Total 6,295,634

3.3 Destination Inference

Like the origin-inference process, the process for inferring bus alighting times and locations builds upon previous research to create a more robust and efficient algorithm. As stated in the previous section, the three se- quential processes are performed partly in parallel for efficiency, but the destination portion is presented here as a logically discrete process for the sake of clarity.

43 3.3.1 Input Data

The primary input to the destination-inference process is the origin-in- ferred Oyster data, to which the rider’s inferred destination will be ap- pended. The process is founded on the two key assumptions introduced by Barry et al (2002): that the best estimate of a rider’s alighting location is the stop closest to that rider’s next origin, and that the best estimate of a rider’s final daily destination is that rider’s first daily origin. The desti- nation-inference process therefore requires spatial information about the rider’s possible alighting locations and about her subsequent origin. In the case of London, this information is contained in two data sets: one of bus stops and another of rail stations, including Underground, Overground, National Rail, DLR, and Tramlink modes. The fourth input to the process is iBus data, which is used to infer alighting times as demonstrated by Wang et al (2011).12 The only information required of stations are the unique identifiers by which they are referenced in the Oyster data—which in this case is the British National Location Code (NLC)—and the station’s Cartesian coor- dinates. Station coordinates were copied from GIS data obtained from TfL but were then converted from geographic coordinates (measured in an- gular degrees) to linear coordinates (measured in meters) using the British National Grid projected coordinate system. The benefits of this conver- sion are, first, that it avoids the spatial distortion that results from the con- vergence of meridians at non-equatorial latitudes (such as London’s) and, second, that meters provide a satisfactory resolution for pedestrian-scale transportation analysis (which is required for the inference of alightings and interchanges). By contrast, the use of degrees of latitude and longi- tude would require the processing of real numbers rather than integers, which would double or quadruple the amount of hardware resources re- quired to process spatial data13. Bus stop data similarly require only a unique identifier and spatial co- ordinates. The “stop code” field from the BusNet system is used, since it is guaranteed to be unique and to persist indefinitely. iBus data, which

12 See section 3.1.2.

13 The software stores metric coordinates and distances using Java’s short and int data types, which comprise 16 and 32 bits, respectively. Real numbers would be processed using the

64-bit double data type.

44 use their own internal, temporary bus stop identifiers, are assigned the ap- propriate BusNet stop codes when exported from TfL’s servers, enabling Oyster and iBus data to be matched in the software. Each stop’s spatial coordinates are stored by BusNet in the British National Grid coordinate system, which is why that system in particular (among many projected metric coordinate systems) was chosen for the station data. Data were obtained for 21,554 bus stops, which describe the locations of each bus stop’s pole marker. In many cases, multiple stops are clustered near a single logical node on the transport network, such as an intersec- tion or rail station (Figure 3-4 and Figure 3-5). Data were also obtained for

Figure 3-4. Letter-designated bus stops near Elephant and Castle Tube station (source: TfL).

Figure 3-5. Elephant and Castle stops S and T, each having its own pole marker and shelter.

45 1,435 stations of various rail modes, 1,361 of which were spatially unique (in some cases multiple codes relate to different modes or gate clusters at the same physical station). These stops and stations are mapped in Figure 3-6, which encompasses all of Greater London. Most of the stations be- yond the extent of the bus network belong to the National Rail system, which extends through all of Great Britain.

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!! !! !! ! ! !! ! ! ! !! !! !! ! !!! !! ! ! ! ! ! ! ! ! !! !!! ! ! ! ! ! !! ! ! ! !! !! ! ! !!! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! !! !! ! ! !! ! ! ! !! !! ! !! !! !! ! ! !! !! ! ! !!! ! ! ! ! !!! !! !! ! ! ! ! ! ! !! ! ! ! ! !!!! ! ! ! ! ! ! ! !! !! ! !! ! ! ! !! !! !! ! ! ! ! ! !! ! ! !!! ! !!! ! ! !! !!!! !!! ! !! ! ! !!! !! ! ! ! !! !!! ! ! ! ! !! ! !! ! ! ! ! !! !! ! ! !!! ! ! ! ! !! !! ! !! ! ! ! ! ! !!! ! ! ! ! !! !! ! ! ! ! ! ! ! !! ! !! ! !! ! ! ! ! ! !!!! !! ! ! !! !! !! ! ! !! !! ! !! ! !!! ! !! ! !! ! !! !! ! ! ! !!! !! ! !!! ! ! ! ! ! ! ! !! ! ! ! ! ! !! ! !! ! ! ! ! !!! ! ! ! !! !! ! ! !!!! ! !! ! !! ! !! ! ! ! ! !!! !! ! ! ! ! ! !! ! !! !! !! !!! ! ! ! ! ! ! ! !!! ! !!!! !!! ! ! ! ! ! !! ! ! ! ! !!! ! ! !!! ! !! !!! ! ! !! ! ! ! !! !!!! ! ! ! !! ! ! ! ! !! ! ! !!! ! !! !! ! ! ! ! ! !! ! ! !!! ! ! ! ! !!! !! ! ! ! ! !! ! !! !! ! ! !! ! ! ! ! ! ! ! ! ! !! ! ! ! !! ! ! !! ! ! ! ! !!!! ! ! ! !! !!! ! ! !!! ! ! !! !! !! ! !! ! ! !!!! ! ! ! !!!!!! ! ! ! ! ! ! ! ! !!! ! !!! !!!! ! !! !! !! !! !! ! ! ! !! ! !! ! ! !!! ! ! ! ! ! !! ! ! !!!! !! ! !! ! !!!!!!! ! ! !! ! ! ! ! ! ! ! ! !!! !! ! ! !!! ! ! ! ! !! ! !! !! ! !!!! ! ! !!!! !!! ! ! ! ! ! !! ! !!! !!! !! ! ! ! !!! ! !!! ! ! ! ! !! ! ! ! ! !!! !! ! !!!! ! ! ! !!! !!!! !!! !! !! ! ! !! ! ! ! !! ! ! ! ! ! !! !!! !! !!!! ! ! !! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! !! !! !! ! ! ! ! ! ! ! !!!! ! ! ! ! ! ! !! ! ! ! !! !! !! ! !! ! ! ! ! ! ! ! !! ! ! ! !!!! !!! !! ! ! ! !! !!! ! ! !! ! !! ! ! ! ! !! ! !!!!!!! ! !! !! ! ! ! ! ! !! !! ! ! !! !! ! ! !! !!!!! ! !! ! ! !! ! ! ! ! ! ! ! !!!! ! ! !! !! ! ! ! !! ! ! !! ! !!! ! ! ! !! ! !! ! !!! ! ! !! ! ! ! ! !! ! !! ! ! ! ! ! !! ! ! ! ! ! ! !!! !!! !! !!! ! !! ! !! ! ! ! ! ! ! ! ! ! !! ! !! ! !! ! !! !! ! ! ! ! !! ! ! ! ! !!!!!! ! ! ! ! ! !! ! !! ! ! ! ! ! !!! !!! !! ! !! !! ! ! ! !! ! !! !! ! ! !!! !!! !!!! ! ! !! ! ! ! ! ! ! ! !! ! !!!! !! ! ! ! !! ! ! ! !! !! !! !!!!!!!!!! ! ! ! ! ! !! ! ! ! ! ! !!! !! !!! ! !! ! ! ! ! ! ! !! ! !!!! !! !! ! ! ! ! ! ! ! !!!!!! ! ! !! ! ! ! ! ! !! !! !! !! !! ! !! ! ! ! !! ! !!! !! !! ! ! ! !! ! ! ! !! ! !! ! ! !!! !! ! ! ! ! !! ! ! ! ! ! !! ! ! ! ! ! ! ! ! !! ! ! ! ! !!! ! ! !! ! ! ! ! ! !! !! !! ! ! !! !! !!! ! ! ! !!! ! ! !!! ! ! ! ! ! !!!! ! ! !! ! ! !! ! ! !! ! ! ! ! ! !!! ! ! ! !!! ! ! !! !!! ! ! !!! ! ! ! ! ! ! !! ! ! !! ! !! ! !! !! ! !! ! ! ! ! ! !! ! ! !! ! ! ! ! ! ! ! ! ! !!! ! ! ! ! !!! ! !! ! !!! ! ! !! ! !! !!! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !!!! !! ! ! ! !! ! ! ! !! ! ! !!!! ! ! ! ! !! ! ! ! !! ! ! ! ! ! ! ! ! ! ! !!!! ! !! ! ! ! ! ! !! !! ! ! ! !! !! !! ! ! ! ! ! !! ! ! ! ! !! ! !!!! !! ! ! ! !! ! ! ! ! ! ! ! ! ! !!!! !! !! !! ! ! ! ! ! ! !! ! ! ! ! !! ! !!!! !! ! ! ! ! ! !! ! ! !! ! ! ! ! !! !!!! !! ! ! ! ! ! ! !!!! ! ! ! ! !! ! ! ! !! !! ! ! !! ! ! ! ! ! ! ! ! ! !!! ! !! ! ! ! ! !! ! ! ! ! ! !!!!!! ! ! !! ! ! ! ! !! ! !!!! ! ! !!! ! ! !! !! ! !! ! !! !!!! ! ! !! !! ! ! ! ! ! ! !! !! ! !! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !!!! !! !! ! !! ! ! ! !! ! ! ! !!!! ! ! !! ! ! ! !! ! ! ! ! ! ! ! ! !! ! ! ! ! !!! ! ! ! ! !! !!! !! !! !!! ! !! ! !! !! !!! ! ! ! ! !! ! ! ! ! !! ! !!! !! ! ! ! ! ! ! ! !! ! ! ! ! ! ! !! ! !!! ! ! !!! ! ! ! ! !! !! ! ! !! ! ! !! ! ! ! !! ! ! ! ! !! !! !! ! ! ! ! ! ! !! !! !! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! !!!! ! !! ! ! !! ! ! ! ! ! !! ! !! ! ! ! !! ! ! ! ! ! ! ! !! ! ! !! ! ! ! ! !! !! ! ! ! !! ! ! !! !! ! ! !!! ! ! ! ! ! !! ! ! !! ! !!!! ! ! ! ! !!!! ! ! !!!! ! ! ! ! ! ! ! !! !! ! ! ! ! !! !! !! !! ! ! !! !! !! ! !!! !! !! !! ! ! ! ! ! ! ! ! ! !! ! ! ! !! ! ! !! !!! ! ! ! ! !! !! ! ! ! !! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! !! ! !! ! ! ! ! !! ! ! ! ! ! ! ! !! !! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! !! !! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! !!! ! ! ! ! ! ! ! ! ! ! ! !

Bus stop ^ N 0 5 10 15 20 25 km Rail station

Figure 3-6. Stop and station locations in Greater London.

46 3.3.2 Destination-Inference Methodology

The destination-inference process draws from several of the methodolo- gies described in section 3.1.2, using a refined processing algorithm to dis- till the destinations of over six million passenger bus trips from roughly 16 million Oyster transactions and five million iBus records in less than ten minutes. The logic of the destination-inference process is built upon Barry et al’s (2002) two assumptions: that riders alight at the stop closest to their subsequent transaction location or, in the case of the last trip of the day, that they alight at the stop closest to their first daily origin. These assump- tions will be referred to herein as the closest-stop rule and the daily-symmetry rule, respectively. It should be apparent to anyone who has ridden an urban bus, how- ever, that there are many cases in which these two assumptions are not valid. The closest-stop rule, for example, is regularly violated by any rider who alights a bus, walks alongside the bus route to visit two or more businesses, then makes his next fare transaction at a stop or station that is closer to some other stop on the previous route than it is to the stop at which he actually alighted. The daily-symmetry rule is similarly violated by any rider whose final Oyster trip of the day ends somewhere other than his first Oyster origin, whether because he started and ended his day in different places, or because his first trip was preceded (or his last trip was followed) by a non-Oyster trip, such as a bicycle or taxi ride. Despite these shortcomings, the closest-stop and daily-symmetry rules have been shown to provide a reasonable approximation of riders’ alighting loca- tions, as discussed in section 3.1.2. In the absence of observed alighting information, Barry et al’s assumptions reflect our best estimates of where riders might have ended their bus trips. The closest-stop and daily-symmetry rules necessitate the calculation of several distances. The determinant location—the origin of the subse- quent trip or the first origin of the day—shall be referred to as the target location. The distance between the rider’s target location and each stop served by the current vehicle trip’s pattern (a distinct sequence of bus stops served by one or more vehicle trips on a bus route) must therefore be calculated in order to determine which stop is closest. To accommodate these calculations, the set of all stops within each pattern, and the set of

47 all patterns within each route, are distilled from the daily set of observed iBus data before any destinations are inferred. Once spatial information is known for all bus patterns, the algorithm illustrated in Figure 3-7 is applied to each Oyster record. First, duplicate Oyster records are discarded, since riders might sometimes tap a card in error before making a successful transaction. Valid non-duplicate transac- tions are then processed according to their mode: rail destinations should already be known, tram destinations are not inferred because AVL data are unavailable, and bus transaction are passed to the next step in the process so that their destinations might be inferred. If a bus transaction is the only record associated with the card that day, it is assumed that any other travel that day was made without the Oyster card—possibly on a non-Oyster or private transport mode. The destina- tion of the journey stage is therefore unknown. If there was more than one transaction made with the card that day, however, the current stage’s origin-inference error is tested. If the magnitude of the error is greater than the maximum origin-inference error (specified by the user), the ori- gin is considered to have been unreliably inferred, and it is assumed that the destination cannot be reliably inferred either. If the bus stage’s origin has been satisfactorily inferred, the target loca- tion for the destination-inference process is defined. If the journey stage is the cardholder’s last that day, the origin location of his first trip is se- lected as the target location. Otherwise, the origin of the cardholder’s subsequent trip is selected. If the target location is unknown, or is a bus boarding that was not in- ferred within the maximum origin-inference error, the destination of the current trip is considered unknown. Otherwise, the algorithm attempts to find spatial coordinates for the current bus pattern and for the target location. If the coordinates of either the target node or the pattern are missing, an alighting location is not inferred.

48 Is the record a duplicate?

[yes] [no] Determine mode

[bus] [rail] Are there any other records in the card's daily travel history?

[tram] [yes] [no] Has the record's boarding location been inferred within the user-specified boarding-error tolerance?

[no] [yes] Is the current stage the final stage of the card’s daily travel history?

Define "target location" as origin [yes] [no] Define "target location" as origin of first trip of daily travel history of next trip in daily travel history

Is the target location known, or has it been inferred within the user-specified boarding-error tolerance?

[no] [yes]

Are spatial coordinates known for the current stage's Select the next closest bus route/pattern and for the target location? bus stop on the current route/pattern Select the bus stop on the current record's route and pattern that is [yes] closest to the target location. [no]

Is the bus traveling away from the rider's next tap location?

[yes] [no] Is the selected stop within the maximum interchange-inference distance of the next tap location?

Alighting time and location unknown [no] Did iBus record a stop event (not inferred) for the associated route and [yes] trip at the selected bus stop? Destination and Alighting time and end time location inferred If the current stage is not the last stage already known of the day, was there enough time for [no] the passenger to have alighted at the chosen stop and to have transferred [yes] Skip record to the target location?

Assume alighting time to [yes] [no] be iBus stop event time

Figure 3-7. Activity diagram of the destination-inference process.

49 Next, the closest-stop and daily-symmetry rules are applied as illus- trated in Figure 3-8. If a rider was inferred to have boarded a bus at stop 1, and if her next tap occurred at station Y, stop 5 would be selected as her tentative alighting location because it is the closest to Y. If her second tap occurred on another bus route rather than at a rail station, the same logic would apply: if her next journey stage was inferred to have originated at stop X, stop 4 would be selected as her tentative alighting location. If the current trip was the cardholder’s last of the day, and if her target location (i.e., her first origin of the day) was stop 6, she would also be inferred to have alighted there for her final trip. At this point, it is possible that the candidate alighting location is not a feasible destination for the cardholder. For example, if the cardholder was inferred to have started her current bus trip at stop 5, but if her sub- sequent transaction was a bus boarding at stop X, the bus was moving away from her subsequent boarding location: the closest stop (stop 4) was already served by the bus before she boarded. It is possible in this case that one or both of the origins were inferred erroneously, or that both origins are correct but that the rider did not alight at the closest stop to her next Oyster transaction, making the closest-stop rule inapplicable. To handle

 

      Y         X  



  

      

Figure 3-8. Example of destination inference for a passenger trip on a bus route (green) fol- lowed by another bus trip (red) or by a rail trip (blue).

50 this scenario, destinations are not inferred if the candidate alighting loca- tion is the same as its journey stage’s boarding location, or if the candidate alighting location was served by the bus prior to its serving of the rider’s boarding location. The closest-stop rule is further checked against the distance between the candidate alighting node and the target node. If the closest stop on the current route is far from the cardholder’s subsequent transaction location, it is assumed that proximity between the two stages was not a priority for the cardholder, and that the nearest-stop rule is therefore inapplicable. Thus, if the distance between the candidate alighting location and the tar- get location is greater than the user-specified maximum destination-inference distance, the destination is not inferred. If the candidate alighting node has passed all of the previous tests, an iBus record is sought for the bus route and trip (both of which are speci- fied by the Oyster record) at the candidate alighting location. If found, the iBus record’s arrival time is used as the candidate alighting time. If an iBus record cannot be found, the next-closest bus stop to the target node is chosen as the candidate alighting node, and testing is repeated starting with the determination of whether the bus was traveling away from the subsequent transaction. Once a candidate alighting node passes all of the aforementioned tests, a final test is applied which checks whether the rider could have walked from the candidate alighting location to the target location in the avail- able time. For example, if stop 4 in Figure 3-8 was chosen as an alighting location because it was the closest to stop X, but if the bus arrived at stop 4 several minutes after the rider boarded at stop X, it is likely that the rider alighted at an earlier stop. Stop 3 is then tested as the possible alighting location, because it was the next closest and next earliest, and therefore might have provided enough time for the rider to subsequently board at stop X. This test is not applicable for the rider’s final trip of the day, since the duration of her egress is unknown. For any other transaction, however, the assumed minimum time required to move between the two locations, t*, is calculated as:

51 t* = —d – R ε where d is the Euclidean distance between the candidate alighting location and target location, R is the user-specifiedmaximum interchange speed, ε is the maximum boarding-error tolerance.

The maximum interchange speed is the greatest speed at which a person is assumed to be able to walk (or run) in the urban environment, taking into account that d is the Euclidean rather than actual distance (i.e., the path taken is likely not straight), and that the pedestrian might be delayed by street crossings or other impediments. The maximum boarding error tolerance is then subtracted to account for the iBus arrival time not neces- sarily being the time at which the rider alighted. This parameter is used in this equation because it is assumed that the amount of error between a cardholder’s alighting and its associated bus arrival event is generally similar to the amount of error between his boarding (and fare transaction) and its associated arrival event. To test whether it was feasible for the rider to have alighted at the inferred location with enough time to walk to and tap at the subsequent

location, her inferred alighting time, , is compared to her subsequent

transaction time, t2, as follows:

* t1 + t < t2

If the expression is true, the passenger is inferred to have alighted at the specified location and time. But if the candidate time and location fail the test (i.e., if the expression is false), the next-closest stop becomes the candidate alighting location, and previous tests are repeated using the new candidate, starting with the determination of whether the bus was traveling away from the subsequent passenger origin.

3.3.3 Destination-Inference Results and Sensitivity Analysis

As discussed in section 3.2.3, the origin-, destination-, and interchange- inference processes were tested together using complete daily sets of Oys-

52 ter data for all of Greater London over ten consecutive weekdays.14 Des- tination inference was completed within the first fifteen minutes of the program’s execution (along with origin inference and part of the inter- change-inference process). The number of destinations inferred, however, depends upon the setting of three user-defined parameters. The first parameter, maximum walk speed, addresses to the speeds at which passengers travel when they are not in the public transport system (calculated as a function of time and the Euclidean distance traveled). Fig- ure 3-9 illustrates the distribution of average speeds between bus or rail destinations and their subsequent rail origins.15 The prevalence of average speeds below one kilometer per hour reflects passengers who performed activities between journey stages. Someone who was at his workplace for nine hours, for example, and who arrived and departed on the same route (having inbound and outbound stops on opposite sides of the street, 20 meters apart), would have an observed speed of 0.002 kilometers per hour.

Frequency 















  , , , , , , , , , Average Speed between station exit or inferred bus alighting and subsequent station entry (meters per hour)

Figure 3-9. Distribution of average out-of-system speeds.

14 The data were obtained for the 6th through 10th and 13th through 17th of June 2011. 15 Speeds are calculated as Euclidean distances between station exits or inferred bus alightings and subsequent station entries. Subsequent bus boardings are excluded to eliminate wait time from the calculation.

53 The second most prevalent cluster of speeds is observed between 3 and 7 kilometers per hour, and likely reflects the speed at which passengers made interchanges. Ninety percent of passengers exhibit out-of-system speeds of less than two kilometers per hour, which likely reflects activities such as work or school, or return trips that start where the previous trip ended. The 90th percentile speed is approximately 5 kilometers per hour, while 8 kilo- meters per hour reflects the 99.8th percentile. Since the maximum walk- speed parameter is used to test whether passengers had enough time to alight a bus before walking to their next transaction locations, the value of 8 kilometers per hours was chosen because it sets a conservatively high threshold for eliminating passengers from the destination-inference pro- cess. This speed is significantly higher than most of the speeds typically observed between Oyster trips, yet lower than any unreasonably high speeds that might indicate vehicle travel (perhaps by taxi, private auto- mobile, or non-Oyster public transport). The second parameter, boarding-error tolerance, is used by both the origin- and destination-inference processes. As described in the previous section, this amount of time is added to the maximum walk speed value to account for the errors typically observed between Oyster and iBus times. The sensitivity of this parameter and its setting of five minutes were described in section 3.2.3. The final parameter used to estimate alighting locations is the maxi- mum destination-inference distance. When the distance between a bus alighting location and the rider’s subsequent fare transaction is great enough, it is reasonable to expect that the closest-stop rule does not apply. A distance of several kilometers, for example, could indicate that the rider took a non-Oyster transport mode between the two journey stages, and therefore did not necessarily alight at the stop closest to the subsequent fare transaction location. A more moderate distance such as 1.5 kilometers might have been traversed by foot, but the difference between any two consecutive potential alighting stops would be relatively small in compar- ison to the total distance walked by the cardholder during that segment of her journey, making its proximity to the next tap less important. A maximum destination-inference distance should therefore be chosen that excludes longer values which might be less relevant to the closest-stop rule while retaining the shorter inter-stage distances which constitute the majority of observed Oyster activity. The distribution of

54 out-of-system distances is illustrated in Figure 3-10,16 and a list of their cumulative frequencies is shown in Table 3-3. A value of 750 meters was chosen as the maximum destination-inference distance, which enables over 94 percent of observed bus stages to potentially be included in the results while avoiding the small marginal gains that would be realized by using a greater distance.

Frequency ,

,

,

,

,

,

,

            , Distance between subsequent tap and closest stop on current route (meters)

Figure 3-10. Distribution of Euclidean distances between candidate alighting stops and next fare transaction.

Table 3-3. Cumulative frequencies of observed inter-stage distances.

Euclidean distance Percentile Marginal (meters) change 500 90.5% 750 93.5% 3.5% 1,000 95.2% 1.7% 1,500 96.9% 1.6% 2,000 97.6% 0.7% 2,500 98.0% 0.4%

16 The candidate alighting stop is the closest stop on a cardholder’s bus stage to his next Oys- ter transaction, regardless of whether that stop was eventually discarded by the destination- inference process in favor of another stop.

55 The rates at which bus alighting times and locations were inferred over the ten-day sample period are shown in Table 3-4.17 The table also indi- cates the frequency with which passenger bus stages were excluded from the process by each of the algorithm’s tests. Since failure of any one test can deem a record’s destination “not inferable,” tests are presented in the order in which they are applied: later tests can only exclude stages that have already satisfied the conditions of all earlier tests. While most tests immediately eliminate transactions that do not sat- isfy their conditions, the two tests that are applied recursively are treated separately at the bottom of the table. If iBus data exist for the cardhold- er’s route and vehicle but not for the candidate alighting node, or if there was not enough time for the rider to have alighted at the candidate loca- tion before her next transaction, the program resumes the destination-in- ference process using the next-closest stop. This process is repeated until a destination is successfully inferred, until one of the non-recursive tests is failed, or until all stops on the vehicle trip have been tested. The table indicates the frequencies at which the recursive tests were applied (count- ing each bus transaction only once, regardless of how many of its stops were tested). Most recursively tested bus stages were either successfully inferred or were eliminated by another test: only thirteen transactions per day typically exhaust all stops on the observed bus pattern during recursive testing.18 On average, destinations were inferred for 75.62 percent of Oyster bus stages, or roughly 4.7 million bus stages per day. Approximately 28 percent of the excluded bus stages (seven percent of total bus stages) could be retained by improving the origin-inference process or by widening its tolerance, while additional stages could be retained (at the expense of ac- curacy) by adjusting the maximum walk-speed and distance parameters.

17 The process was executed using a maximum boarding error of ±5 minutes, a maximum walk speed of 8 km/h, and a maximum destination-inference distance of 750 meters. 18 For example, if a rider boarded at the third to last stop, and if iBus events were not recorded for the last two stops, the transaction would exhaust all possible alighting locations and thereby fail all recursive tests.

56 Table 3-4. Destination-inference results:

Result Count Destination not inferred because: Only one stage on card that day 305,351 4.85% Current stage's boarding location not matched to 80,707 1.28% an iBus record Current stage's boarding location inferred beyond 156,580 2.49% allowable error (±300 sec.) Next stage's boarding location not matched to an 55,620 0.88% iBus record Next stage's boarding location inferred beyond 151,184 2.40% allowable error (±300 sec.) Last stage of day; rider traveling away from first 203,238 3.23% origin of day Rider traveling away from next origin 230,657 3.66% Last stage of day; distance between candidate 226,571 3.60% alighting location and first origin of day greater than 750 meters Distance between candidate alighting location and 197,873 3.14% next origin greater than 750 meters All stops in pattern failed recursive tests (see below) 12 0.00%

Subtotal, destination not inferred 1,607,793 25.54% Destination inferred 4,687,842 74.46% Total bus stages 6,295,634

Recursive tests: iBus did not record an event for the nearest stop; 54,593 0.87% tested next-closest stop Not enough time for rider to have alighted at the 20,424 0.32% nearest stop; tested next-closest stop

57 3.4 Summary

The methods presented in this chapter are shown to infer bus boarding locations in 96 percent of cases and of bus alighting times and locations in 76 percent. Most uninferred boardings are due to the Oyster and iBus data not matching within the five-minute threshold, although this param- eter could be changed after additional validation. Further analysis should be conducted to test whether a more relaxed maximum boarding-error tolerance would be reasonable. The origin-inference tolerance doubly af- fects the destination-inference process, since origin locations are required both for the journey stage being processed and for the subsequent stage. The software developed to execute this algorithm is discussed in detail in Chapter 6 and its outputs are used in the interchange-inference and full-journey matrix-expansion processes in Chapters 4 and 5. Applica- tions of this algorithm, and of the algorithms that utilize its outputs, are presented in Chapter 7.

58 Interchange Inference 4

The origin- and destination-inference processes discussed in the previous chapter enrich the Oyster data set by adding time and location informa- tion to most bus boardings and alightings. These enhanced bus records can then be analyzed along with Oyster rail transactions from the same card, enabling the observation of passengers’ full journeys or daily travel histories. This chapter presents a method for inferring whether each of an Oys- ter card’s journey stages was linked to the next through an interchange (or transfer1). Doing so enables the analysis of full passenger journeys—in- cluding those that span multiple public transport modes—which are im- portant descriptors of travel demand, and are critical for transport plan- ning at the network level. In addition to its value for providing full-journey information, this work is also useful for studying interchanges themselves. By yielding quantitative information about the distances and durations of inter- changes—two key determinants of interchange disutility (Hine et al 2003)—this work can complement qualitative studies of interchange fa- cilities to help planners improve the transfer experience, thereby reducing barriers to cross-modal travel and enabling cities and their residents to enjoy the social and economic benefits of increased mobility and acces- sibility (Guo and Wilson 2007, TfL 2001, 2002, 2009).

1 The terms transfer and interchange will be used interchangeably.

59 4.1 Concepts and Terminology

Before discussing the interchange inference process, it is necessary to de- fine a few terms and concepts used in this research. First, since this chap- ter addresses the problem of inferring interchanges between AFC journey stages, it is necessary to clarify the concept of a journey stage. At many London Underground stations, for example, an Oyster cardholder can alight a train, walk to another platform (serving a different Underground line), and board another train without tapping out or leaving the station. By convention, this is informally referred to as an interchange, but the change is not recorded by the Oyster system because both of the custom- er’s train rides occur between his entry and exit taps, thereby associating both trips with a single Oyster record.2 In the context of this research, an Oyster journey stage (or simply journey stage) is therefore defined as any portion of a rider’s travel activity that is represented by a single Oyster record. Each rail journey stage will therefore include one or more pas- senger trips, while the requirement that Oyster bus fares or passes be paid or validated onboard guarantees that each bus journey stage relates to a single passenger trip. Second, it is necessary to define the concept of an interchange as it relates to this work. It should be generally agreed that the passenger in the previous example, or another passenger who alights a bus and imme- diately walks to, waits for, and then boards another bus (serving a route perpendicular to the first for example) both performed interchanges. Al- ternatively, it should be agreed that if the Tube passenger left the station and picked up his child from day care before returning to the station and boarding the second train, or that if the bus passenger collected a package at the post office before boarding her second bus, neither case would be considered an interchange. The distinguishing difference in both cases is the purpose for the transitions between the riders’ trips. In the latter case, the passengers traveled to the locations mentioned in order to perform activities from which they derived utility (picking up a child and collect-

2 The Oyster data set actually contains two records for each rail transaction: one for the entry tap and another for the exit. These transactions are combined by the software prior to the interchange-inference process (as described in Chapter 6) and are thus considered a single record.

60 ing a parcel). In the interchange case, their purpose was simply to board another transit vehicle in order to derive utility from activities performed at subsequent destinations. There are many cases, however, in which the discerning of inter- changes from activities may be more subjective. If, for example, a passen- ger purchases a cup of coffee while walking from her alighting bus stop to her subsequent bus stop, she is deriving utility from her purchase but is unlikely to have traveled to that location solely for the coffee—she pre- sumably could have made such a purchase at another interchange location or at her final destination. While this example might be considered both an activity and an interchange, the important distinction from a network- planning perspective is whether the customer chose to travel to that loca- tion to make her purchase, or made her purchase at that location because she happened to be transferring there. If the latter case were true it would mean that the customer’s demand for travel lies with her ultimate destina- tion rather than the bus stop near the coffee house. For this research, an interchange is therefore defined as a transition between two consecutive journey stages that does not contain a trip-gen- erating activity. The passenger may have derived utility from an activity performed during the transition, but the activity was not reason enough to make the trip: the primary purpose of the transition was to connect a previous stage’s origin to a subsequent stage’s destination. Lastly, the inference of interchanges allows the linking of journey stages into full journeys. Taking into account the concept of journey stages and interchanges, it follows that a full passenger journey can be defined as a sequence of journey stages connected exclusively through interchanges. Passengers may still transfer between rail lines using a sin- gle Oyster transaction, but these “behind-the-gate” interchanges are in- cluded within rail journey stages and are therefore not considered in this algorithm.3

3 Behind-the-gate interchanges could be inferred by integrating a path choice model, such as those developed specifically for the TfL network (Guo 2008, Paul 2010) or elsewhere (Raveau et al 2010).

61 4.2 Previous Research

4.2.1 Discerning Activities Using AFC Data

This research aims to infer interchanges by distinguishing them from trip- generating activities, but activity information is not recorded by AFC sys- tems. By noting the spatial and temporal characteristics of certain activity types, however, these patterns can be detected in the AFC data. Holton (1958) distinguishes “convenience goods,” for which prox- imity to the customer is a key concern, from “shopping” and “specialty” goods, for which variations in price, quality, or selection are great enough to warrant dedicated trips. Holton also notes that convenience purchases (generalized here to convenience activities) are typically shorter in dura- tion than other types. The implications for this research are that conve- nience activities—which by definition are not trip-generating activities and which can therefore take place during interchanges—can be partially distinguished from trip-generating activities by their duration. Kuhnimhof and Wassmuth (2002) mine a database of travel survey in- formation to detect correlations between activity types and their spatial and temporal patterns such as duration, frequency, and time of day in order to calibrate the activity-generating component of a travel-demand forecasting model. Chu (2010) searches for similar spatial and temporal patterns in a set of AFC data from a bus network (for which he previously inferred origins, interchanges, and destinations) to infer general activity types such as home, work or school, and recreation.

4.2.2 Interchange Inference Using AFC Data

The tendency of trip-generating activities to be generally longer in dura- tion than convenience activities has led several researchers to infer bus- to-bus interchanges according to the amount of time elapsed between consecutive boarding transactions or journey stages.4 Bagchi and White (2004, 2005) assume that two bus stages are linked if they occur on differ- ent routes and if both AFC boarding transactions occur within 30 minutes of one another. Barry et al (2009) and the ’s re-

4 Thill and Thomas (1987) provide a literature review of early attempts at trip chaining.

62 gional planning organization (Metropolitan Transportation Commission 2003) use the same assumption, while Okamura et al (2004) apply a thresh- old of 60 minutes, McCaig and Yip (2010) allow 70 minutes, and Hoffman and O’Mahoney (2005) set an upper limit of 90 minutes. Seaborn et al (2009) use Oyster boarding transactions to infer inter- changes between London buses, and use Oyster station entry and exit data to infer transfers between bus and Underground. After exploring the distributions of time for these various journey types, they recommend thresholds of 20 minutes for tube-to-bus interchanges, 35 minutes for bus-to-tube, and 45 minutes for bus-to-bus. Seaborn et al then calculate the distributions of cardholder journeys per day and stages per journey, and compare both to the London Travel Demand Survey (LTDS). Chu and Chapleau (2007, 2008) infer alighting times and locations us- ing the GPS location-stamped boarding transactions of other cardholders, while Wang et al (2011) and Munizaga et al (2011) infer alighting times and locations using AVL data. All three studies thereby eliminate in-vehicle travel time from their observations, providing a more accurate estimate of actual interchange times. Munizaga et al set a 30-minute interchange threshold between bus stages, while Chu and Chapleau set time thresh- olds dynamically. By assuming a maximum walking speed of 4,320 me- ters per hour, they allocate an interchange time threshold as a function of the distance between the alighting and subsequent boarding locations, and add a five-minute buffer to prevent unreasonably short time thresh- olds when the two stops are extremely close together:

interchange distance Interchange time threshold = —4,320 + 5

63 In addition to using time thresholds to test for interchanges, Mc- Caig and Yip (2010) also apply a spatial condition. After classify- ing London’s bus routes into four general travel directions (north- east, southeast, southwest, and northwest), they calculate the elapsed times between pairs of Oyster cardholders’ bus board- ings and group the results into the categories shown in Table 4-1.5 Under the assumption that an interchange cannot take place if both trans- actions occurred on the same route, and testing the assumption that in- terchanges are not likely to include travel in the opposite direction, they show that the time distributions are consistent with these assertions (building upon the additional assumption that interchanges are likely to be shorter in duration than trip-generating activities). They then add a spatial condition to their proposed interchange-inference process by im- posing the rule that an interchange cannot consist of two transactions in opposite directions.

Table 4-1. Median elapsed times between Oyster cardholders’ consecutive bus boarding

Same Direction Opposite Direction Same Route 85 min. > 120 min. Different Route 30 min. 55 min.

4.3 Methodology

The interchange-inference algorithm developed in this thesis builds upon the research described in the previous section by applying a combination of spatial, temporal, and binary criteria to a complete set of AFC data col- lected from a multimodal transport network. Since these tests use tem-

5 Since McCaig and Yip measured the time between consecutive bus boarding transactions, the durations shown in the table include both the in-vehicle travel time of the earlier stage and the true inter-stage time (which includes any interchange, activity, or waiting time between the two stages).

64 poral and spatial data as proxies for passenger activity information, it is important to note that there are still certain trip-generating activities that could be mistaken for interchanges. Returning to the example in section 4.1, if a rider alighted a bus, quickly picked up a package at the post office, boarded another bus serv- ing a route perpendicular to the first, and finally alighted at her workplace, all of the methods discussed in section 4.2.2 would consider her to have made an interchange. Since the package was at that particular post office, there was a reason for the rider to visit that bus stop. Even if there were a direct bus from her origin (her home, for example) to her workplace, she would still have taken these two journey stages because she intended to visit that specific post office. In other words, the post office visit was a trip-generating activity and should not be considered an interchange.6 For this reason, each of the tests applied in this algorithm are used to determine whether a transition between two journey stages was not an interchange. Failing any one test will label the former transaction as not linked to the next, while any transactions that pass all tests can be considered likely interchanges. Since the previous example illustrates the possibility of producing false positives, the algorithm errs on the side of labeling transactions as not linked when interchange indicators are con- tradictory or ambiguous. The algorithm requires two inputs: a set of origin- and destination- inferred Oyster data (the output of the OD-inference process), and a match- ing set of iBus data. The interchange status of each Oyster record is then inferred by applying the following binary, temporal, and spatial tests, as listed in Table 4-2 and illustrated in Figure 4-1. If any one test fails, the transaction is considered “not linked” to the following transaction: all further tests are skipped for that record and testing resumes with the next transaction. If a test fails because of missing data, the interchange status is considered to be “non-inferable.” The record is considered unlinked

6 From an activity-based demand-modeling perspective, these two bus trips were part of the same tour, since they were not separated by a primary origin (e.g., home) or a primary desti- nation (e.g., work), but were nonetheless two distinct single-stage journeys which included the post office visit (a trip-generating activity) in the tour. While the journeys inferred in this work could be grouped into tours, tours are extraneous to the interchange-inference process itself and are not considered further in this chapter.

65 rather than linked, but the algorithm notes its status as non-inferable for reporting purposes.

Table 4-2. Tests applied during the interchange-inference process

Binary Conditions ·· Subsequent transaction ·· Successful bus destination inference ·· Complete rail exit information ·· Repeat transport service ·· Subsequent origin inference

Temporal Conditions ·· Interchange time ·· Maximum bus wait time ·· Observed bus headway

Spatial Conditions ·· Maximum interchange distance ·· Circuity ·· Full-journey length

4.3.1 Binary Conditions

Subsequent Transaction. The first condition tested is whether the transaction was the cardholder’s final stage of the day. If so, it is considered unlinked.

Successful Bus Destination Inference. If the transaction represents a bus stage, its alighting time and location are required in order to apply the spatial and temporal tests. If a destination was not inferred for the transaction, its interchange status cannot be inferred.

Complete Rail Exit Information. Similarly, if the transaction represents a rail stage, its station exit time and location must be recorded. If not, the trans- action cannot be inferred.

66 Is current record the final stage in its card's daily itinerary?

[no] [yes] Determine mode

[tram] [not recorded] Check inferred alighting Check exit time [not inferred] time and location [bus] [rail] and location

[recorded] [inferred] Is the next record in the Is the next record in the card's itinerary a bus card’s itinerary a rail stage, and stage, and is its route the is its origin station the same same as the current as the current stage’s stage's route? destination? If the next record is a bus stage, has its origin [no] been inferred? [yes] [yes] [no] [yes] [no]

Define "interchange start" as the current stage's destination and “interchange end” as Interchange the next stage's origin. Is the distance between the interchange start and interchange cannot be end greater than the Maximum Interchange Distance? inferred

[yes] [no] Calculate the maximum interchange time as a function of the Minimum Interchange Speed, the Minimum Transfer Time Allowance, and this interchange's distance. Is the time between the interchange start and interchange end greater than the maximum interchange time?

[yes] [no] What is the mode of the next stage?

[rail] [bus] Calculate the estimated passenger arrival time as a function of the interchange start time and the Minimum Walk Speed.

Is the time between the passenger arrival time and the interchange end time greater than the Maximum Bus Wait Time?

[yes] [no] Check iBus data to determine whether the customer boarded the first bus that arrived after the estimated passenger arrival time.

[boarded subsequent bus] [boarded first bus]

Calculate the circuity ratio as the total length [Future:] Do iBus-inferred bus-load of the prospective journey (current stage + data suggest that all buses that interchange distance + next stage) divided the customer skipped were full? by the distance between the first segment's origin and the next segment's destination. Is this ratio greater than the Maximum Circuity [no] [yes] Ratio?

[yes] [no]

Not linked to Linked to next next segment segment

Figure 4-1. Activity diagram of the interchange-inference process

67 Repeat Transport Service. If two successive stages of an Oyster card used the same bus route or rail station, the first is not linked to the next, regardless of the direction of travel. Successive trips on the same service in opposite directions indicate a return trip, while travel in the same direction (on the same service) still indicates that a trip-generating activity was performed (otherwise the passenger would not have alighted the vehicle). Since Oyster bus records denote the route, this test is applied by sim- ply comparing the route numbers of the current and subsequent transac- tions. For rail transactions, the station IDs of the Oyster records are com- pared. If they match, the current record is considered unlinked.

Subsequent origin inference. If the record following the current transaction represents a bus stage without an inferred boarding location, it cannot be compared to the current record during the temporal and spatial tests. If a boarding location was not inferred for the following bus stage, the cur- rent stage’s interchange status cannot be inferred.

4.3.2 Temporal Conditions

Interchange Time. Since it is assumed that trip-generating activities are likely to have longer durations than interchanges, a maximum interchange time is imposed. As with Chu and Chapleau (2008), this limit is calculated as a function of distance, but rather than adding a five minute buffer this al- gorithm sets a lower limit to the maximum interchange time, as follows:

d tint = max tmin, — (4.1) [ Rmin ] where

tint is the maximum interchange time,

tmin is the minimum interchange-time allowance, d is the interchange distance: the Euclidean distance between the current stage’s alighting or exit location and the following stage’s boarding or entry location,

Rmin is the user-specified minimum walk speed,

The minimum interchange-time allowance is a user-defined parameter that provides a small buffer when short interchange distances would oth-

68 erwise result in unreasonably short interchange-time thresholds. For ex- ample, if using a minimum walk speed of 3,000 meters per hour, two intersecting bus routes with a pair of stops 20 meters apart would oth- erwise allow only 24 seconds in which to transfer. The minimum inter- change-time allowance parameter allows for errors between Oyster and iBus timestamps, while providing a small buffer for non-trip-generating activities such as reloading an Oyster card. If the next record represents a rail stage, and if the time between the

current stage’s exit or alighting time (t1) and the following stage’s station-

entry time (t2) is greater than the maximum interchange time (tint), it is assumed that the cardholder was engaged in an activity and the current stage is considered not linked to the next. If the following record is a bus stage, however, this test is applied and its result temporarily recorded, but the current record’s interchange status is not affected, since the time between the two stages might include some amount of bus wait time. In such cases, the result of this test will be taken into account during the two remaining temporal tests.

Maximum Bus Wait Time. This test is only applied if the following record represents a bus stage and if the time between stages was greater than the

maximum interchange time (tint) in the previous test. If both conditions are met, it is assumed that the rider either waited at the bus stop or was engaged in a trip-generating activity before boarding.7 It is assumed that there is some upper limit to the amount of time that someone will spend waiting for a bus, regardless of the bus’s headway. The estimated time between the passenger’s arrival at the stop and her subsequent boarding is therefore calculated as

t2 − (t1 + tint), and if the result is greater than the user-specified maximum bus wait time parameter, the current stage is considered not linked to the next.8

7 It is also possible that the rider boarded the bus immediately upon arrival at the stop, but for consistency, riders are given the same time allowance to walk to a bus stop as they are to walk to a rail station.

8 tint is used in the maixmum-bus-wait-time test for the same reasons as in the interchange- time test: short interchange distances are guaranteed a minimum time allowance to account for the difference between Oyster and iBus times, or to allow brief, non-trip-generating activities.

69 Observed Bus Headway. This test is only applied if the current record passed the maximum interchange test. In other words, the following transaction represents a bus stage and the cardholder boarded the bus after the allotted maximum interchange time but before the maximum bus wait time. It might therefore be assumed that the customer waited at the stop before boarding the bus, but by searching the iBus data the program can determine whether another bus assigned to the same route served the stop while the customer was presumably waiting. If the rider did not board the first bus that served the stop during this pe- riod, it is assumed that he was engaged in an activity rather than wait- ing.9 The iBus data are searched to determine whether another vehicle served the route specified by the rider’s following trip during the interval

[t1 + tint, t2), and if so, the current stage is considered not linked to the next.

4.3.3 Spatial Conditions

Maximum Interchange Distance. Since the primary purpose of an inter- change is to transition from one vehicle to another (and to ultimately perform some activity at a subsequent destination), it follows that there should be some limit to the distance between the current stage’s alighting or exit location and the following stage’s boarding or entry location if the transition is to be considered an interchange. The Euclidean distance between the two points is compared to a user-specified maximum-inter- change distance, and if the cardholder’s interchange difference is greater, the current trip is considered not linked to the next.

Circuity. In addition to a maximum interchange distance, it is also assumed that a multi-stage journey will not entail an overly circuitous path. If the Euclidean distance traveled over two consecutive stages is sufficiently greater than the Euclidean distance between the current stage’s origin and the next stage’s destination, it is assumed that the disutility of the addi- tional travel would outweigh the utility of the interchange.

9 The rider might have skipped the first bus because the vehicle was full or because the driver did not stop (for example, if the driver knew that another bus was close behind). These criteria are being considered for a future version of the algorithm but in the present version it is assumed that, over the course of the entire day and network, it is more likely that a skipped bus indicates an activity.

70 The algorithm tests for excessive circuity by first calculating the

Euclidean distances traveled during the current stage (dcur), the fol-

lowing stage (dnxt), between the current stage’s destination and the fol-

lowing stage’s origin (dint), and directly between the current stage’s

origin and the following stage’s destination (ddir). The sum of the first three distances is considered the distance traveled, while the third is considered the direct distance. The ratio of these distances,

(dcur + dint + dnxt) / ddir, is compared to the user-specifiedcircuity ratio param- eter, and if the observed ratio is greater than the parameter, the current stage is considered not linked to the next. The benefit of this metric rather than an angular cost is that, by tak- ing distance into account rather than deviation, reverse travel is permitted but penalized. Figure 4-2 shows two potential interchange locations for the same origin and destination (assuming a zero interchange distance for simplicity). Both paths have the same cumulative Euclidean distance, but in the example to the right the destination is farther from the interchange location than it is from the origin: the rider spent the first stage traveling away from the second stage’s destination. The ratio of the distance traveled to the direct distance defines an el- lipse with the first stage’s origin and the second stage’s destination as its foci. These elliptical bounds are more tolerant of lateral travel than of X reverse travel, which is useful for defining interchanges. While a passen- ger might travel backwards for a short distance—for example, to take a slower but more frequent local service before transferring to a faster limited-stop service—itEx. is assumed that riders are not likely to travel as far O D backward as they are laterally.  

X

X

Ex. Ex.  O D   O D

Figure 4-2. Two potential interchange locations yielding equal cumulative Euclidean distances

X 71 Ex. 

O D

O D

. . . . . . . . . .

O D

. . . . . . . . . . X

Ex.

O D

X

Ex. 

O D

O D

. . . . . . . . . .

Figure 4-3. Interchange boundaries for various circuity ratios.

Full-Journey Length. While the circuity test helps to prevent round trips from being erroneously classified as single journeys, it only takes two suc- cessive journey stages into account. After all other tests have been applied, full journeys are tentatively defined by grouping stages according to their inferred link status. It is possible, however, that a set of journeys that in- cludes a return journey (which by definition ends sufficiently close to the journey’s starting point) passed the circuity test because no single transi- tion was excessively circuitous, but the cumulative angular change over two or more transitions resulted in a return journey. The journey-length test therefore “unlinks” all stages in the journey (by reclassifying each stage as not linked to the next) if the journey’s origin and its destination were closer than the user-definedminimum journey length parameter.10

10 Two-stage journeys can be unlinked in this way but, in the case of journeys with three or more stages (hence two or more interchanges), it is possible that only one of the tentative interchanges must be unlinked in order to satisfy the journey-length condition, but it is unclear which one. To infer interchanges as conservatively as possible, all of a journey’s links are broken in such cases.

72 4.4 Results and Sensitivity Analysis

The interchange-inference algorithm was incorporated into the same Java application that executes the origin- and destination-inference processes. The software was tested on the same ten-weekday period used in Chapter 3,11 and typically completed in less than 20 minutes for each day, which includes the time taken to execute the previous two processes. The ap- plication creates a copy of the Oyster data set enriched with the fields inferred by the three processes. This section discusses the sensitivity of the algorithm’s parameters and then presents the results of the ten-day analysis.

4.4.1 Sensitivity Analysis

The results of the interchange-inference process are influenced by the val- ues of the following six user-defined parameters, which allow the user to select a balance between the accuracy and completeness of the results:

·· Minimum walk speed ·· Minimum interchange time allowance ·· Maximum interchange distance ·· Maximum bus wait time ·· Circuity factor ·· Minimum linked-journey distance

This section explores the sensitivity of the results to these parameters by observing the distributions of the associated properties in the ten-day Oyster data set.

Minimum Walk Speed. Figure 3-9 (page 53) illustrates the distribution of out-of-system speeds recorded between consecutive journey stages, where the latter transaction represented a rail stage (in order to exclude bus wait time from the calculation). As discussed in section 3.3.3, the speed is calculated as a Euclidean distance and therefore does not take the cardholder’s path into account. The primary peak near 0 kilometers per

11 The 6th through 10th and 13th through 17th of June 2011.

73 hour likely represents activities while the secondary peak just below 5 kmph presumably indicates interchanges. Since the minimum walk speed parameter sets a floor below which stages will be considered not linked, it should be set low enough to retain people who may move slowly yet high enough to exclude shorter trip-generating activities. The tests run in this analysis used a value of three kilometers per hour, which appears to provide a reasonable tradeoff between the two criteria and excludes 96.4 percent of the distribution, most of which represents obvious activities. Decreasing the parameter to 1.9 kilometers per hour reduces the exclu- sion rate by one percent (to 95.4 percent), while raising it to 3.7 increases the rate by one percent (to exclude 97.4 percent).

Minimum Interchange Time Allowance. When potential interchanges are tested for excessive interchange time, this parameter provides a floor to the criterion. It should be set in a way that enables the test to exclude transitions of excessive duration while preventing the imposition of any unreasonably short time limit. Figure 4-4 shows the cumulative distributions of inter-stage time for four stage-transition categories (all four permutations of bus and rail stages, measured from the prior stage’s exit or inferred alighting time to the latter stage’s entry or boarding time). Since most rail-to-rail inter- changes occur behind the gate (and therefore within an Oyster journey stage), the distribution of rail-to-rail times is likely to contain mostly ac-

Cumulative Frequency %

%

%

%

%

Rail to Rail % Rail to Bus Bus to Rail Bus to Bus %            Time (hours) Figure 4-4. Time between cardholders’ successive journey stages, ten-day average.

74 tivities and few interchanges. The notable rise between 8 and 9.5 hours likely represents workdays, which supports this assumption. The three other distributions likely contain a mix of interchanges and activities. Since bus-to-bus transitions can include return stages on the same route, they are likely to contain a higher proportion of activities than the remaining two distributions (the bus-to-bus distribution also in- cludes bus wait times). The rail-to-bus distribution has a higher propor- tion of shorter transitions than the bus-to-bus distribution, likely due to the absence of return trips on the same service. The bus-to rail distribution, like rail-to-bus, is likely to contain a higher proportion of interchanges than the first two distributions. How- ever, bus-to-rail transitions exclude wait time, making this distribution a more appropriate indicator of interchange times. The 70 percent of bus-to-rail transitions that occur within five min- utes likely indicate interchanges, as many bus routes in London stop very close to rail stations. The tapering at approximately 12 minutes presum- ably indicates longer interchange distances, yet these can be accommo- dated by the program because an interchange time threshold is applied as a function of distance. Setting a minimum allowance of five minutes only affects those cases where the interchange occurs over a very short distance, but ensures that the 70 percent of transitions that occur within that time limit (and which presumably are mostly interchanges), are pro- tected against being erroneously considered not linked. Decreasing this parameter below five minutes can dramatically affect the inference rate of short-distance interchanges, while doubling the setting to ten minutes only changes the potential output from 70 to 75 percent.

75 Maximum Interchange Distance. Figure 3-10 (page 55) shows the distri- bution of distances between Oyster stages while Table 3-3 (page 55) reveals the sensitivity of the maximum destination-inference distance. The same distribution and sensitivity apply to the maximum interchange distance, and a value of 750 meters is therefore applied to this parameter as well.

Maximum Bus Wait Time. While Figure 4-4 shows the inter-stage times for all Oyster transactions, Figure 4-5 combines bus-to-bus and rail-to-bus times, after subtracting the estimated interchange time required for the rider to arrive at the stop (assuming tentatively that the transition might be an interchange). The dense but tapering cluster of times at the left of the distribution fits the expected pattern of passenger wait times resulting from the daily average of headways over the entire bus network, while the long tail at the right should represent activities.12 While a threshold of 30 minutes might seem a reasonable cutoff for discerning waiting times from activities, the observed headway test provides a more robust mea- sure of how long a customer might actually have waited. For this rea- son, a maximum bus wait time of 45 minutes was applied in this research, providing a backup in the presumably unlikely event of bus headways exceeding that duration (for example, during service disruptions). This conservative estimate excludes the 42 percent of the distribution that oc- curs to its right and which is very unlikely to contain a significant amount of interchange activity, while allowing the observed-headway test to act upon 58 percent of the distribution to its left. When set to 45 minutes, a change in the maximum bus wait-time parameter of plus or minus three minutes corresponds to a one percent change in the number of transac- tions eliminated by the maximum-bus-wait-time test.

12 The typical headways for London bus routes should result in a large number of customers waiting for only a few minutes, which is likely represented by the high frequency of short wait times in the histogram. Activities, by contrast, should be expected to exhibit a far greater range of durations—for example, an hour for a meal or more than eight hours for a work shift. Activities are therefore likely to be spread sparsely over the long tail of the distribution.

76 , Frequency , ,

, , , ,              Implied Waiting Time (minutes)

Figure 4-5. Histogram of elapsed times between candidate bus-stop arrival times and subse- quent bus boarding times.

77 Circuity Factor. Figure 4-6 shows the distribution of circuity ratios for all potential interchanges that passed all previous tests (all binary and tempo- ral tests, and the maximum interchange-distance test). More than half of the distribution lies below 1.07, and the 95th percentile has a factor of 1.8. The concentration of low circuity ratios is likely due in part to the spatial arrangement of National Rail terminals in Central London. These stations encircle the urban core, leading many commuters from outside the city center to travel most of their journey by National Rail before transferring to a TfL mode for the relatively short final stage of their trip. For example, Figure 4-7 shows that a customer traveling by National Rail (green) from Honor Oak Park to London Bridge, then transferring to the (gray) and arriving at Bond Street, would travel within the bounds of the 1.1 circuity factor shown on the map. Many National Rail stations are much farther from the core than Honor Oak Park, leading to even lower ratios. Despite the dense concentration of circuity ratios below 1.1, the algo- rithm was applied with a factor of 1.7. Figure 4-8 shows a shorter jour- ney, from London Fields National Rail station to Angel Underground station. If this journey were made by traveling north to and transferring to the (orange) at , it would require a circuity ratio of at least 1.5 (inner ellipse). If the rider instead traveled south and made the interchange at Liverpool Street sta- tion, a circuity ratio of 1.7 (outer ellipse) would be needed. Although few journeys have such high circuity factors, these journeys (and similarly cir- cuitous bus journeys) are feasible commutes and should not be excluded.

, Frequency ,

,

,

,

 . . . . . . . . . . . . . . . . Circuity Ratio Figure 4-6. Histogram of circuity ratios for potential interchanges (inter-stage transitions that passed all previous tests).

78 Southern London Midland London Midland First Capital Connect First Capital Connect First Capital Connect National Express East Anglia Milton Keynes Milton Keynes St. Albans Abbey St. Albans, Luton Airport, Welwyn Garden City, Hertford North Broxbourne, Hertford East, and Northampton Luton and Bedford Stevenage, Letchworth, and Stevenage Harlow, Bishops Stortford, How Wood Cambridge, Kings Lynn, Stansted Airport and Cambridge Huntingdon and M25 Peterborough Jn22 Chesham M25 Cuey Jn21 Jn21a Cheshunt

Bricket Wood A5 Epping Kings Langley Potters Bar Theobalds M1 R D S H I R E Grove O A1(M) Jn20 T F R A10 E A121 M11 H M25 M25 M25 Jn24 Crews Hill Waltham Southern London Midland London Midland First Capital Connect First Capital Connect Jn23 First Capital Connect Cross National Express East Anglia Milton Keynes Milton Keynes St. Albans, Luton Airport, Garston Radlett Hertford North Jn25 Chiltern Railways St. Albans Abbey Welwyn Garden City, Broxbourne, HertfordJn26 East, and Northampton Aylesbury Luton and Bedford A41 Stevenage, Letchworth, and Stevenage Harlow, Bishops Stortford, Jn27 How Wood Cambridge, Kings Lynn, Stansted Airport and Cambridge Theydon Bois Hadley A1005 A121 Huntingdon and Wrotham Wood Amersham M25 Peterborough Turkey Enfield Chalfont Park A111 A1 Street Lock Jn19 Jn22 A1010 A121 E & Latimer A1000 A1081 Chesham M25 Jn21 A404 Cuey A1055 S Lee ValleyCheshuntA112 Jn21a M25 Watford Gordon Hill Grove A411 North Country S Park Park Watford Trent Park A104 E M1 Country Brimsdown Junction Park E N F I E L X Bricket Wood Enfield D Epping M25 A5 Cassiobury Epping A41 A110 Enfield Town A110 Forest Park A411 High Cockfosters National Express East Anglia Chase A113 Southend, Chelmsford, Colchester, Kings Langley Watford A5 Potters Bar Aldenham Barnet Southbury Ipswich and Norwich Jn18 Watford Park Elstree & Borehamwood Oakwood Theobalds Loughton Debden M11 M1 A105 Chorleywood A404 Ponders High Street New A110 A10 Grove E Barnet Grange End H I R Park A110 E S A412 Bush Hill Croxley A411 R D A111 Park O A1(M) A411 Shenfield Jn20 F A411 R T A1010 A1069 Oakleigh Park R Winchmore A10 A1112 I E Bushey Elstree Hill A121 M11 H M25 M25 Open Space Moat Mount Chingford H A1 Totteridge & M25 1 Jn24 Open Space Crews Hill Waltham M A5109 Whetstone A109 Southgate A1010

Jn23 A1055 Jn17 A105 S Rickmansworth A1037 Cross Garston A413 Radlett Moor A111 Edmonton Jn25 Buckhurst

0 Green A112

Park 0 Hill Chiltern Railways M Carpenders 0 Jn26 A4140 T 1 Aylesbury E A A404 Park A409 Jn27 A41 A4125 N W A1009 A41 Palmers Silver A1009 Hainault Forest A R Arnos Grove Angel Country Park Brentwood Bentley Priory A410 A Woodside Park Green Street Roding Theydon Bois A406 Road A406 Chigwell A12 Open Space B A Valley M25 H Hadley A1 A1005 F A121 Stanmore Mill Hill New Bounds Weald Wrotham A1003 Grange Country

WoodBroadway 0 L Chiltern Railways Edgware Southgate Green EnfieldO M11 G West Finchley 1 Turkey Highams Hill Park Amersham High Wycombe, Aylesbury, A Hatch Park A111 Chalfont Banbury and Birmingham A410 White Hart T Park Woodford A A1 Mill Hill East Bowes Street Lock A1112 H 128 Jn19 End Lane A1010 R Epping Jn28 N Park A121 E & Latimer Canons Park A1000 Northumberland H Forest M25 Northwood I A5 A A404 A1081 Park E Hainault A1055 Headstone A4140 A104 A404 Beaconsfield A412 Wood Green A R S W S A112 K Watford A404 Lane Lee Valley A12 Northwood Burnt Oak M1 Gordon Hill Bruce V M25 A411 O Finchley A406 Fairlop Grove North Hills A105 Grove M Country E R A41 Central T C Alexandra A1080 S A404 Blackhorse South A406 Park A1 Palace Turnpike Lane A503 Park A12 E R Harrow & Colindale Road D A564 A504 Woodford U A504 Tottenham A104 A Harold Wood Seer Pinner A Wealdstone Queensbury Trent Park Walthamstow 1 G E Y Hale Wood 1 R E Watford Green I N Seven Central B 1

Barkingside 2 A4180 A504 RHornsey Street A1400 A118 B M1 H CountryEast Finchley A Brimsdown A1199 Hendon H Sisters St. James Street A12 A125 Junction Harringay A123 A409 A4006 A103 I E Walthamstow Central A1 F R I A598 N L E Green North Harrow Park D Snaresbrook Epping M25 X A4006 LanesEnfield South Queens Road Cassiobury Gerrards A404 Kingsbury A502 Harringay A12 Gidea N A127 A110 A110Tottenham I Forest Park Park CrossA41 Northwick Kenton Hendon EnfieldCrouch TownStamford Leyton A12 Newbury Park A411Park High Cockfosters D Romford National Express East Anglia Preston Brent Highgate Chase Hill Hill Midland Road Redbridge A118 A127 A5 0 Gants A113 Jn29 Southend, Chelmsford, Colchester, Watford Rayners 1 Denham A4140 G Road A1006 Wanstead A406 Aldenham Barnet Cross A503 A Hill A1201 A107 Emerson Ipswich and Norwich Golf Club Lane A123 Denham West Harrow- A41 Southbury A104 G M11 Jn18 Golders A103 Manor House Stoke A118 Debden Park Park Elstree & Borehamwood Harrow on-the-Hill A406 Oakwood Seven Loughton Watford A312 A40 A404 Leytonstone Hampstead A105 Newington Wanstead West Chorleywood Eastcote South Green Finsbury Park Kings A124 Upminster A404 A502 Heath Archway Ponders Leytonstone Park E Chadwell Upminster Horndon c2c High Street B New A10 Basildon, A4090 Kenton Goodmayes Heath Ruislip R A110 A105 Rectory Wembley ParkE A5 Tufnell ISL INGTO N High Road A124 Bridge Southend and A4005 N C AMDEN GrangeUpper Road Clapton Hackney End Shoeburyness Ruislip Manor South Barnet A407 Wanstead Ilford A1083 West Ruislip North T Gospel Park Marsh A110 A11 Harrow Cricklewood Hampstead ParkHolloway Arsenal Flats A1083 Hornchurch A124 E Sudbury Hill Wembley Oak Bush Hill A106 B A RKING A412 Leyton Manor A1112 Eastbrookend Heath A112 Wanstead Croxley A411 Neasden A111 A41 Hampstead Holloway H A C K NEY Elm Park South Harrow Wembley Dollis Park Park Country M25 M40 A411 Sudbury & Road Park A118 AND Northolt Park Stadium Hill Willesden Finchley Road Drayton Park Dalston Park Shenfield A40 Ickenham Ruislip West Kentish A123 A411 Harrow Road & Frognal Homerton Woodgrange Park A124 R Green Hampstead Town Canonbury Kingsland A1010 Forest Gate DAGEN HAM Dagenham Ruislip Sudbury Hill Oakleigh Park WinchmoreKentish Caledonian Stratford A1069 Wembley Kilburn International A124 East Gardens Belsize Park Town Highbury & Islington Maryland Dagenham I Jn16 Central Road A1112 A4020 Elstree Hill Heathway Bushey Finchley Chalk Farm West Camden Dalston Hackney Sudbury Road Junction A118 Becontree A312 Stonebridge Park Road Swiss Cottage Caledonian Essex Road Wick Barking Open Space Brondesbury 0 Moat Mount Town A106 Stratford East Ham Hillingdon Brondesbury Road & 1 Upney

A355 A Chingford A1 A4127 Park Camden A404 A407 A1200 Victoria H Northolt Totteridge & Kilburn Town Barnsbury A40 Open Space High Road Park Stratford 1 South High Street Upton Park Hornchurch M HarlesdenA109 Kensal Southgate Haggerston A1010 A5109 Whetstone Hampstead Mornington Uxbridge Alperton Queen’s A1205 Country Rise St. John’s Wood Crescent A125 Willesden King’s Cross A1055 Jn17 N A406 St. A105 Angel Park Greenford Park A5 Pudding A1037 Plaistow M25 Kilburn Cambridge A406 S A437 A413 Rickmansworth Junction Pancras Abbey Park International Edmonton Mill Lane Dagenham A117 A13 A1306 Moor O A312 RegentA111’s Hoxton Heath Bethnal West Road A112 Buckhurst Perivale Maida A124 Dock A4020 Vale Green Ham South 0 Park Green A1208 Mile A112 Kensal Green Euston King’s Cross End Bow Church Park 0 Hill D Greenford Great St. Pancras Old Street Bethnal Green 0 North Portland Ockendon M Carpenders Hanger Lane WESTM INSTER Baker Shoreditch A4140 1 Acton Warwick Avenue Street Street Bow Road N T Marylebone Euston Russell High E W G A40 A Farringdon Street EPark Royal Edgware Square Square Bromley- H Rainham A1306 A409 Stepney Green Devons A A404 Park G Moorgate Road Liverpool M A4125 Warren Goodge Street Road A1009 NN Wormwood Royal Oak Regent’s Whitechapel by-Bow W Star Lane PalmersStreet Tottenham Street A13 A1009 Hainault Forest I Silver A1020 N A41 West Scrubs Edgware Park Beckton A A412 A4020 LR North Arnos Grove Bond Court Road Holborn Aldgate Barbican A312 Road AngelA13 TOW E R CastleA Bar Park Ealing Acton East Acton Westbourne Bond Green East Langdon Park Country Park Brentwood Woodside ParkEaling Street Chancery StreetSt. Paul’s Bentley Priory A410 I A Park Bayswater Street Oxford Aldgate Roding E Broadway Lane Bank Canning Town A408 Paddington Circus H AMLETA406 S Ri A12 Ladbroke Leicester Covent City Fenchurch St. Road vChige well A13

7 Beckton r Burnham Grove A406 Marble Arch Thameslink Mansion Tower Limehouse Custom House Prince T Taplow B 2 West Square Garden Shadwell A Royal Park Gallions ha M25 Open Space L Drayton House Poplar All Saints Royal Valley m 1 Temple Hill es Latimer Lancaster Victoria for ExCeL Regent Reach 4 Acton A40 Albert H A1 Green Ealing White City Charing Westferry F A Road Queensway Gate Tower Blackwall East Cyprus Weald Stanmore A437 Mill Hill Main Line Wood Lane Cross Blackfriars New Notting Bounds Cannon Gateway A1203 West India Quay India L Acton Hyde Piccadilly Street Monument Grange A4 Hill Gate Green Park Embankment London A1003 Circus City Airport Country Broadway Central Shepherd’s Bush Holland Park 0 Canary Wharf L A1020 Chiltern Railways I Edgware Southgate KensingtonGreen Waterloo East A100 O West M11 A2016 Jn30

Market Park 1 London Bridge Crossrail Drayton Hanwell Common A4020 Westminster London BridgeWapping Heron Silvertown Hill

G Maidenhead Slough West Finchley Shepherd’s Gardens Highams King A13 Park A A200 A2016 High Wycombe, Aylesbury, Southall Quays A George V Hatch Bush A315 A1206 Pontoon

South Acton Southwark 1 A410 Hayes & Goldhawk High Street Hyde Park White Hart Dock A Knightsbridge Rotherhithe 2 Banbury and Birmingham South Kensington Kensington Corner Waterloo South NorthT Park Woodford First Great Western H Road 0 Thames Bowes A1112 H 128 S Harlington Mill Hill East Turnham (Olympia) Borough Quay 6 Reading, Oxford, L End Iver Ealing Acton St. James’s Lambeth North Greenwich Flood Epping Abbey Jn28 O Lane Canada Water R A102 Banbury, Worcester, M4 5 Green A4 Park Barrier U 0 Town Park Wood N and Bedwyn Langley A408 Northfields Gloucester Bermondsey Woolwich G 0 Sloane Crossharbour Road 3 Forest Earl’s Square Elephant & Castle Northumberland H H Canons Park A Chiswick Hammersmith Victoria Belvedere M25 Northwood Court Surrey Quays A206 Chaord I A5 A4127 Boston Manor Ravenscourt Barons Woolwich Jn31 A A404 Park Stamford Mudchute E Park Court South Park Island Gardens Woolwich Plumstead Hainault Hundred M4 Brook Kensington Westcombe Dockyard A2041 A4140 Kennington A215 A200 Arsenal A206 Headstone Jn15 K ENSINGTON K Park A104 Erith A412 M4 West South Bermondsey A Charlton R Purfleet Beaconsfield Gunnersbury WoodPimlico Green Kew West AND R 5 W Kensington s Cutty Sark S 2 4 A4 Brompton e A12 M A408 A c2c K A404 Lane Bridge A3220 m for Maritime Greenwich H M A3044 Burnt Oak M4 M1 C HELSEA ha A3 V Northwood T Vauxhall Bruce A102 A406 A209 Tilbury and O FinchleyBrentford Chiswick A3220 er W A2 Maze Hill v Battersea Fairlop Southend A306 Fulham Broa dway Ri Deptford A312 H ParkA105 Oval H M C E Hills Windsor A4 A219 A M M Grove Greenwich Grays M25 R WA41 Central Battersea T T Slade & Eton A4 Osterley A315 Alexandra Park A1080 Queens Road Greenwich Park A207 I C A404 A220

A A4 A Riverside Syon Lane E S M U A202 Peckham BlackhorseDeptford A205South A406 Green 2 Queenstown 2 Southeastern R A1 Parsons Green New Bridge A503 Y E 0 Palace 3 New i O Kew A316 Turnpike Lane A12 v I Ashford International and Kent R 6 e Hounslow T Road W D r Imperial Stockwell Peckham Cross A2 Datchet Harrow & Colindale Syon Gardens Barnes H DenmarkSO Cross Road A2 Woodford T A504 A A207 h A3044 L East A564 F N Rye Elverson Road a Wharf Windsor & Park Royal A504 Bridge U Loughborough Hill Gate E A U m Heathrow L D Tottenham N Welling Bexleyheath e Harold Wood Seer PinnerEton Central A Wealdstone Isleworth H A A3 St. Johns Walthamstow 1 s Queensbury Terminals Botanic Putney Bridge Junction Lewisham Barnehurst S Clapham Y Wood 1 Heathrow Clapham Junction E M Wandsworth G Brixton Nunhead R Gardens Hale E N 1 Green 1, 2, 3 Hounslow I North Central L Hounslow Barnes Seven Blackheath B A4180 R Terminal 5 A504 R Road Street A1400 Barkingside 2 West Richmond Mortlake Hornsey Brockley Key to lines A118 H A1199 O N Central A E Falconwood NorthEast Finchley B A30 A205 Putney Wandsworth Town Clapham High Street S Hendon H Sisters St. James Street Kidbrooke A A125 Brixton East A12 Sheen Harringay A20 R A2 2 X A123A207 Jn1a D Sunnymeads Clapham A215 2 A409 A4006Hatton A103 Dulwich Crofton Walthamstow 1 N Jn14 U Central A1 Common Clapham Common R LondonGreenhithe Underground lines I I A315 Hounslow A598 Green Hither North Harrow Cross East Putney A205 Park G Eltham E Dartford W D D Ladywell GreenQueens Road Snaresbrook N A4006 LLAanesMBETH NorthSouth Crayford Stone Swanscombe A A404 HeathKingsburyrow O St. Margarets A502 W A N DSWOR T H Harringay Herne Crossing A12 Northfleet A127 Gerrards A E Dulwich Honor L Lee Gidea Terminal 4 Clapham South Hill B Bakerloo N A244 Tottenham Southeastern H Oak Park E I Cross N Northwick Kenton H Hendon A214 Wandsworth A23 A12 Newbury Park Gravesend Park Crouch Stamford A2 and Chatham E Whitton W A205Leyton Central I D Park Feltham Twickenham Common Catford D Romford A Preston Brent A3HighgateSouthfields Hill Hill Catford Bridge I Midland RoadMottingham Redbridge A118 A127 M RICH M O N D Richmond Balham A205 0 A21 S A20 Gants Bexley Circle A218 Jn29 M25 Denham Rayners Wraysbury A4140 1 G Road Park Earlsfield West Dulwich A205 A1006 Wanstead A406 A312 Cross Figure 4-7. Honor Oak Park to BondA503 Street via LondonA Bridge: Circuity factor of 1.1 (baseH Hill A1201 Tulse A107 Forest New Emerson Golf Club Lane UPO N A41 A104 A A123 District A2 Denham West Harrow- A219 Streatham Hill Eltham Albany G Jn13 A30 Strawberry Hill A103 Manor House Hill Sidcup Ashford Golders map: TfL).Tooting Bec Hill Stoke M Park Seven A118 A2 Park Harrow on-the-Hill A406 THAMES Sydenham Bellingham Jn2 Hammersmith & City A312 A404 Leytonstone A40 Hampstead Wimbledon Park A217 NewingtonHill Grove Park Wanstead West Green A24 West Kings Eastcote South A313 A502 Finsbury Park A124 Upminster c2c Fulwell HeathWimbledon Archway A214 Norwood Park ChadwellJubilee Haydons Gipsy Sydenham Leytonstone E Upminster Horndon B A307 Common Basildon, A4090 Kenton A312 A3 Beckenham Hill Goodmayes Heath R A105 Ruislip Road Hill Rectory Lower Staines Wembley ParkE A5 ISTootingL INGTO N A214 Crystal High Road Metropolitan Bridge Southend and N Teddington C AMDENWimbledonTufnell Clapton HackneySydenham A124 Shoeburyness A4005 A308 Upper Broadway Streatham Palace Manor Egham A316 Road Ilford A1083 Ruislip South A407 A308 Penge Elmstead Wanstead T Park Colliers A11 A20 North Gospel A219 East Marsh Northern West Ruislip M25 Hampton Holloway Tooting Streatham A215 Woods A1083 Hornchurch A124 Harrow Cricklewood Hampstead Wood Arsenal A222Flats Kempton Park Wick Oak Common Kent A106 Sundridge A30 Sudbury Hill Wembley A311 Kingston Dundonald Road New Leyton Manor B A RKING

A238 Penge Park A225 Piccadilly A1112 Eastbrookend Heath House Ravensbourne A112 Wanstead Neasden Hampton Bushy Park A41 Hampstead Holloway HWestA C K NEBeckenhamY Elm Park Harrow Dollis Raynes Norbury Park M25 South Wembley Sunbury Norbiton Merton South Wimbledon Country M A308 Park Beckenham Road Park Farningham ANVictoriaD Sudbury & M3 Park Morden Road Road A118 40 Northolt Park Stadium Willesden Drayton Park Dalston HackneyAnerley Bromley North Road Hill Finchley Road A24 Park Kentish A23 A40 Ickenham Ruislip Upper A3050 West New Wimbledon MER TON Downs Woodgrange Park A123 Harrow Road A308 & Frognal Beckenham A124

Homerton A21 Chislehurst Waterloo & City Halliford Green Hampstead A2043 Malden Chase Town Kingsland Avenue Clock A222 DAGEN HAM Dagenham A298 Phipps Bridge Mitcham Canonbury Birkbeck Junction Shortlands Forest Gate Southeastern Ruislip Sudbury Hill Kentish Road House Stratford Chatham, Margate, Caledonian Eastfields Canterbury and Dover Wembley HamptonKilburn Belsize Park Morden Belgrave Walk International A124 25 DagenhamInterchanges East A240 South Merton Maryland St. Mary M Jn16 Gardens South West Trains A244 Hampton Court Town Road Highbury & Islington Hackney Bromley South Bickley Ascot, Bracknell, Central Berrylands Thornton Harrington Cray Swanley A4020 Court A307 Mitcham Heathway Reading and Weybridge West Mitcham Heath Dalston Road Central Elmers End Camden Town Finchley ChalkA3 Farm Camden Hackney Jn3 SudburyEssex Road Shepperton London Road Junction Mitcham Junction A118 Becontree A312 Stonebridge Park Road Motspur Morden South Common Essex Road Petts BarkingA20 National Rail lines Fieldss Swiss Cottage Caledonian Norwood Wick South Virginia Brondesbury Park 0 Wood Town Central Londone South West Trains Selhurst A214A106 Stratford East Ham Hillingdon Hampstead Waters m SurbitonBrondesburyK I N G STON Road & Beddington Lane 1 LondonJunction Arena Upney (cased in black. All routes are shown Kilburn M3 A1200 a Aldershot, Basingstoke, A23 A355 A217 A A41 A4127 h Park Camden Eden Park A5205 T Salisbury, Exeter, A1200 Fields Victoria M20 High A30 Mornington ConnectionsA404 A407 Thames Kilburn Barnsbury excluding limited services) Northolt r Southampton, St. Helier Town Therapia Lane A21 e UPON Woodside Stratford Road A40 v Bournemouth,Poole, Ditton High Road Crescent i South West Park High Street R Weymouth, Guildford,Kensal Blackhorse Lane West Upton Park Hornchurch St. John’s Harlesden Havant, Portsmouth THAMES CroydonHaggerston St. King’s Cross 0 Hampstead Wickham Chiltern Railways 1 (for Isle of Wight), Worcester Wood Mornington Ampere Way A1205

Angel Alperton A Country Uxbridge Pancras Chertsey Chertsey and StainesRise Queen’s Malden Wellesley Addiscombe Y A5 Crescent Willesden St. JohnPark’s Wood Road A125 M25 Cambridge Manor King’s Cross A232 N A4210 Angel International A406 A24 St. Angel Centrale East c2c Park Greenford A1208 Heath Park Kilburn A5 N Waddon Marsh PuddingHayes PlaistowE M20 M25 Hoxton Reeves Cambridge A406 A437 Maida Junction Esher A2043 Pancras T O Hackbridge Croydon Sandilands Orpington Regent’s Park Sutton Common S U T Corner Mill Lane Abbey Dagenham A3 International A232 L A13 First Capital Connect Vale Park A501 Tolworth Wandle Park Hoxton Lebanon Heath Bethnal Road A112 A117 A1306 O A312 Bethnal Hinchley Regent’s Church George West Perivale Maida Street Road A232 M Dock Wood A217 Street A124 A4020 HershamGreen Vale Carshalton A232 Green Ham A232 First Great Western Euston KingSouth’s Cross Park West A1208 N Mile O A41 Addlestone Bethnal Kensal Green A2043 Euston King’s Cross Bow Church M3 St. Pancras Old Street Walton-on- A244 Sutton Waddon A212 BethnalLloyd Green EndA2022 D A5205 Green A243 Great O R A233 Stoneleigh St. Pancras Old Street Park Heathrow Connect Greenford A1 Great Thames North South Baker Portland A232 D A21 Eynsford Ockendon Baker Hanger Lane WESTChessington NorthM INSTEA240 R Wallington Shoreditch B Portland Shoreditch A317 Acton Warwick Avenue Street Street Croydon Y Bow Road N Heathrow Express

Street A4200 Russell High 5 E Street A5201 Marylebone Euston O W Claygate A404 Warwick Russell High Street Street3 Coombe Addington Village Chelsfield G A40 Square Farringdon 2 H Rainham Euston R Gravel Marylebone A307 Edgware Square A1306 Square Park Royal Sutton A Lane Stepney Green Devons Bromley- A Avenue Carshalton C Hill A23 London Midland Farringdon G A217 Moorgate Square Road Liverpool Fieldway M Chessington Warren Goodge StreetBeeches Liverpool Road A40 Edgware N Whitechapel Wormwood Royal Oak Regent’s Whitechapel by-Bow Star Lane Ewell Street Warren Moorgate South Cheam Street Tottenham Street King Henry’s Drive A5201 I A1020 Goodge Street Weybridge West Scrubs West Edgware Park Beckton National Express East Anglia A412 N Regent’s Street NorthLiverpool Court Road Holborn Aldgate A4020 A4210 L Barbican A312 Park Chancery Road A2022A13 TOW E R Royal Oak Ealing Street A11 Westbourne Bond New Addington Edgware Tottenham CastleA Bar Park Acton East Acton A232 Sanderstead East Langdon Park M25 Southeastern I Lane Ealing Street Chancery St. Paul’s A21 T Road Court Road Barbican Aldgate A3 Park EPSOBayswaterM Oxford Aldgate Jn4 A319 E Broadway Lane A2022 Bank Canning Town A408 Bond East Paddington Belmont Circus Purley H AMLETS Knockholt Ri Ladbroke Ewell Leicester Covent City FenchurchOaks St. Southern ve A13

7 Beckton r Burnham Street Oxford Holborn A40 St. Paul’s Byfleet & Grove A N D East Marble Arch Thameslink Mansion Tower Limehouse Custom House Prince N T

A5 2 Garden Royal Gallions h Taplow M25 L Bayswater Drayton West New Haw Aldgate Square House Shadwell Poplar All Saints Royal Park am Circus 1 Temple Hill for ExCeL es Latimer EWELL Lancaster Victoria Regent Reach 4 Acton A40 A237 Albert E South West Trains M25 White City Westferry A4206 Paddington Covent Garden GreenCity Ealing Bank Fenchurch A240 Charing A Road Gate Blackwall East Leicester Street Wood Lane Queensway Cross Purley Tower Cyprus A437 Marble Arch A4 Thameslink West A245 Main A3Line Epsom Blackfriars Cannon India Shoreham Square ShaAdwctonell Oxshott Notting A2022 A2022 Reedham Gateway A1203 West India Quay K Interchanges L ByfleetMansion Tower Hyde Piccadilly Embankment Street MonumentRiddlesdown London A4 Lancaster Temple A307 Hill Gate BansteadGreen Park West House Hill Circus City Airport A40 Gate Charing Ealing Central Shepherd’s Bush Holland Park Smitham (Coulsdon Town) Canary Wharf A1020 Figure 4-8. London FieldsWaterloo to East Angel via Hackney A2022or LiverpoolA100 Street: Circuity factors of 1.5 West Jn30 A4202 A244 Kensington A2016 I Queensway Cross Tower A1203 Market A243 Park London Bridge Crossrail Drayton HanwellBlackfriars Common A4020 Westminster Wapping Heron Silvertown Crossrail Maidenhead Slough Cannon Gateway Shepherd’s Gardens A22 King A13 Notting A217 A200 A2016 Southall Piccadilly and 1.7 (base map: TfL). Quays A George V

Monument Bush A315 Woodmansterne A1206 Street South West Trains Pontoon under construction Hill Gate Embankment South Acton Epsom Southwark 1 Hayes & Circus A245 GuildfordGoldhawk High Street Hyde Park Dock A3 Kenley Hyde Park Green Park Knightsbridge Rotherhithe 2 Kensington South KAshteadensington Kensington Downs Corner Waterloo South North First Great Western H WaterlooS Road Coulsdon 0 Thames S Gardens Harlington Borough Quay 6 (Olympia) Reading, Oxford, L Iver East Acton Turnham St. James’s Lambeth North South Greenwich Flood Abbey Ealing London Bridge A240 London Overground O U Wapping Canada Water A102 5 Park Barrier Banbury, Worcester, M4 Westminster A100 Green A4 U 0 Town Wood under construction and Bedwyn Langley A408 NorthfieldsR Gloucester Bermondsey 0 2 4 Miles Woolwich G 0 A245 Sloane Upper 79Crossharbour Road 3 R A315 Hyde Park Southwark M25 A200 Jn9 Earl’s Tattenham Square Elephant & Castle Whyteleafe Warlingham Otford H Knightsbridge A Hammersmith Victoria Belvedere High Street Corner Waterloo E Chiswick Court Corner Chaord A4127 Rotherhithe Cobham & A24 Chipstead Surrey Quays A206 A3 Ravenscourt M25 Kensington A302 Woking Boston Manor Y Barons Woolwich Jn31 Park StamfoStoke D’Abernonrd South Mudchute 0 3 6 Kilometres Docklands Light Railway Borough Park Court Island Gardens Woolwich M26 Hundred St. James’s Lambeth North A201 Canada Water Plumstead Southeastern A2041 Kensington A23 Whyteleafe Westcombe Dockyard M4 Park Brook Maidstone and Kennington A215 A200 Arsenal A4 South Ashford International A206 Jn15 South A302 K ENSINGTON K Park Erith A3216 A2 Bermondsey

A3212 Purfleet Gloucester M4 Gunnersbury West Pimlico South Bermondsey A233 Charlton Dunton

Kensington Leatherhead West R 5 Earl’s A201 Tramlink Road A3036 Jn9 AND 4 Court A4 Kensington Brompton es Cutty Sark 2 M A408 A Woldingham c2c Victoria Bridge A3220 Tadworth m for Maritime Greenwich H M A3217 A3044 M4 Elephant & Castle C HELSEA ha Kingswood A3 Jn5 Bat & T Vauxhall A102 A209 Tilbury and r A22 Ball Brentford Chiswick A3220 ve Battersea W A2 Maze Hill Southend A306 Fulham Broa dway Ri Deptford A312 A217 A202 Engham H Oval C A4 A219 Park H Grays Windsor Sloane A M M Caterham Greenwich M25 M25 Juction South West Trains Battersea Square W Bookham Guildford T & Eton A4 Osterley A2 A207 I Slade South M23 Queens Road Greenwich Park National Rail Kennington A315 Park A220 A23

A A4 A E S M A205 London Connections Riverside main line connections Syon Lane Bermondsey U A202 Peckham Deptford Green

2 Queenstown 2 New Southeastern R Pimlico Parsons Green Jn8 Jn6 Bridge September 2011Y 0 i O Kew A316 3 Jn7 New v I M25 Ashford International and Kent e Hounslow T Road W 6 r Southern and Imperial Southern Cross Datchet H Stockwell SO Peckham A2 A2 Southeastern Gardens Barnes South West Trains M25 Denmark A22 Cross Sevenoaks T Guildford, Havant, Syon A First Capital Connect Redhill, Gatwick Airport, M25 Southern A207 Tonbridge, Hastings, h A3044 L East Dorking and F NWharf Gatwick Airport Brighton, Eastbourne Rye East Grinstead Elverson Road Ashford, Canterbury, Windsor & a Park Royal Bridge U Loughborough Hill Gate E m Heathrow L D N Welling Bexleyheath Eton Central e Isleworth H A A3 St. Johns s Botanic Junction Lewisham Barnehurst Terminals S Putney Bridge Clapham Heathrow Hounslow Gardens M Clapham Junction Wandsworth Brixton Nunhead E R Terminal 5 1, 2, 3 Hounslow Barnes North Blackheath L West Richmond Mortlake Road Brockley Key to lines O N Central North E Falconwood A30 A205 Putney Wandsworth Town Clapham High Street S Brixton East Kidbrooke A Sheen A20 R A2 2 X A207 Jn1a D Sunnymeads Clapham A215 2 N Jn14 Hatton U Dulwich Crofton 1 I A315 Hounslow Common Clapham Common Hither LondonGreenhithe Underground lines Cross East Putney A205 Park G Eltham E Dartford W D D Ladywell Green N L A MBETH North Crayford Stone Swanscombe A Heathrow O St. Margarets W A N DSWOR T H Herne Crossing Northfleet A E Dulwich Honor L Lee Terminal 4 Clapham South Hill B Bakerloo A244 Southeastern H Oak Park E N H A214 Wandsworth A23 Gravesend A2 and Chatham E Whitton W A205 Central I D Feltham Twickenham Common Catford A A3 Southfields Catford Bridge I Mottingham M RICH M O N D Richmond Balham A205 A21 S A20 Bexley Circle A218 Wraysbury Park West Dulwich A205 M25 A312 Earlsfield H Tulse Forest New UPO N A District A2 A219 Streatham Hill Eltham Albany Jn13 A30 Strawberry Hill Hill Sidcup Ashford Tooting Bec Hill M Park A2 THAMES Sydenham Bellingham Jn2 Hammersmith & City Wimbledon Park A217 Hill Grove Park A313 A24 West Fulwell Wimbledon A214 Norwood Jubilee Haydons Sydenham A307 Common Gipsy

A312 A3 Beckenham Hill Staines Road Tooting Hill Lower Teddington A214 Crystal Metropolitan A308 Wimbledon Broadway Streatham Sydenham A316 Palace Egham A308 Penge Elmstead A219 Colliers East A20 Northern M25 Hampton Wood Tooting Streatham A215 Woods Kempton Park Wick Common Kent Sundridge A222 A30 A311 Kingston Dundonald Road New

A238 Penge Park A225 Piccadilly House Beckenham Ravensbourne Hampton Bushy Park Raynes Norbury West Sunbury Norbiton Merton South Wimbledon A308 Park Morden Road Beckenham Road Farningham Victoria M3 Park Anerley Bromley North Road A24 Upper A3050 New Wimbledon MER TON A23

A308 Beckenham A21 Chislehurst Waterloo & City Halliford Malden Chase Avenue Clock A222 A2043 A298 Phipps Bridge Mitcham Birkbeck Junction Shortlands Southeastern Road House Chatham, Margate, Eastfields Canterbury and Dover Hampton Morden Belgrave Walk 25 Interchanges A240 South Merton St. Mary M South West Trains A244 Hampton Court Bromley South Bickley Ascot, Bracknell, Berrylands Thornton Harrington Cray Swanley Court A307 Mitcham Reading and Weybridge Road Elmers End Camden Town A3 Mitcham Heath Essex Road Shepperton London Junction Mitcham Jn3 Fields Motspur Morden South Common Petts A20 National Rail lines South Virginia s Park Norwood Wood Central Londone South West Trains Selhurst A214 Hampstead Waters m Surbiton K I N G STON Beddington Lane Junction Arena (cased in black. All routes are shown Kilburn M3 a Aldershot, Basingstoke, A23 A1200 A217 A41 A5205 h Eden Park M20 High A30 Mornington ConnectionsT Salisbury, Exeter, Thames excluding limited services) r Southampton, UPON St. Helier Therapia Lane A21 e Bournemouth,Poole, Ditton Woodside Road Crescent iv West R Weymouth, Guildford, Blackhorse Lane West St. John’s Havant, Portsmouth THAMES Croydon St. King’s Cross 0 Wickham Chiltern Railways Wood 1 (for Isle of Wight), Worcester Ampere Way

Angel A Pancras Chertsey Chertsey and Staines Malden Wellesley Addiscombe Y A5 Park Road M25 Cambridge Manor A232 A4210 International A24 Centrale East c2c A1208 Heath Waddon Marsh Hayes E M20 Hoxton Esher A2043 T O N Hackbridge Reeves Croydon Sandilands Orpington Maida Regent’s Sutton Common S U T Corner A3 Tolworth A232 L First Capital Connect Vale Park A501 Hinchley Wandle Park Church Lebanon Bethnal George Road A232 Wood Street Street M HershamGreen A217 Carshalton A232 A232 First Great Western Euston King’s Cross West N O A41 Addlestone Bethnal A2043 M3 St. Pancras Old Street Walton-on- A244 Sutton Waddon A212 Lloyd A2022 A5205 Green A243 Stoneleigh Park O R A233 Heathrow Connect Great A1 Thames South A232 D A21 Eynsford Baker Chessington North A240 Wallington B Portland Shoreditch A317 Croydon Y Street A4200 Heathrow Express Street A5201 Claygate 5 O A404 Warwick Russell High Street 3 Coombe Addington Village Chelsfield Euston 2 R Gravel Marylebone A307 Sutton A Lane Avenue Square Carshalton C Hill A23 London Midland Square Farringdon A217 Chessington Beeches Fieldway A40 Edgware Whitechapel Warren South Ewell Cheam Road Goodge Street A5201 Moorgate King Henry’s Drive Regent’s Street Weybridge Liverpool West National Express East Anglia Park A4210 A2022 Royal Oak Chancery Street A11 New Addington Edgware Tottenham A232 Sanderstead M25 Lane A21 T Southeastern Road Court Road Barbican Aldgate A3 EPSOM Jn4 Bond A319 East Belmont A2022 Purley Knockholt Ewell Oaks Southern Street Holborn A40 St. Paul’s Byfleet & A N D N A5 Oxford East Bayswater Aldgate M25 Circus New Haw EWELL A237 E South West Trains A4206 Paddington Covent Garden City M25 Bank Fenchurch A240 Leicester Street Purley Marble Arch A4 Thameslink West A245 A3 Epsom Shoreham Shadwell Oxshott A2022 A2022 Reedham K Interchanges Square ByfleetMansion Tower Riddlesdown Lancaster Temple House Hill A307 Banstead Smitham (Coulsdon Town) A40 Gate Charing A2022 A4202 A244 Queensway Cross Tower A1203 A243 Crossrail Blackfriars Cannon Gateway A22 Notting Piccadilly Monument A217 Woodmansterne under construction Hill Gate Embankment Street South West Trains Circus A245 Guildford Epsom Kensington Hyde Park Green Park A3 Ashtead Downs Kenley Gardens WaterlooS Coulsdon East South U London Bridge Wapping A240 London Overground Westminster R A100 under construction A245 Upper 0 2 4 Miles A315 Hyde Park Southwark R M25 A200 Knightsbridge Jn9 Tattenham Whyteleafe Warlingham Otford High Street Corner Waterloo E Corner Rotherhithe Cobham & A24 Chipstead Kensington A302 Y A3 M25 Woking Stoke D’Abernon 0 3 6 Kilometres Docklands Light Railway Borough M26 St. James’s Lambeth North A201 Canada Water Southeastern A23 Whyteleafe Park Maidstone and A4 South Ashford International South A302 A3216 A2 Bermondsey Gloucester A3212 A233 Dunton Kensington Leatherhead Earl’s A201 Green Tramlink Road A3036 Jn9 Court Woldingham A3217 Victoria Tadworth Elephant & Castle Kingswood Jn5 Bat &

A22 Ball

Sloane A202 Engham A217 Juction South West Trains Caterham M25 Square Bookham Guildford National Rail Kennington A2 South M23 main line connections Bermondsey A23 London Connections Pimlico Jn8 Jn7 Jn6 September 2011 M25 Southern and Southern Southeastern South West Trains M25 A22 Sevenoaks Guildford, Havant, First Capital Connect Redhill, Gatwick Airport, M25 Southern Tonbridge, Hastings, Dorking and Gatwick Airport Brighton, Eastbourne East Grinstead Ashford, Canterbury, Minimum Linked-Journey Distance. Figure 4-9 shows the distributions of Euclidean distances between the start and end points all potential full- journeys before applying the minimum journey-distance test. The small cluster at the left of the distribution presumably represents sets of two or more journeys that contain round trips, but which were not eliminated by the previous tests. The transition between the two clusters appears to oc- cur at 275 meters, but a more conservative limit of 400 was applied, since common experience suggests that distances any shorter are unlikely to be traveled on linked journeys.

, Frequency , 

,  , , , ,    , , , , , , , , , , , Distance (meters ) Figure 4-9. Histogram of potential full-journey distances.

4.4.2 Results

The interchange-inference process was performed on the ten-day Oys- ter sample using the parameters in the previous section, yielding the re- sults shown in Table 4-3. Since bus origin and destination information is required when inferring interchanges to, from, or between bus stages, the inference rates of the origin- and destination-inference process affect the interchange-inference rates of bus-to-bus, bus-to-rail, and rail-to-bus transactions. The number of Oyster cards recorded over the ten day period ranged from 3.0 to 3.1 million per day, containing 9.5 to 10.1 million daily jour- ney stages linked into 6.6 to 7.1 million daily journeys. The median jour- ney duration was 24.5 minutes, with the distribution shown in Figure 4-10.

80 Table 4-3. Interchange-inference results: ten-day average.

Result Count Percentage Link status cannot be inferred because:

Current stage is tram 38,776 0.39%

Current boarding location not inferred 80,707 0.82% Current boarding location inferred beyond 156,580 1.58% maximum error Next stage’s boarding location not inferred 55,620 0.56% Next stage’s boarding location inferred beyond 151,184 1.53% maximum error Bus destination not inferred 140,248 1.42% Bus destination inferred beyond maximum error 197,873 2.00% Next’s stage’s bus destination not inferred 90,409 0.91% Not linked to next because: Final stage of day 2,955,332 29.89% Same bus route 886,344 8.96% Same rail station 1,034,640 10.46% Beyond maximum interchange distance 276,022 2.79% Walk to rail stage slower than minimum walk 334,111 3.38% speed Maximum bus wait time exceeded 835,178 8.45% Did not board first observed bus 363,741 3.68% Too circuitous 109,395 1.11% Less than minimum journey length 2,481 0.03% Subtotal, interchange not inferred 911,397 9.22% Subtotal, not linked to next 6,797,244 68.75% Subtotal, assumed linked to next 2,139,578 21.73% Total stages 9,848,219

81 , Number of Full Journeys ,

, , , , ,             Minute s Figure 4-10. Histogram of inferred journey durations.

4.4.3 Comparison with the London Travel Demand Survey

Following Seaborn et al (2009), the results of the interchange-inference process were compared to the London Travel Demand Survey (LTDS) of the three-month period containing the ten-day Oyster study period. A comparison of the number of daily journeys per passenger is shown in Figure 4-11. Consistent with the findings by Seaborn et al, the survey indicates a significantly higher proportion of passengers with two jour- neys per day than the Oyster data. This might be due to sampling error (the LTDS sample is much smaller than the Oyster data set) or bias—for example, respondents might be more likely to report their journeys to and from work or school while underreporting recreational, unplanned, or infrequent travel. The difference between the two distributions might also indicate an underestimation of interchanges by the algorithm. Table 4-3 shows that 9.2 percent of journey stages could not be inferred, and these stages are treated as single-stage journeys (rather than discarding them and leaving gaps in cardholders’ daily travel histories). Furthermore, the decision to apply the interchange-inference criteria conservatively may have caused some number of multi-stage journeys to be erroneously classified as mul- tiple single-stage journeys, which might explain the larger number of journeys per day in Figure 4-11, and, conversely, the smaller number of stages per journey in Figure 4-12.

82 Percentage of Carhdolders % % % % % % % LTDS Inferred % % % % % % % % % % % % % % % %        Journeys per Day Figure 4-11. Full journeys per passenger per day.

Percentage of Carhdolders % % %

% % %

% % LTDS Inferred % %

% %

% % % % % % % % % % % %        Stages per Journey Figure 4-12. Stages per journey.

4.5 Summary

The algorithm presented in this chapter employs a range of temporal, spatial, and logical assumptions about public transport interchanges and applies a series of tests to infer whether each journey stage in a set of roughly 9.8 million is linked to its cardholder’s following stage. Stages of various public transport modes can then be joined to reconstruct the full journeys and daily travel histories of roughly 3 million daily customers. The tests for inferring interchanges were applied conservatively, and a comparison to the results of London’s principle travel survey suggests that the algorithm, when applied using the parameters described in this chap- ter, might be underreporting transfer activity. The interchange-inference

83 rate could also be increased by improving the inference rates of the origin- and destination-inference processes, which provide its inputs. Further studies of the algorithm’s outputs could aid in the tuning of its parameters. By mining multiple days worth of data, for example, ac- tivity types could be inferred, potentially revealing correlations between a trip’s purpose and its spatial and temporal attributes. Different values for the six interchange-inference parameters could also be applied to different zones of the city, different service types, or during different times of day. The implementation of the interchange-inference algorithm is dis- cussed in Chapter 6, and multiple examples of its application are pre- sented in Chapter 7.

84 Full-Journey Matrix Expansion 5

The methods described in previous chapters infer the details of passengers’ boarding, alighting, and interchange activity, which in turn yield infor- mation about passengers’ full journeys. Although these methods utilize complete populations of AFC data, some AFC journeys are not properly re- corded or cannot be inferred, while some riders pay their fares or provide proof of payment using other media. The large market share of London’s Oyster card and the high inference rates of the aforementioned methods result in a very large sample of full passenger journeys (approximately 70 percent of all TfL passenger activity is inferred), but analyses of aggregate travel behavior require that the sample values be scaled to estimate the complete population of passenger activity on the TfL network. This chapter describes a method for estimating expansion factors for full passenger journeys during one (or more) user-defined time periods, enabling the construction of a passenger flow matrix of travel activity across multiple public transport modes. The method is then validated by comparing the journeys’ constituent rail-stage flows to those estimated using a traditional single-mode OD matrix of the Oyster rail network.

5.1 Problem Definition

Flows of passengers, vehicles, or goods are often described using origin– destination (OD) matrices. By constructing a table in which each row rep- resenting an origin and each column a destination, the value in each cell can be used to indicate the flow between a distinct OD pair. This is useful

85 for describing passenger flows between any two points during a given time period, where the origins and destinations could be the entry and exit stations on a closed rail network,1 the boarding and alighting stops on a single bus route, or the geographic zones connected by a highway net- work. Full passenger journeys could be studied in a similar fashion—for example, by counting the number of passengers who start at a given bus stop and end at a given rail station, regardless of their intermediate trans- fer locations—but the inclusion of interchange locations in this work ne- cessitates a different analytic framework. Rather than estimating a scaling factor for each OD pair, this work seeks to estimate a scaling factor for each full-journey itinerary, which is defined in this context as a unique sequence offare-transaction nodes observed to have been visited by at least one passenger. The concept of transaction nodes and itineraries can be illustrated using the hypotheti- cal transit network shown in Figure 5-1 (referred to hereafter as the test network).

A

BUS ROUTE 1 BUS ROUTE 2 BUS ROUTE 3

B RA IL L INE C

Figure 5-1. The test transit network.

In the context of full-journey expansion, a transaction node is de- fined as a node on the network at which a rider can tap an Oyster card.

1 A network is considered closed if each passenger has one entry and one exit transaction but no interchange transactions. The London rail network can be considered closed because transfers are made behind the gate, yielding a single journey stage per system entry and exit.

86 Each transaction node is uniquely identified by either a station and move- ment (entry vs. exit) or a route and direction (inbound vs. outbound). For example, the test network consists of the following twelve transaction nodes (inbound is considered northbound in Figure 5-1):

Station A entry Station A exit Station B entry Station B exit Station C entry Station C exit Route 1 inbound Route 1 outbound Route 2 inbound Route 2 outbound Route 3 inbound Route 3 outbound

This simple network yields 38 possible combinations of transaction nodes that can be traversed on any passenger journey. For example, a common itinerary might be station A entry to station C exit, while a less common itinerary might be route 1 inbound to station A entry to station B exit to route 2 inbound. It follows that every bus journey stage relates to a single transaction node (identified by the route and direction), while each rail journey stage consists of two nodes (an entry station and an exit station). While this network of three routes and three stations yields a manage- able 38 potential itineraries, the Oyster network contains over 1,300 sta- tion codes and over 800 routes. A very large number of potential itinerar- ies can be defined, many entailing hundreds of interchanges and therefore being extremely unlikely to be used by any passengers. It is for this reason that the set of itineraries included in the analysis only includes those se- quences of transaction nodes that have been observed to have been taken by at least one passenger on the day being analyzed. The estimation of full intermodal passenger-journey flows therefore requires the estimation of an expansion factor for each full-journey itin- erary observed in the sample. The approach taken is conceptually related to some of the methods used to estimate traditional OD matrices, and is presented after a review of the work upon which it builds.

87 5.2 Previous Research

5.2.1 Iterative Proportional Fitting on Closed Networks

The problem of expanding, or scaling, full passenger journeys is in some ways similar to that of expanding OD flows on a single bus route or closed rail network. In these cases a sample of passenger flows can be obtained or inferred, and the flows can be scaled to match a set of control totals mea- sured at each start and end point (or in the case of full journeys, at each interchange point as well). The iterative proportional fitting (IPF) method (Deming and Stephan 1940) is often used to solve the OD matrix expansion problem for rail networks and bus routes (Ben Akiva et al 1985, Wilson et al 2008, McCord et al 2010), and has also been applied to the London Underground network (Gordillo 2006, Chan 2007). If a rail origin–destination matrix is viewed as a contingency table, with each row representing an origin and each column representing a des- tination, the IPF method attempts to estimate a scaling factor for each row (origin) and another for each column (destination) that satisfy the follow- ing system of equations:

To,d = to,d · αo · βd o,d (5.1)

∀ To,d = Co o (5.2) dΣ D ∈ ∀ To,d = Cd d (5.3) oΣ O ∈ ∀ where

To,d is the estimated total passenger flow from origin stationo to des- tination station d,

to,d is the observed sample passengers flow from origin stationo to destination station d,

αo is the estimated scaling factor for origin station o,

βd is the estimated scaling factor for destination station d,

Co is the control total for entries at origin station o,

Cd is the control total for exits at destination station d.

88 In other words, scaling factors must be chosen for each origin and each destination such that the sample OD flows (collectively called theseed ma- trix) are scaled to satisfy each station’s entry and exit control totals. In this way IPF yields a scaling factor for each OD pair, but each of these scaling factors is the product of two other scaling factors: the entry and

exit factors αo and βd. When applied to the scaling of OD matrices from Oyster data, this relationship necessitates the assumption that for each OD pair, the proportion of travel successfully recorded with Oyster is a function of the station of entry and the station of exit. This relationship also provides the mechanism by which the IPF method solves the system of equations. Figure 5-2 depicts the IPF problem as a contingency table, using the rail subsystem of the test network (stations A, B, and C) as an example. Each row corresponds to an origin station; each column, a destination sta-

tion; and each cell, an OD pair. Each sample flow (to,d) is multiplied by its

corresponding scaling factors (αo and βd), and the products are summed for

each row (∑o) and each column (∑d). Equations 5.1 through 5.3 show that scaling factors must be chosen such that each row sum equals its corre- sponding origin control total and each column sum equals its correspond- ing destination control total. A solution is obtained by first setting all scaling factors to 1, then re- peatedly solving two of the three equations, alternating between solv- ing for row factors (equations 5.1 and 5.2) and column factors (equations 5.1 and 5.3). In the example, this is achieved by setting all α to the cor-

βA βB βC

αA 0 tA,B tA,C ∑oA CoA

αB tB,A 0 tB,C ∑oB CoB

αC tC,A tC,B 0 ∑oC CoC

∑dA ∑dB ∑dC

CdA CdB CdC

Figure 5-2. IPF contingency table for the test network shown in Figure 5-1.

89 responding quotient Co / ∑o, and then setting all β to the corresponding

quotient Cd / ∑d. Since the total estimated flow for each OD pair, To,d, is a function of two scaling factors—one in the entry dimension and another in the exit dimension—the adjustment of factors in one dimension can satisfy that dimension’s control totals but typically upsets the balance in the other dimension. With successive iterations, however, the differences between the control totals and marginal totals (the row and column sums) often converge toward zero. Iterative proportional fitting, when convergent, yields the maximum-

likelihood estimate for each OD pair’s scaling factor (αoβd) (Halberman 1974). Ben Akiva et al (1985) compared IPF to other matrix-expansion methods, including constrained generalized least squares (Björck 1996), constrained maximum-likelihood, and an intervening opportunity model. They found IPF to be preferable to the other methods due to its “computational ease without loss of accuracy.” Ben Akiva (1987) also noted that IPF will converge to a unique solu- tion (a biproportional fit) if the seed matrix contains no zeros. If zeros do exist, either because of infeasible combinations of origins and destina- tions (structural zeros, such as those OD pairs in the example that start and end at the same station) or because the flow between a feasible OD pair was not included in the sample (sampling zeros), the estimated total flow for that OD pair will remain at zero. It follows that there will be no direct relationship between that OD pair’s origin and destination scaling factors (although they might influence each other indirectly through other OD pairs), and that successive cycles might not converge. Pukelsheim (2012) shows that in the case of non convergence, successive iterations tend to oscillate between two accumulation points, one of which is approached during the origin phase of the adjustment cycle; the other, during the destination phase.

5.2.2 Applications of IPF on London’s Public Transport Network

Gordillo (2006) used IPF to build an OD matrix for the London Under- ground, using Oyster data to construct the seed matrix and station gate- line data to derive the control totals. To correct for the bias of some non-gated stations being undercounted, a method was devised which supplements data from non-gated stations with data from manual counts and from the RODS travel survey.

90 Gordillo’s OD matrix describes the London Underground’s travel ac- tivity for a full weekday, but Chan (2007) builds upon this work by devis- ing a method for constructing a separate matrix for each of TfL’s six time periods, as listed in Table 5-1:2

Table 5-1. London Underground weekday time periods

Time Period Hours Early morning 5:30–6:59 AM AM peak 7:00–9:59 AM Midday 10:00 AM–3:59 PM PM peak 4:00–6:59 PM Evening 7:00–9:59 PM Late evening 10:00 PM–12:30 AM

If each time-period OD matrix were to contain only the passenger trips that started and ended during the specified time period, passenger trips that began during one time period and ended during another would be excluded from the set of matrices. Chan therefore assigns trips to time periods based on the time of the passenger’s entry tap, regardless of when the rider tapped out of the system.3 The sample (the seed matrix) will then include some portion of passenger trips that end during later time periods, and which therefore contribute to the later periods’ control to- tals rather than those of the entry time period. Chan’s solution is to adjust the stations’ exit control totals to incorporate some portion of the cur- rent period’s total and some portion of the subsequent period’s total. This exit-adjustment methodology can be generalized to the adjustment of rail-exit, rail-entry, or bus-boarding totals (all of which are required for

2 For a discussion of the homogeneity within time periods see Ji et al (2011). 3 Alternatively, trips could be assigned based on their end times, regardless of when the trip started. This second approach might be more useful when analyzing the AM peak, when commuters tend to choose various start times based on their desired (and often similar) ar- rival times. Conversely, the former approach might be more useful for the PM peak, when passengers tend to enter the system at somewhat similar times (such as after work) but whose exit times vary based on their travel times. For consistency, however it is conven- tional to choose one of the two approaches for all time periods.

91 the scaling of full-journey matrices4), spanning any number of arbitrarily sized time periods, as follows:

tn,p,s rn,p,s = — n N, p P, s P (5.4) Σ tn,p,i i P ∈ ∈ ∈ ∈ ∀

Ĉn,A = rn,p+s,s · Cn,p+s n N (5.5) pΣΣ s ∈ A ∈ P ∀ ∈ where N is the set of transaction nodes (such as rail entry or exit stations) on the network, P is the set of recording periods: time periods for which control- total data are collected for a given service day, A is the analysis period: the time range for which the matrix is be- ing constructed, which consists of a subset of the recording peri- ods contained in P, s is the span, or number of recording periods, between a given fare transaction and the first transaction of the corresponding journey,

tn,p,s is the number of passengers in the seed matrix who tapped at node n during recording period p, and whose journeys began s recording periods before p,

rn,p,s is the ratio (or proportion), of all passengers in the seed matrix who tapped at node n during recording period p, whose journeys began s recording periods earlier,

Cn,p is the unadjusted recording-period control total: the number of passengers counted at node n during recording period p,

Ĉn,A is the adjusted analysis-period control total: the estimated num- ber of passengers who passed through node n, regardless of re- cording period, whose journeys began during analysis period A.

4 Since Chan’s OD matrices are defined by the time period of the entry transaction, only the exit transaction count must be adjusted (the time period of the entry tap is by definition the time period of the passenger trip). But since any full journey can include multiple station entries, station exits, and bus boardings, all control totals must be adjustable to reflect the time period of the first transaction in the passenger’s journey (for example, a station entry might be part of an interchange that occurs in a later time period than the first transaction in that journey).

92 Equation 5.4 illustrates that the timespan proportions (rn,p,s) are derived en- tirely from the seed matrix. It is assumed that the timespan proportions in the sample are a reasonable proxy for those of the population, which are unknown. Under this assumption, equation 5.5 applies the proportions to the observed control totals, yielding the adjusted control totals. Figure 5-3 illustrates the control-total adjustment process, using the example of the exit counts at a London Underground station, with each recording period corresponding to an hour. The table at the left contains the timespan proportions, inferred from the Oyster sample. These are multiplied by the unadjusted hourly totals, distributing them among the

Adjusted Unadjusted Adjusted Analysis- Recording- Adjusted Total Recording- Period Analysis Recording Period for Period Control Period Period Timespan Proportions Totals Period and Span Total Total

(A) (p ) (r n,p,s ) (C n,p ) (r n,p,s · Cn,p ) (Ĉn, A) s = 0 s = 1 s = 2 s = 3 s = 0 s = 1 s = 2 s = 3 0 0.0% 0.0% 0.0% 0.0% 1 0.0% 0.0% 0.0% 0.0% 2 0.0% 0.0% 0.0% 0.0% 3 0.0% 0.0% 0.0% 0.0% 4 0.0% 0.0% 0.0% 0.0% 5 100.0% 0.0% 0.0% 0.0% 1 1 22 Aearly a.m. 221 6 66.7% 33.3% 0.0% 0.0% 59 39 20 199 7 64.7% 35.0% 0.3% 0.0% 444 287 156 1 623 Aa.m. peak 8 59.4% 40.1% 0.5% 0.0% 803 477 322 4 0 891 2168 9 47.7% 50.6% 1.7% 0.0% 803 383 407 14 0 654 10 48.7% 49.9% 1.5% 0.0% 535 260 267 8 0 392 11 60.8% 38.2% 1.0% 0.0% 337 205 129 3 0 315 12 62.1% 37.1% 0.4% 0.4% 298 111 1 1 336 Amidday 185 2293 13 63.0% 36.6% 0.0% 0.4% 405 148 0 2 412 255 14 58.9% 40.7% 0.4% 0.0% 381 155 1 0 408 224 15 55.0% 44.3% 0.3% 0.3% 405 179 1 1 376 223 16 62.2% 37.0% 0.9% 0.0% 404 149 3 0 388 251 Ap.m. peak 17 69.9% 29.2% 0.7% 0.2% 457 134 3 1 623 1798 18 59.1% 40.4% 0.4% 0.1% 742 319 300 3 1 787 19 51.9% 47.8% 0.3% 0.0% 729 379 348 2 0 612 Aeve. 20 55.3% 44.4% 0.0% 0.2% 523 289 232 0 1 468 1486 21 57.5% 42.2% 0.3% 0.0% 423 243 178 1 0 406 22 55.3% 44.7% 0.0% 0.0% 358 198 160 0 0 363

Alate eve. 23 46.6% 52.6% 0.8% 0.0% 313 146 165 3 0 256 655 24 24.7% 75.3% 0.0% 0.0% 146 36 110 0 0 36 25 0.0% 0.0% 0.0% 0.0% 0 0 0 26 0.0% 0.0% 0.0% 0.0% 0 0 27 0.0% 0.0% 0.0% 0.0% 0 Totals: 8566 4840 3669 49 7 8566

Figure 5-3. An example of the control-total adjustment process, using exit counts from a Lon- don Underground station.

93 four observed spans, as shown in the table at the right. The components of each span/period count are then summed diagonally to yield the ad- justed hourly totals, which are in turn aggregated by analysis time period. The diagonal summation in the figure is reflected in equation 5.5, where the hour dimension is incremented by p + s. Chan also adapted the IPF procedure to ensure that all scaling factors for each OD pair were no less than 1.0, since Oyster transactions are a subset of a station’s fare activity and therefore cannot be greater than the station’s control total. After adjusting the control totals, Chan constructs OD matrices by applying Gordillo’s method to each time period, substitut-

ing Ĉn,P for Cn in equations 5.2 and 5.3 and adding the constraints αo ≥ 1

and βd ≥ 1. (A more robust method for applying these constraints to IPF is presented in sections 5.4.1 and 5.5.1.)

5.3 Input Data

Like the IPF method, the full-journey expansion process requires a set of sample data and control totals, as follows:

Origin-, destination-, and interchange-inferred Oyster data. After origins, des- tinations, and interchanges (ODX) have been inferred for Oyster transac- tions using the methods described in chapters 3 and 4, the Oyster records can be used to construct a seed matrix of full passenger journeys, which can in turn be scaled to match a set of control totals. The ODX-inferred Oyster data only include those journey stages that were successfully pro- cessed by the ODX-inference tools: any data that were discarded during any of the three inference processes will not be represented in the seed data and must be accounted for by the scaling process. Additionally, any Oyster trips that were only partially recorded, or any travel that was made without an Oyster card, will not have been included in the seed matrix. Because the seed matrix is an approximately 75 percent sample of TfL travel, the probability of it containing a significant number of sampling zeros is very low.

Rail Transaction Totals. Transaction counts from electronic station gates, or gatelines, are available for most gated stations that accept Oyster cards.

94 Gateline counts are aggregated by date, hour, station, movement (either entry or exit), and payment type. These totals include transactions made with the gates’ Oyster readers as well as the magnetic-stripe readers used to process paper tickets. There are some stations at which transaction to- tals are expected to undercount the number of actual transactions, such as ungated stations at which passengers using valid travel passes are not required to tap. Additionally, at the time of this writing, counts for some stations (such as some of the new stations on the East London Line) are not yet available.

Bus Farebox Counts. Data from bus fareboxes, or electronic ticket machines (ETMs), include counts of each Oyster transaction, as well as of many non- Oyster transactions such as paper tickets and visually inspected passes. Bus operators are required to record the use of visually inspected passes by pressing a button on the ETM but adherence to this rule varies among drivers. Because of this variation, and because of the current difficulty in obtaining disaggregate ETM data (as described in section 3.2.1), bus control totals are obtained at the route level rather than at the stop level, with the route direction being recorded as well. While stop-level counts may be useful for route-level analyses, the use of route-level control totals provides a reasonable level of aggregation for the scaling of intermodal and system-wide travel activity: rider activity can be analyzed at the stop level, but all stops will contribute to the ETM total of their route and direc- tion. If stop-level data can be more easily obtained in the future, however, the scaling processes described in this chapter can still be applied: it will simply process a larger number of ODX combinations.

95 5.4 Methodology

5.4.1 Problem Definition Revisited

The problem of estimating expansion factors for full-journey itineraries is illustrated in Figure 5-4. The entry gates at Station A and the exit gates at Station B (from the test network in Figure 5-1) have different control

totals (ĈAo and ĈBd, estimated using the process generalized from Chan in section 5.2.2), and the flows through these nodes comprise various full-

journey itineraries. The observed flow of each itinerary (t1 through t37)

must be scaled by some factor (α1 through α37) in order to estimate the additional flow that was not observed or inferred from the Oyster data.

This additional flow, Ao∆ and ∆Bd, must be allocated to the itineraries that constitute each node’s flow. In this example, itinerary scaling factors must be chosen that satisfy the following two equations:

∆Ao = t1α1 + t2α2 + t3α3 + t4α4 + t5α5 + t17α17 + t18α18 + t19α19 + t20α20 + t21α21

∆Bd = t1α1 + t3α3 + t11 α11 + t12α12 + t17α17 + t1α19 + t32α32 + t33α33 + t36α36 + t37α37

There are multiple solutions to each node’s equation. For example, all of the unobserved flow entering Station A could be allocated to itinerary

1 by setting α1 to ∆Ao/t1 while setting all other scaling factors related to

the node (α1 through α5 and α17 through α3) to zero. The unobserved flow could similarly be allocated exclusively to any of the other itineraries that contribute to Station A's entry count. At the network level, however, the problem is constrained by the relationships between transaction nodes. Transaction nodes are related to each other through itineraries. In Fig- ure 5-4, for example, itineraries 1, 3, 17, and 19 are common to both nodes. Itinerary 1 starts at Station A and terminates at Station B, while itinerary 3 starts at Station A and then interchanges from Station B to Route 1 (see figures 5-1 and 5-2). Most of the itineraries that constitute Station A's entry flow do not pass through Station B's exit gates, but contribute to the totals of other transaction nodes not shown in the figure. Since each itinerary is given its own scaling factor, the factors for itineraries 1, 3, 17, and 19 contribute to the unobserved flow of both nodes in the illustration

(∆Ao and ∆Bd). This relationship is illustrated for itinerary 1, for which the

96 t1α1

∆Bd t1α1

t37 ∆Ao

t1α1 t36

t21 t33 t20 t t19 32 t t18 19 t t 17 17 ĈBd ĈAo t12

t11 t3

t2 t3

t1 t1

Station A entry Station B exit

Figure 5-4. Example of estimated passenger flow on the test network.

sample flow (t1) is shown in a darker shade and the estimated non-sample

flow (t1α1) is delimited by a dashed line. While the relationship between full-journey itineraries and transac- tion-node control totals constrains the problem, there is no guarantee that a solution to the problem will be unique. An ideal scaling method would therefore derive the most likely of all solutions to the problem, much like IPF attempts to find the most likely solution to the OD-matrix-expansion problem. The method proposed in this section therefore builds upon the IPF approach, but modifies it to reflect the relationship between transac- tion nodes and full-journey itineraries.

97 5.4.2 Problem Formulation and Solution

The relationship between full-journey itineraries and transaction-node control totals can be formulated as follows:

∆ n = Ĉn − ti · B[n, i] n N (5.6) Σi I ∈ ∀ ∈ ti · αi · B[ n, i] = Δn n N (5.7) iΣ I ∈ ∀ ∈ Ti = ti · (1 + αi) i I (5.8)

∈ where ∀ N is the set of all Oyster transaction nodes (bus routes inbound, bus routes outbound, stations of entry, and stations of exit), I is the set of all full-journey itineraries (unique sequences of Oys- ter transaction nodes inferred to have been taken by at least one cardholder),

∆n is the difference between the control total and the sample total at transaction node n,

Ĉn is the adjusted control total for transaction node n, as defined in equations 5.4 and 5.5 (it is assumed that all variables correspond to a single analysis period, A),

ti is the flow of cardholders inferred in the seed matrix to have taken itinerary i, B[n, i] is an incidence matrix—a binary matrix of ones and zeros— indicating whether node n is traversed by itinerary i,

αi is the amount by which the sample flow for itineraryi will be scaled,

Ti is the estimated total flow of passengers on itineraryi.

The purpose of equation 5.6 is to ensure that no itineraries are scaled downward. Chan (2007) achieved a similar goal by imposing the con-

straint αoβd ≥ 1 in her IPF methodology (see section 5.2.2), which in the

full-journey expansion problem would correspond to a constraint of αi ≥ 1. Applying such a constraint in an iteratively solved problem, however, can inhibit convergence during any one phase of the cycle by forbidding

98 the selection of some factors that would otherwise (temporarily) satisfy the control total, thereby propagating error to a subsequent phase.

By scaling the seed itinerary flows downward to the difference,n ∆ ,

rather than upward to the entire control total, Ĉn, the constraint αi ≥ 1 becomes unnecessary, since scaling factors now need only be greater than

or equal to zero (rather than one). The corresponding constraint αi ≥ 0 need not be applied because non-negativity is guaranteed by the defini-

tion of a scaling factor (αi) as the quotient of two non-negative numbers (the control total and the sample total). After scaling factors are calculated in this way, they are added back to the seed itinerary flow to derive the total itinerary flow, as shown in equation 5.8. Since the unobserved flows are unknown, the goal of the full-journey expansion process is to find the most uniform scaling factors that satisfy equation 5.7. If we assume that the best estimate is that all itineraries are scaled as evenly as possible, we can seek to minimize the variance among all scaling factors. This could be approached as a least-squares optimiza- tion problem, where the objective function—the scaling factor variance— is minimized, subject to the satisfaction of equation 5.7. But with over 4,000 transaction nodes in the Oyster network and roughly 800,000 dif- ferent itineraries observed on a typical weekday,5 the optimization prob- lem requires a matrix of approximately 3.2 billion elements (the product of the two values), making it too large to process on a typical computer. A somewhat similar problem is solved by IPF, at least in the case of traditional OD matrices. The convergence of errors between sample and control totals is the direct result of repeatedly adjusting each scaling fac- tor, which effectively distributes the potential error among all scaling fac- tors, leading in many cases to the attainment of a maximum-likelihood estimate (Halberman 1974). Since this meets the goals of achieving likely estimates while satisfying control totals, a similar approach is taken when solving the full-journey scaling problem. In each phase of the IPF cycle, a scaling factor is estimated for each node by dividing the desired flow (the control total) by the current flow (the sample flow scaled by the expansion factors of the previous iteration). A less direct approach must be taken for full journeys, since expansion factors exist only in the itinerary dimension and control totals exist only

5 800,000 itineraries are typically observed when aggregating itineraries to the bus-route level. When further disaggregated by bus stop, there are rougly 1.7 million daily itineraries.

99 in the transaction-node dimension (in IPF, factors and nodes exist in both the origin dimension and the destination dimension).

Returning to the example in Figure 5-4, a scaling factor, α1, must

be estimated for the observed itinerary flow t1. If all nodes on the net- work are ignored except node A, we might assume that the distribution of itineraries in the node’s unobserved flow is the same as the distribu-

tion of itineraries in the sample flow—that is, we would setα 1 equal to

∆Ao /(ĈAo − ∆Ao). But it is clear from the example that the proportion of a node’s sample flow assigned to an itinerary is not necessarily equal to the proportion of the unobserved flow assigned to that itinerary. In this

case, t1 constitutes a greater proportion of the sample flow through node

A than through node B, while t1α1 constitutes a greater proportion of the unobserved flow through node B than through node A. The approach taken is therefore to choose an itinerary scaling factor that is the average of the ratios of the itinerary's seed flow to the seed flows of its constituent nodes. This problem is solved iteratively, and can be formulated as:

Mn = ti · αi · B[n, i] n N (5.9) iΣ I ∈ ∀ ∈ Δ —n * nΣ i Mn αi = α i — i I (5.10) ∈ 1 Σn i ∀ ∈ ∈ where

Mn is the marginal transaction-node total: the non-Oyster flow through node n, as calculated using the currently selected scaling factor,

α*i is the itinerary scaling factor chosen during the previous iteration of the problem, and all other symbols are as defined earlier in this chapter.

Each Δn is calculated once, as shown in equation 5.6. All αi are then set to 1.0, and equations 5.9 and 5.10 are repeatedly solved until the marginal

totals converge upon the set of Δn. Section 5.2.1 showed that convergence is reached in IPF by alternating between the adjusting of entries and exits. The adjustment of any entry

100 scaling factor can directly affect any number of exit totals, but cannot affect any other entry totals, and vice versa. All entries (rows) can there- fore be adjusted simultaneously or in any order, and the process is then repeated for all exits (columns). Since the full-journey problem contains scaling factors only in the itinerary dimension and control totals only in the node dimension, the adjustment of any itinerary scaling factor can af- fect the choice of scaling factors for other itineraries. Because of this interaction between itinerary scaling factors, the order in which factors are estimated can affect the results. If the scaling factors in the example were calculated in order, itinerary 1 would be allocated without being constrained by any other itinerary’s scaling factor, then

itinerary 3 would be allocated to some portion of ΔA not already allocated to itinerary 1. Since there are approximately 4,000 transaction nodes on the Oyster network but more than 800,000 itineraries, it is possible that some number of control totals will be satisfied by the scaling of some subset of their constituent itineraries before all of their itineraries are pro- cessed. This bias is eliminated by calculating scaling factors as described in equations 5.9 and 5.10, but not applying the factors until the end of each iteration, after all itineraries’ scaling factors have been calculated.

5.5 Example: Test Network

The methodology described in the previous section was first imple- mented in a spreadsheet program and applied to the test network (Figure 5-1). Unlike a real transport network, the total itinerary flows (which are typically unknown) can be defined in advance, and the method’s outputs can be validated against them. A rail-only OD matrix is then derived from the full-journey matrix, and the results are tested against a traditional OD matrix generated using IPF.

5.5.1 Validation

The full-journey scaling algorithm was applied to the test network, as illustrated in Figure 5-5. The actual itinerary counts and Oyster penetra- tion rates (the share of each itinerary count paid for or validated using Oyster) are unknown in a real network, but were chosen in this example.

101 The actual itinerary flows are multiplied by the Oyster penetration rates to derive the itinerary seed flows, which along with the control totals are the independent variables in the algorithm. Below the actual and seed itinerary counts in the figure are the esti- mated itinerary scaling factors, which are the only adjustable parameters in the problem: the algorithm solves for these values. Beneath the scaling factors is the incidence matrix (B in equations 5.6 and 5.7), which defines the relationships between itineraries and transaction nodes by indicating which of the twelve transaction nodes constitute each of the 37 observed itineraries (there are 38 possible itineraries, but it is assumed that not all were used by riders during the analysis period). Comparing the full-journey scaling problem illustrated in Figure 5-5 to the IPF contingency table in Figure 5-2, it can be seen that IPF has con- trol totals and scaling factors in both dimensions (the origin, or vertical dimension, and the destination, or horizontal dimension). Since the full- journey scaling problem contains flows in two or more dimensions (an origin, a destination, and any number of intermediate interchange nodes),

Itinerary (i) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 Actual itinerary counts (T) 200 176 22 29 38 84 27 123 28 16 87 16 134 21 74 58 16 21 7 13 5 60 64 19 6 23 3 84 71 36 32 24 13 41 49 24 13 Oyster penetration rate 50% 68% 91% 86% 79% 95% 67% 73% 82% 75% 86% 94% 82% 95% 81% 86% 75% 90% 86% 69% 80% 83% 70% 84% 67% 70% 67% 83% 92% 97% 78% 75% 92% 73% 71% 67% 69% Itinerary seed counts (t) 100 120 20 25 30 80 18 90 23 12 75 15 110 20 60 50 12 19 6 9 4 50 45 16 4 16 2 70 65 35 25 18 12 30 35 16 9 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * Itinerary scaling factors (α) 0.68 0.46 0.59 0.39 0.43 0.26 0.29 0.26 0.22 0.26 0.36 0.31 0.16 0.19 0.07 0.39 0.59 0.41 0.52 0.39 0.35 0.26 0.08 0.26 0.29 0.26 0.22 0.08 0.28 0.14 0.17 0.30 0.27 0.19 0.21 0.34 0.30 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * Marginal Control Station A entry: 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 181 vs. 182 Station B entry: 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 67 vs. 68 Station C entry: 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 92 vs. 90 Station A exit: 0 0 0 0 0 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 1 1 0 0 75 vs. 76 Station B exit: 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 138 vs. 139 Station C exit: 0 1 0 1 1 0 0 1 1 1 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 127 vs. 125 Bus 1 inbound 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 27 vs. 26 Bus 1 outboond 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 41 vs. 41 Bus 2 inbound 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 23 vs. 23 Bus 2 outbound 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 29 vs. 28 Bus 3 inbound 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 1 1 1 1 0 0 0 0 40 vs. 40 Bus 3 outbound 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 1 59 vs. 59

Figure 5-5. Formulation and results of the full-journey scaling process, as applied to the test network.

102 all nodes are collapsed to a single dimension, with all three node types (station entries, station exits, and bus boardings) listed on the vertical axis. Since the itineraries correspond to the horizontal axis, scaling factors ex- ist only in the horizontal dimension and control totals exist only in the vertical. As with IPF, the scaling factors are initially set to zero. The prob- lem is then iteratively solved as described in section 5.4.2. The model was applied using various parameter settings in order to test its robustness. If the iterative averaging process scales the itineraries relatively evenly (within the constraints imposed by the control totals), the results should be more accurate when there is less variance between the itineraries’ penetration rates. Figure 5-6 illustrates the convergence of the marginal totals (the estimated unobserved flow on each node) towards the nodes’ control totals over 20 iterations of the algorithm. The first data set, which contained Oyster penetration rates with a standard deviation of 6 percent, converged to match all control totals within eight iterations. The second data set, which had a standard deviation of 20 percent, con-

Itinerary 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 Actual itinerary counts 200 176 22 29 38 84 27 123 28 16 87 16 134 21 74 58 16 21 7 13 5 60 64 19 6 23 3 84 71 36 32 24 13 41 49 24 13 Oyster penetration rate 50% 68% 91% 86% 79% 95% 67% 73% 82% 75% 86% 94% 82% 95% 81% 86% 75% 90% 86% 69% 80% 83% 70% 84% 67% 70% 67% 83% 92% 97% 78% 75% 92% 73% 71% 67% 69% Itinerary seed counts 100 120 20 25 30 80 18 90 23 12 75 15 110 20 60 50 12 19 6 9 4 50 45 16 4 16 2 70 65 35 25 18 12 30 35 16 9 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * Itinerary scaling factors 0.68 0.46 0.59 0.39 0.43 0.26 0.29 0.26 0.22 0.26 0.36 0.31 0.16 0.19 0.07 0.39 0.59 0.41 0.52 0.39 0.35 0.26 0.08 0.26 0.29 0.26 0.22 0.08 0.28 0.14 0.17 0.30 0.27 0.19 0.21 0.34 0.30 (M) (Ĉ) * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * Marginal Control Station A entry: 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 181 vs. 182 Station B entry: 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 67 vs. 68 Station C entry: 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 92 vs. 90 Station A exit: 0 0 0 0 0 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 1 1 0 0 75 vs. 76 Station B exit: 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 138 vs. 139 Station C exit: 0 1 0 1 1 0 0 1 1 1 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 127 vs. 125 Bus 1 inbound 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 27 vs. 26 Bus 1 outboond 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 41 vs. 41 Bus 2 inbound 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 23 vs. 23 Bus 2 outbound 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 29 vs. 28 Bus 3 inbound 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 1 1 1 1 0 0 0 0 40 vs. 40 Bus 3 outbound 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 1 59 vs. 59

103  Station A entry  Station A exit Station B entry  Station B exit Station C entry  Station C exit  Bus  inbound Bus  outbound - Bus  inbound Bus  outbound Oyster penetration rate standard deviation: 6% Error (marginal − control total) (marginal − control Error - Bus  inbound Bus  outbound - Standard deviation Sum of squares -                     Iteration 





-

- Error (marginal − control total) (marginal − control Error

- Oyster penetration rate standard deviation: 20%

-                     Iteration Figure 5-6. Convergence of scaled transaction node flows toward control totals.

200 Sample itinerary flow Estimated itinerary flow 150 Actual itinerary flow rmse = 3.6 Flow 100

50

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 Itinerary 200

150

rmse = 10.7 Flow 100

50

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 Itinerary Figure 5-7. Comparison of actual and estimated itinerary flows.

104 verged toward all control totals with the exception of two, which each had errors of one rider. Figure 5-7 compares the estimated itinerary flows to the actual flows (which are unknown in a real network) and to the sample flows (which are independent variables in the algorithm). On the data set with less vari- ability, 34 of the 37 estimated flows provided closer estimates of the actual flows than did the sample flows, and the root-mean-square error (RMSE) between the actual and estimated totals was 3.6. The model with greater deviation provided an equal or better estimate on 29 of the 37 itineraries, with an RMSE of 10.7.

5.5.2 Comparison to Iterative Proportional Fitting

While the full-journey matrix-expansion process estimates scaling factors for full-journey itineraries, these itineraries comprise individual journey stages that can be compared to traditional single-mode OD matrices. For example, the flow from Station A to Station B in the test network (see Figure 5-4) can be derived by adding the scaled flows of all itineraries that include that OD pair, or in this case:

Flow from A to B = t1(1 + α1) + t3(1 + α3) + t17(1 + α17) + t19(1 + α19)

By calculating flows for all OD pairs of the rail network in this way, a rail- only OD matrix can be derived from the full-journey matrix. The resultant rail OD matrix was compared to another rail OD matrix, generated using IPF on the same data. The IPF algorithm was implemented as described by Chan (2007) and Gordillo (2006), which were reviewed in section 5.2. The only modification to the algorithm was to scale the OD flows to the unallocated portion of the control totals rather than to the entire control totals, as described in the full-journey estimation process (section 5.4.2). The modified IPF algorithm is formulated as follows:

105 ∆ o = Co − to,d o (5.11) dΣ ∈ D ∀ ∆ d = Cd − to,d d (5.12) Σo ∈ O ∀ to,d · αo · βd = ∆ o o (5.13) dΣ ∈ D ∀ to,d · αo · βd = ∆ d d (5.14) oΣ ∈ O ∀ To,d = to,d · (1+ αo · βd) o, d (5.15) where ∀

∆ o is the difference between stationo ’s control-total entries and sam- pled entries,

∆ d is the difference between station d’s control-total exits and sam- pled exits, and all other notation is as defined for equations 5.1 through 5.5.

Since the full-journey matrix expansion algorithm is more constrained than the IPF method (both are constrained by the matching of node con- trol totals while full-journey expansion adheres to the additional con- straint of having to relate transaction nodes through itineraries), it should be expected that there is more variation between the OD scaling factors derived from the full-journey matrix than between the OD scaling factors calculated through IPF. The small number of OD pairs on the test network yields too small a sample to test this difference in variation (this is tested in section 5.6.2 using the London network), but the comparison of each OD pair’s scaling factors in Figure 5-8 shows that both approaches yield similar scaling factors, with a greater variation in Oyster penetration rates leading to greater variation between the results of the two algorithms.

106 120

IPF ODX 100 19.22385 19.75479 Oyster penetration rate standard deviation: 6% 38) .77615 38.24296 n g 49i .1803286 48.25459 a l

c S

28.86714 29.74901 od pair 54orune y .2609441 54.74289 J -

28l .70559 28.25061 u l F (

ow 40

Jackl ed: F

IPF ODX 20 34.28263 39.03449 116.7174 112.2341 50.830356 44.69517 0 20 40 60 80 100 120 Flow (Iterative Proportional Fitting)

54.16644 60.64803 10612.50 798 110.7095 58.42019 53.31606

100 Oyster penetration rate standard deviation: 20% ) n g

i 80 a l c S

orune y 60 J - l (F u l

ow 40 l F

20

0 0 20 40 60 80 100 120 Flow (Iterative Proportional Fitting)

Figure 5-8. Comparison of passenger flows for the test network

107 5.6 Application to the London Network

Following its validation on the test network in section 5.5, the algorithm was implemented in a Java application (to be described in Chapter 6) and applied to the London network using five consecutive weekdays of Oyster and control-total data (October 17th–21st, 2011). Before calculating the scaling factors, all control totals are automatically adjusted as described in section 5.2.2. Figure 5-9 shows the results of this adjustment for Victoria Underground station. The distribution of station exits is shifted to the left, since some station exits correspond to journeys that started during an earlier hour. For many stations the entry counts are not shifted as far as the exit counts because station entries that are part of the first (or only) stage in a journey define the journey’s start hour, thereby precluding any offset. Victoria Underground Station exhibits a relatively large shift in the distribution of entry counts because many of its customers transfer from its interconnected National Rail station in the mornings.6

8000

7000

6000 Raw entries Adjusted entries 5000 Raw exits ge rs n e 4000 Adjusted exits s s a P 3000

2000

1000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hour

Figure 5-9. Hourly control totals for Victoria Underground Station, Wednesday, October 19, 2011.

6 The land uses surrounding Victoria Station are primarily commercial, yet the station’s en- tries peak during the morning and its exits peak in the afternoon. While these peaks con- tain some number of nearby residents and interchanges from bus, the peaks are largely due to National Rail interchanges.

108 5.6.1 Results

After adjusting all entry and exit totals, the algorithm was applied to each of the five daily data sets until no itinerary scaling factors were adjusted by more than .001 percent (.00001) during a single iteration. For each of the five days, this condition was satisfied after approximately 450 iterations when generating a full-day OD matrix, and after approximately 2,500 it- erations for peak-period matrices (with each iteration completing in less than one second). All bus and rail control totals were matched to within the ranges shown in Table 5-2.

Table 5-2. The degree to which the full-journey matrix expansion process satisfied the control totals for all Oyster transaction nodes (five-day total), as measured by the ratio of each node's scaled total to its control total.

Transaction node type Min. Max. Bus boarding totals (by route and direction) 99.99999% 100.00001% Station entry totals 99.99850% 100.00468% Station exit totals 99.99537% 100.00153%

Unlike the test network, London’s actual itinerary flows are unknown and cannot be compared with the estimated flows. Figure 5-10 shows the average daily distribution of full-journey scaling factors, as well as the ratios of bus and rail transaction node control totals to their sample totals. The distribution is shown for the AM peak period and for the entire day. Since each full-journey scaling factor (in the top histogram) is calculated as an average of the scaling ratios of its constituent transaction nodes (the middle and bottom histogram), it should be expected that the top distri- bution bears some similarity to the other two. On average, the bus-route ratios are significantly higher than those for rail stations. This is partly due to riders who board buses without tapping an Oyster card, such as those who pay cash fares onboard or stu- dents whose passes are visually inspected by the operator (who can re- cord the boarding to the ETM by pressing a button). The primary reason for this discrepancy, however, is that approximately 24 percent of Oys- ter bus transactions are excluded from the seed matrix because they were discarded during the origin- or destination-inference process. While rail

109 70000

60000

50000

40000 Full-Journey Itineraries y n c equ e r F 30000

20000 Daily  peak 10000

0 100% 105% 110% 115% 120% 125% 130% 135% 140% 145% 150% 155% 160% 165% 170% 175% 180% 185% 190% 195% 200% Full-Journey Scaling Factor

300

250 Bus Routes

200

y n c

u e 150

e q Bus: daily r F Bus:  peak 100

50

0 100% 105% 110% 115% 120% 125% 130% 135% 140% 145% 150% 155% 160% 165% 170% 175% 180% 185% 190% Control Total÷ Seed Total

300

250 Rail Stations 200

y n c

u e 150 Rail: daily

Fr e q Rail:  peak 100

50

0 100% 105% 110% 115% 120% 125% 130% 135% 140% 145% 150% 155% 160% 165% 170% 175% 180% 185% 190% Control Total÷ Seed Total

Figure 5-10. Histograms of scaling factors

scaling factors account for incomplete or non-Oyster activity, bus scal- ing factors account for non-Oyster activity plus the 24 percent of Oyster activity that was discarded. The histograms also show that bus ratios are lower during the AM peak period than throughout the entire day, sug- gesting that bus commuters are more likely to use Oyster cards than bus riders in general. Although the distribution of gateline-to-Oyster ratios is significantly lower for rail stations, many of the stations with higher ratios have ex- tremely high passenger flows. For example, four Underground stations— Victoria, , Liverpool Street, and London Bridge—account for over ten percent of the total daily counts, and each station has a ratio higher than 1.4. Three of these stations are adjacent to National Rail fa- cilities, and the fourth, Oxford Circus, is a popular destination from the other three. The higher ratios at these stations are likely due to commut-

110 ers who live outside Central London and use non-Oyster point-to-point season tickets to travel from National Rail to an Underground station near their workplace. The large number of 100% scaling factors in the rail ratios is due to the fact that gateline data were not available for approximately 30 percent of rail station codes.7 Most of these stations, however, belong to modes that exhibit relatively low ridership in the seed matrix or that have only recently been added to the Oyster network. Control totals should be ob- tained for these stations in the future, or their totals could be estimated using other methods before being input to the program. At present, the stations lacking control totals have a relatively minor impact on the full-journey scaling process, as illustrated in Figure 5-11. The X and Y axes compare the sampled and scaled flows of each full-jour- ney itinerary, and the itineraries that are scaled to approximately 100% (forming a 45-degree line from the chart's origin) can be seen to have relatively small flows.

5,000

4,000

3,000

2,000 Scaled Flow (passengers perScaled day) (passengers Flow Full-journey itinerary 1,000

0 0 1,000 2,000 3,000 4,000 5,000

Sample Flow (passengers per day) Figure 5-11. Comparison of sample (or seed) flows to scaled flows, for all itineraries (five-day average).

7 Gateline data were obtained for almost all Underground stations, but were not available for several National Rail stations and some newer Overground stations (although these data are recorded).

111 5.6.2 Validation Against Iterative Proportional Fitting on the London Network

As was done for the test network, a single-stage rail-only OD matrix was derived from the full-journey matrix for the London network, and this OD matrix was compared to another OD matrix generated using IPF with the same Oyster and control-total data. Full-day and AM-peak OD matri- ces were generated for each of the five weekdays, and the five-day average of each OD pair was used for comparison. It was asserted in section 5.5.2 that the OD-pair scaling factors derived from the full-journey scaling process should vary more than those ob- tained from IPF, since the full-journey algorithm is subject to more con- straints. The distribution of OD-pair scaling factors from both methods are shown in Figure 5-12: the series is sorted by scaling factor, with each point on the X axis corresponding to a single OD pair. The distributions are similar, but the full-journey curve exhibits greater variation, as it lies below the IPF curve throughout the left side of the graph and lies above it throughout the right. Since the curves in Figure 5-12 are sorted by scaling factor, however, the OD pair at any point on one curve is unlikely to be the same OD pair that occupies the same X position on the other curve. Figure 5-13 compares both algorithms’ scaled flows for each OD pair. While there is a generally linear relationship between the OD flows cal- culated using the two methods—presumably because both use roughly similar techniques to scale the same sample data to the same control to- tals—the variations between the two is expected, since the full-journey approach scales and constrains the problem at the itinerary level. The dif- ference between the AM-peak and full-day scatter plots, however, is likely due to the smaller sample size of the shorter analysis period. While the

300%

280%

260%

240%

220% r Iterative Proportional Fitting Fact o 200%

ng Full-journey scaling i l a

c 180% S

160%

140%

120%

100%  Pair Figure 5-12. Distributions of OD-pair scaling factors on London's rail network.

112 500

450 AMAM PeakPeak Period

400

350

300

250

200

150 Scaled Passenger Flow (Full-JourneyScaling) Flow Scaled Passenger 100

od Pair 50

0 0 50 100 150 200 250 300 350 400 450 500

Scaled Passenger Flow (Iterative Poportional Fitting)

500

450 AllAll Day

400

350

300

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200

150 Scaled Passenger Flow (Full-JourneyScaling) Flow Scaled Passenger 100

od Pair 50

0 0 50 100 150 200 250 300 350 400 450 500

Scaled Passenger Flow (Iterative Poportional Fitting)

Figure 5-13. Comparison of full-journey scaling and IPF by rail OD pair.

113 program converged upon the control totals in both cases, the AM peak required roughly five times as many iterations to do so.

5.7 Summary

Although the full-journey matrix-expansion process has not yet been tested against passenger surveys or other sources of empirical full-journey information, its performance on a small test network with known values, its matching of control totals on London’s transport network, and the similarity between its constituent rail OD flows and those of a conven- tional OD matrix suggests that the algorithm is a reasonable method for estimating full-journey scaling factors. The algorithm might also be ap- plicable to similar problems, such as the estimation of highway flows by using survey data to construct a seed matrix and traffic counts to serve as control totals. While this method provides a novel solution to the problem of scaling full journey itineraries, transport agencies still perform many analyses at the level of the unlinked passenger trip. By applying the full-journey ex- pansion process to a transport network and storing the scaling factors or the scaled route- or OD-level flows in a central database, single- or multi- stage analyses can be performed throughout the agency in a way that en- sures compatibility between their outputs. The technical implementation of the full-journey expansion algo- rithm is discussed in Chapter 6, and multiple applications of the process are presented in Chapter 7.

114 Implementation 6

One of the primary advantages of using automatically collected data as a source of public-transport passenger information is the unprecedented size of the sample. AFC, AVL, and aggregate transaction-count data enable transport providers to observe the travel activity of most of their custom- ers on any given day, but this data is only useful if it can be processed efficiently. One of the goals of this thesis is to demonstrate the feasibility of processing complete sets of London’s Oyster and iBus data on a daily basis, and a significant portion of this research was devoted to the design of a software application that achieves this goal. This chapter describes the implementation of the origin-, destination, and interchange-inference processes, as well as that of the full-journey matrix-expansion algorithm. While exhaustive documentation of the software’s code is beyond the scope of this thesis, the goal of this chap- ter—supplemented by the methodologies presented in chapters 3 through 5—is to provide enough information to guide the development of similar implementations.1

6.1 Overview

The algorithms developed in this thesis were implemented using the Java programming language, chosen for its flexibility, cross-platform compat-

1 Users of the code developed for this thesis, or those seeking additional technical documen- tation, should contact the author or the MIT Transit Research Group.

115 ibility, and succinctness. Rather than using a database to execute these procedures, the program takes advantage of Java’s object-oriented fea- tures and stores collections of data objects in memory (and occasionally caching them to disk), providing more control over the process and the allocation of the system’s resources. The application, consisting of approximately 10,000 lines of code, was developed and tested on a consumer-grade PC to demonstrate that a specialized server is not needed to execute the application,2 and care was taken to minimize the memory footprint and tune the performance of the process. A high-level overview of the process and its inputs and out- puts is shown in Figure 6-1. The classes of the software package are shown in Figure 6-2. The ori- gin-, destination-, and interchange-inference processes are performed by the OysterTransactionProcessor and NearestStopCalculator classes, while full-journey matrix expansion is performed by the ODXMatrixGenerator class. Journey-stage data are represented by the

Oyster data file Origin-inference process

Origin-inferred iBus data file Oyster file

Destination-inference process Station data file

-inferred Oyster file Bus stop data file

Interchange-inference process

-inferred Oyster file Station gateline data file Full-journey matrix-expansion process Bus  data file Full-journey scaling factors

Figure 6-1. Overview of processes, inputs, and outputs.

2 Unless otherwise noted, tests were performed on a PC with a 2.8 GHz Intel i7 Core processor and 8 GB of RAM, running the Windows 7 operating system.

116 subclasses of both OysterTransaction and Stage: the former repre- sents a relatively complete copy of the Oyster input data while the latter contains a lightweight version having only those fields that are relevant to the linking of journey stages. The OysterTransactionProcesser dispatches searches for iBus data to an IBusManager, which contains a hierarchical data structure of routes, vehicle trips, and stop events, while a collection of OysterCard objects are used to store passengers’ daily travel histories and perform various sorts, searches, and enumerations on their constituent stages.

1 1 OysterTransactionProcessor NearestStopCalculator

1 1 1 1 * 1 OysterTransactionProcessor NearestStopCalculator 1 1 1 ODXMatrix1Generator 1 * 1 1 OysterTransactionProcessor NearestStopCalculator * 1 1 IBusManager ODX1MatrixGenerat*or Oyst1erCard 1 * 1 1 1 ODXMatrixGenerator IBusManager OysterCard 1 * * *

1 1 IBusManager OysterCard IBusRoute Stage * * 1 1 1 IBusRoute Stage * * * 1 IBusRoute Stage IBusTrip BusStage RailStage * 1 1 IBusTrip BusStage RailStage * * * 1 IBusTrip BusStage RailStage OysterTransaction IBusStopEvent Cardinality Inheritance * * 1 one superclass

IBusStopEvent OysterTransaction * * many subclass IBusStopEvent OysterTransaction OysterBusTransaction OysterRailTransaction OysterTramTransaction

OysterBusTransaction OysterRailTransaction OysterTramTransaction

OysterBusTransaction OysterRailTransaction OysterTramTransaction Figure 6-2. Class diagram of origin-, destination-, and interchange-inference package

117 6.2 Process

To maximize compatibility with the program’s various data sources and the systems which ultimately process its outputs, the application loads data from text files and generates output in a similar format (although the program could also be configured to interface directly with databases). The program can be divided into five general processing steps, which are described in the following subsections.

6.2.1 Step 1: Preprocessing and Nearest-Stop Calculation

The largest input to the program is the Oyster data set, which typically consists of roughly 16 million daily records, but which contains several fields that are not used by the program. Rather than storing these data in memory, only the relevant fields are retained and the full records (includ- ing the information inferred by the program) are temporarily cached to disk. All other data which must be matched with the Oyster records— such as spatial information and AVL data—are therefore loaded into mem- ory prior to the processing of Oyster information so that they can be looked up as each record is read. The first step in this process, as shown in Figure 6-3, is the loading of all bus-stop IDs and their spatial coordinates. iBus data, typically compris- ing approximately 5 million records per weekday, are then loaded into memory from a text file and are stored in the IBusManager data structure, with each record from the input file being stored in an IBusStopEvent and added to the appropriate parent IBusTrip and IBusRoute. Each vehicle-trip number is unique within a route and day, thus allowing Oys- ter records to be uniquely associated with a trip using three fields (day, route, and trip). While the iBus data are being loaded and organized, an additional data structure is built containing spatial information for each route pattern. The top level of this second data structure consists of route patterns, each uniquely identified by its route name and pattern number, separated by a period (for example, “24.4882” denotes route 24, pattern 4882). The sec- ond level, within each pattern, contains the codes of all bus stops in that pattern.

118 Bus stop Load bus stop data from file data file

iBus data file iBus Manager Load iBus data into iBus Manager

Nearest stop to stop Generate matrix of nearest stops to all other matrix stops

Station data file Load station data from file

Nearest stop to station Generate matrix of nearest stops to stations matrix

Oyster data file Load next Oyster record from file

[bus] [other] Infer boarding location [rail or tram] Oyster cards

Binary temp file Store stage data on related card and write full record to binary file

[more records in file] [all records read] Sort each card's journey stages, incorporate OSI and intermediate-validation information, discard unneeded records

Load next Oyster record from file

[bus] [rail or tram] Infer alighting time and location

[more records in binary file] Binary temp file Infer interchange status

[all records read]

Link stages into journeys

[more records in binary file]

-Inferred Look up journey information, write updated record to output file [all records read] Oyster file

Figure 6-3. Origin-, destination-, and interchange-inference implementation.

119 The pattern collection is loaded in parallel with the stop-event collec- tion: each stop event is denoted by its route and pattern, and redundant stop events are ignored (since many bus vehicle trips serve the same pat- tern). Finally, spatial coordinates are loaded for all bus stops and rail sta- tions, enabling the calculation of distances during this and other steps of the destination- and interchange-inference algorithms. The destination-inference process requires knowledge of the closest stop on a bus pattern to a cardholder’s subsequent bus boarding or station entry location. Rather than calculating the nearest stop for each of the approximately six million daily bus alightings (which requires a distance calculation for each stop in a route’s pattern), nearest stops are calculated once for each combination of route and subsequent location, and are stored for later reference. (The resultant “nearest-stop” matrices—one for subsequent bus stops and another for subsequent rail stations—are cached to disk and can be reused for multiple days’ analyses for as long as the network and route patterns remain unchanged).3 Station information is therefore loaded prior to the generation of the nearest-stop-to-station matrix, and is retained in memory for use by other steps of the destina- tion- and interchange-inference processes. Efficient processing and memory allocation are top priorities due to the large sets of data required. All four matrices are therefore imple- mented as two-dimensional primitive arrays, and the indices of the rows and columns are implemented as one-dimensional arrays sorted by their values to enable efficient access using the binary search algorithm (Knuth 1973).

6.2.2 Step 2: Origin Inference and Daily History Reconstruction

After iBus and spatial data are loaded into memory and initialized, the program makes its first of three iterations over the Oyster data set to in- fer bus boarding locations while storing compact versions of the Oyster records (stored as a subclass of Stage) to the appropriate OysterCard object. For memory efficiency, the Oyster file is read one record at a time, looking up each bus transaction’s boarding location in the iBus data struc- ture and writing it to a binary output file (from where it will be read and

3 Bus stop and pattern information are typically updated every two weeks in iBus, or sooner if changes are implemented.

120 processed during the following iteration).4 Tram boardings and station exits are written to the output file without being processed, since their origins are already known. Oyster record types that are not relevant to the program—such as top-ups, voids, unfinished station entries, and unstarted station exits— are ignored, along with duplicate transactions made with TfL staff cards. In many cases TfL staff use their Oyster cards to open station gates for ex- iting customers, often in response to equipment malfunctions, to correct erroneous transactions, or to expedite passenger egress. All of these exit transactions are assigned the entry time and location of the staff mem- ber’s previous entry tap and, moreover, the transactions are recorded to the staff member’s card rather than the rider’s. For these reasons, any exit transaction made on a staff card but not preceded by an entry transaction is discarded (these discarded records should be accounted for in the con- trol totals and therefore the scaling process). Special handling is required for out-of-station interchanges (OSIs, de- scribed below) and intermediate-validation transactions. Intermediate- validation and completed station-entry records are therefore temporarily stored, along with the record types copied to the binary output file, as Stage objects in the associated OysterCard’s collection. After all records in the Oyster input file have been processed, each OysterCard object sorts its child stage objects by transaction time to construct a daily travel history. Intermediate-validation and OSI informa- tion are then added to the appropriate journey stages before performing the second iteration over the Oyster data OSIs are designated pairs of stations at which cardholders can tap out of one station, quickly tap into the other, and pay a single fare for the combined journey. When OSIs are recorded, the start time and location of the stage before the interchange is written to its own exit record as well as that of the stage after the interchange (recall from section 2.2.1 that entry records contain entry data only, while exit records contain both entry and exit data in order to charge the correct fare). This is appropriate for the fare-collection application for which the Oyster system was designed: no fare is charged for the prior stage, while the latter stage denotes the origin, destination, and full fare of the combined journey. For the purpose of

4 The records are read from and written to memory buffers, to minimize the number of harware I/O transactions.

121 full-journey travel analysis, however, it is important to accurately repre- sent each physical stage in the cardholder’s journey. Therefore, the station entry and exit records immediately following the OSI are compared, and the start time and location are copied from the start record to the exit record so that both journey stages associated with the OSI contain their physical entry locations and times. The temporary Stage objects repre- senting station entries are then discarded. Following the handling of OSIs, the program incorporates intermedi- ate-validation information, recorded by a dedicated type of Oyster record which indicates the time and location of a cardholder transfer “behind the gate.”5 Since no other type of behind-the-gate transfers are recorded by the Oyster system, intermediate validations are not retained by the software as fare transactions. However, they provide useful information about the cardholder’s path and are therefore appended to two optional fields in the related Oyster journey-stage record.

6.2.3 Step 3: Destination and Interchange Inference

Only after bus origins have been inferred and travel histories constructed can interchanges or bus destinations be inferred. To ensure that all fields in the Oyster input file are present in the output, the temporary binary file written during the previous step is processed sequentially, and inter- changes and bus alightings are inferred by looking up the associated travel history in the collection of OysterCard objects. As in the first iteration, the updated Oyster records are written to a buffered, temporary binary file. Bus alighting times and locations are inferred using the algorithm dis- cussed in section 3.3.2. As each Oyster bus record is read, its correspond- ing journey stage object is retrieved from memory along with that of the following stage (specified by the OysterCard object), and the specified route, pattern, and target location data are used to retrieve the nearest stop and its distance by looking up its value in the nearest-node matrices. The inferred destination data are additionally recorded to the associated BusStage object, in preparation for the inference of interchanges.

5 Intermediate validators enable customers to tap their cards at designated interchange sta- tions to show that they transferred in a travel zone with a lower fare than that of another potential interchange location.

122 After each bus destination is inferred, or rail record is read, its inter- change status is inferred (tram journey stages can be linked to, and they can be used to infer preceding bus destinations, but their interchange sta- tus cannot be inferred because their own destinations are unknown6).In both the binary output file and the OysterCard data structure, alighting times and locations are recorded, as well as the times and distances to the cardholder’s next transaction and a flag indicating whether the stage was linked to the following one. If a bus stage’s destination or any stage’s in- terchange status cannot be inferred, these values are replaced with one of several error codes to indicate which test failed.

6.2.4 Step 4: Full-Journey Construction

The preceding step inferred whether each stage was linked to the next, but stages cannot be linked into full journeys until the interchange status of all of a card’s stages have been inferred.7 For this reason, journeys are constructed after the second Oyster iteration, and a third and final itera- tion is required to append this journey information to the full Oyster re- cords while writing them to the output file. After the second Oyster iteration, all OysterCard objects are in- structed to link their child Stage objects into journeys. By inspecting the interchange-status flag of each stage, each can be assigned a journey number, a stage number within the journey, and the number of stages in the journey (for example, journey 2, stage 2 of 3). These three fields enable the output data to be generated in a format similar to that of the input data (with each row representing a journey stage rather than a full journey), which in turn enables the full-journey matrix expansion soft- ware (or other analysis tools) to easily join records into journeys while retaining the details of each stage. After recording journey information to each Stage object, the second binary file is read sequentially, the journey information is retrieved from memory, and data from both are combined and written to the final Oys-

6 Tram transactions note the station where the fare was paid but riders do not tap their cards upon alighting. Tram destinations could be inferred using vehicle-location data, but this was not available. 7 Oyster data are not guaranteed to be stored in temporal order in the original input file or the subsequent binary files.

123 ter output file. The file is identical in format to the original Oyster input file, but contains the additional fields generated during the inference pro- cesses. The file can then be loaded into a database or other analysis tool, or can be used to construct intermodal full-journey matrices.

6.2.5 Step 5: Full-journey Matrix Expansion

The full-journey matrix expansion process was implemented in the ODXMatrixGenerator class, which can be called independently from the OysterTransactionProcessor, provided that the latter has been run and has generated the necessary Oyster output file. Following the methodology presented in Chapter 5, the application generates an inter- modal full-journey matrix for one or more time periods specified by the user, adjusts the network’s control totals, and estimates expansion factors to account for unobserved or uninferred passenger flows. The first step is the construction of the seed matrix, as illustrated in Figure 6-4. The ODX (origin, destination, and interchange)-inferred Oys- ter file is parsed and the origin and destination stop or station codes of each record are stored in a lightweight inner class representing a journey stage, with each stage being loaded to a similarly lightweight object rep- resenting a full journey. After Oyster data are loaded, all journey objects are inspected and a set of full-journey itinerary identifiers is constructed by concatenating the identifiers of the transaction nodes that comprise the journeys. Since expansion factors are calculated at the bus-route level, but full-journey matrices are to be constructed at the stop level, two sets of matrices and identifiers are generated. For example, the identi- fier _24.708/_24.796/755/540 would signify all customers who boarded bus route 24 at stop 708, alighted at stop 796, transferred to sta- tion 755, and ended their journeys at station 540.8 The identifier for the associated route-level itinerary would be _24/755/540. The route- and stop-level seed matrices are implemented as four-di- mensional integer arrays indexed by transaction node, recording period, span, and direction (inbound vs. outbound, or entry vs. exit).9 The first dimension of each array is then mapped to the itinerary identifiers, and all

8 Underscores denote bus transaction nodes, periods separate routes from stops, slashes sepa- rate transaction nodes, and tildes denote tram stations. 9 See section 5.2.2 for terminology

124 Load -inferred Oyster data from file ODX-inferred Oyster file

Generate seed matrix of full-journey itineraries

Calculate timespan proportions for stations and stops Full-journey seed matrix

Load station gateline counts Gateline file

Time span proportions Load bus ETM counts ETM file

Adjust control totals Control totals

Adjust scaling factors for all itineraries

[scaling factors not converged] [scaling factors converged]

Write scaled flow information to file Scaled full-journey matrix

Figure 6-4. Full-journey matrix-expansion implementation.

journeys are inspected again to look up the appropriate elements in both seed matrices and increment the appropriate count. The journey collec- tion is then discarded and the memory allocated to it is cleared. Once the seed matrices are constructed, timespan proportions are ini- tialized by constructing a four-dimensional array of floating-point vari- ables with the same indices as the route-level seed matrix. The route-level seed matrix is then analyzed as described in Chapter 5 to calculate the timespan-proportion values. Gateline and ETM data are then loaded from text files, in which they are aggregated by node, hour, and direction (inbound/outbound for bus, entry/exit for rail).10 Each input record is multiplied by the appropri- ate timespan proportion and is added to the corresponding element of a three-dimensional array, similarly indexed by node, hour, and direction.

10 TfL’s gateline and ETM data are provided as hourly counts, which relate to the recording periods in this implementation.

125 Finally, the scaling factors are estimated by repeatedly performing the calculations in equations 5.9 and 5.10. The application iterates over the entire adjustment process until the greatest change in any node’s scaling factor between two successive iterations is less than the user-definedcon - vergence threshold parameter, or until the number of iterations exceeds the maximum iterations parameter.

6.3 Results and Performance

The origin-, destination-, and interchange-inference processes, which are executed in a single function call, typically complete within 20 minutes on an eight-core, 2.8 gigahertz, eight-megabyte PC. TfL staff have run the process in under eight minutes on a 32-core 2.9 gigahertz server with 256 gigabytes of RAM. The full-journey matrix-expansion process, which includes the construction of seed matrices and the adjustment of control totals, typically completes in less than ten minutes on the MIT machine and three minutes on a TfL server. In addition to the text files containing the processed Oyster and full- journey matrix information, the application generates a series of reports that contain the performance statistics and inference rates displayed in tables 3-1, 3-2, 3-4, 4-3, and 5-2.

6.4 Summary

The performance of the software described in this chapter demonstrates the feasibility of applying the methodologies of this research on a daily basis. By automating the application to run after hours, when all daily AFC, AVL and transaction-count data have been transferred from their respective servers, transport providers could obtain continuous system-wide full- journey information. These data could be used to generate a variety of automated reports, and would enable ex post analyses of any portion of the network at any time.

126 Applications 7

The methods developed in this thesis can be applied to a range of analyses on multiple public transport modes at various spatial and temporal scales. TfL staff have used Oyster data to perform many analyses on the Under- ground, Overground, and National Rail networks (due to the require- ment that passengers tap their cards both upon entering and exiting the system), but the inference of origins and destinations on London’s buses allows TfL’s highest-ridership mode to be included in these analyses.1 In addition to observing travel on these multiple modes, the inference of interchanges enables the observation of passengers’ full journeys, both within and between modes. This chapter demonstrates some of the applications of the methods developed in this thesis, and describes its use by other researchers and TfL staff. Analyses that rely only on the outputs of the OD-inference process are presented first, followed by a summary of full-journey and longitudi- nal applications.

1 Bus ridership is higher than other public modes in terms of the number of Oyster journey stages per day

127 7.1 Bus Applications

7.1.1 Boarding/Alighting/Flow Profiles and Route-Level OD Matrices

By extending the Oyster data set to include bus boardings and alightings, the origin- and destination-inference process enables a number of analy- ses independently of the interchange-inference and full-journey expan- sion methods. Since passengers must tap their Oyster cards when board- ing each bus, analyses can be conducted at the vehicle-trip level or can be aggregated over many trips at the route level. Enriched Oyster bus data can be used to generate passenger board- ing, alighting, and flow profiles, as demonstrated in Figure 7-1 (showing route 488 inbound, 7–10 AM). After loading the updated Oyster data into a database, records can be aggregated by boarding and alighting locations, with the cumulative difference between the two counts (boardings minus alightings) revealing the passenger flow. The figure illustrates the total flow on the route for five consecutive weekdays (9–13 May 2011) during the AM peak, but individual vehicle loads can be calculated by simply ag- gregating the data by vehicle trip. Doing so could be useful for observing the effects of crowding on rider behavior, dwell time, or service reliability. In addition to aggregating ridership information by boardings and alightings as in Figure 7-1, the data can be more finely aggregated by OD pair. Figure 7-2 shows an OD matrix for the same route, direction, and time period as shown in the graph, enabling a better understanding of the relationships between the route’s boarding and alighting counts.

128 1500 Passengers Boardings Alightings Flow 1200

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Bow Church Clapton Pond Brooksby’s Walk Hackney HospitalHomerton Grove Bow Fairfield Road Bromley High Street ld Ford Ruston Street Old Ford Parnell Road Bromley-by-Bow Tesco ld Ford Hand &O Flower Bromley-by-Bow Station O Jodrell Road / Wick Lane Urswick Road / Sutton Place Bromley-by-Bow Station (SB) Chapman Road / Wick Road Rothbury RoadKenworthy / Wallis Road Road / Wick Road Priory Tavern (NB) / Tesco (SB) Fairfield Road / Tredegar Road Wansbeck Road / Monier Road Kenninghall Road / Clapton DownsWay Road / Rendlesham Road Homerton High Street / Link Street Homerton High Street / Digby Road Lower Clapton Road / Downs Road Kenworthy Road / Hackney Hospital Lower ClaptonLower Clapton Road / CoultonRoad / Linscott Road Road

Figure 7-1. Boarding/alighting/flow profile for Route 488 inbound, 7–10A M



Boa rdings Aligh tings  ld Ford Hand & Flower ld Ford Ruston Street Flow Bromley-by-Bow Tesco Bromley-by-Bow StationBromley-by-Bow (SB) Station Priory Tav. (NB) / Tesco Bromley(SB) High Street Bow Church Bow Fairfield Road Fairfield Road / Tredegar RoadO Old Ford Parnell Road O Jodrell Road / Wick LaneWansbeck Road / MonierRothbury Road Road / Wallis RoadChapman Road / Wick RoadKenworthy Road / Wick RoadKenworthy Road / HackneyHackney Hospital Hospital Brooksby’s Walk Homerton Grove Homerton High Street / DigbyHomerton Road High Street / LinkUrswick Street Road / Sutton PlaceLower Clapton Road / CoultonLower RoadClapton Road / LinscottLower RoadClapton Road / Downs Road

Bromley-by-Bow Tesco – 0 17 0 7 24 11 4 7 15 7 7 2 2 9 4 0 4 2 2 0 0 2 4 0 0 130 Bromley-by-Bow Station (SB) – – 0 0 2 0 0 2 0 0 0 0 0 0 2 0 0 0 0 2 7 0 0 0 0 0 15 Bromley-by-Bow Station – – – 2 9 30 28 2 15 35 7 9 56 9 11 11 37 13 4 46 35 0 7 7 9 4 384 Priory Tav. (NB) / Tesco (SB) – – – – 2 20 2 0 0 4 2 2 2 0 2 0 2 4 0 2 4 0 0 2 0 4 56 Bromley High Street – – – – – 4 4 0 0 11 2 7 0 2 0 0 2 2 0 9 0 2 0 0 0 0 46 Bow Church – – – – – – 7 7 17 30 4 0 7 7 2 4 4 17 2 2 9 7 9 4 2 4 146 Bow Fairfield Road – – – – – – – 7 13 48 9 7 15 15 9 13 28 26 2 9 11 2 0 0 2 9 224 Fairfield Road / Tredegar Road – – – – – – – – 4 0 0 0 0 11 0 0 0 2 0 4 4 0 2 2 0 0 30 Old Ford Hand & Flower – – – – – – – – – 0 0 2 0 9 7 2 2 20 0 11 13 9 0 2 2 11 89 Old Ford Parnell Road – – – – – – – – – – 0 4 0 2 7 4 0 4 2 4 2 0 2 0 0 4 37 Old Ford Ruston Street – – – – – – – – – – – 0 0 17 2 0 0 4 0 2 0 0 4 2 0 7 39 Jodrell Road / Wick Lane – – – – – – – – – – – – 0 11 2 0 0 0 0 0 4 0 0 2 0 0 20 Wansbeck Road / Monier Road – – – – – – – – – – – – – 7 0 0 0 0 2 0 0 2 0 2 0 4 17 Rothbury Road / Wallis Road – – – – – – – – – – – – – – 0 0 0 0 0 0 0 2 2 0 0 2 7 Chapman Road / Wick Road – – – – – – – – – – – – – – – 7 9 9 2 13 15 9 11 15 24 22 135 Kenworthy Road / Wick Road – – – – – – – – – – – – – – – – 9 43 7 13 15 13 35 17 28 26 206 Kenworthy Road / Hackney Hospital – – – – – – – – – – – – – – – – – 7 0 2 0 4 4 4 17 13 52 Hackney Hospital – – – – – – – – – – – – – – – – – – 0 4 15 13 15 35 20 22 124 Brooksby’s Walk – – – – – – – – – – – – – – – – – – – 2 0 4 15 7 11 4 43 Homerton Grove – – – – – – – – – – – – – – – – – – – – 4 7 13 15 7 15 61 Homerton High Street / Digby Road – – – – – – – – – – – – – – – – – – – – – 17 35 24 13 24 113 Homerton High Street / Link Street – – – – – – – – – – – – – – – – – – – – – – 2 0 4 9 15 Urswick Road / Sutton Place – – – – – – – – – – – – – – – – – – – – – – – 0 0 7 7 Lower Clapton Road / Coulton Road – – – – – – – – – – – – – – – – – – – – – – – – 9 2 11 Lower Clapton Road / Linscott Road – – – – – – – – – – – – – – – – – – – – – – – – – 2 2 Lower Clapton Road / Downs Road – – – – – – – – – – – – – – – – – – – – – – – – – – 0 0 0 17 2 20 78 52 22 56 143 30 37 83 91 52 46 93 156 24 128 139 91 159 146 148 195 2009

Figure 7-2.Scale t o2 :0Origin–destination09 2.17 matrix for Route 488 inbound, 7–10 AM 6

0 0 17.4 2.17 19.5 78.2 52.1 21.7 56.5 143 30.4 36.9 82.5 91.2 52.1 45.6 93.4 156 23.9 128 139 91.2 151299 146 148 195 7.1.2 Bus Passenger Travel Time and Distance

In London, revenue is allocated to bus operating companies according to the number of passenger miles traveled on each route, calculated as the number of passengers recorded by the ticket machine multiplied by that route’s average passenger journey-stage length. TfL has traditionally used the Greater London Bus Passenger Survey (GLBPS) to determine aver- age journey length, but analysts in the organization’s Fares and Ticketing group have been testing the software developed in this thesis as a possible replacement.2 Figure 7-3 plots the average journey distance observed through GLBPS against those derived from the output of the OD-inference process. Each

6.0

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 ) 4.0 ( m k

3.5 nce, t a s d i e 3.0 era g v A 2 duties 2.5 3 duties 4 duties 2.0 5 duties 6 duties 1.5 7 duties

1.0 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 Average distance, km (-inferred Oyster)

Figure 7-3. Comparison of average journey-stage lengths: GLBPS vs. OD-inferred Oyster

2 Bus passenger miles were similarly inferred from AFC data by Lu and Reddy (2011).

130 point corresponds to a single bus route, and the data are classified by the number of GLBPS duties—or surveyor shifts—that were undertaken on each route. Routes with more GLBPS duties matched the Oyster data more closely: Figure 7-4 shows the distributions of error between Oyster and GLBPS, classified by number of duties (the lines extending from each class indicate the minimum and maximum error, while the bottom, middle, and top lines of the boxes indicate the first, second, and third quartile er- ror values). For routes having zero or one GLBPS duties, TfL estimates an average journey length by assigning the average of other routes having the same fare zone (hence, an average of average journey lengths). Larger sample sizes (in terms of numbers of GLBPS duties) appear to match the Oyster average journey lengths more closely, as the median error for five- duty routes is ten percent while 75 percent of routes having six or more duties have less than a ten percent error. Similarly to the calculation of bus journey-stage distances, durations can be easily distilled from OD-inferred Oyster bus data. Schil (2012) uses OD Infethere nceoutputs Application: of the OD-inference Passeng process to ecalculater Journ buse passengers’y Leng tin-hs vehicle travel times, which he then analyzes as an indicator of service reli- ability and expected travel time. By calculating the same metrics for rail

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% Percent change: Oyster vs.  vs. Oyster change: Percent %

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% * *     + Number of  Duties per Bus Route

Figure 7-4. Comparison of average journey-stage lengths by bus route: GLBPS vs. OD-inferred Oyster*Routes with fewer than two GLBPS duties substitute the average journey length from other routes in the same fare zone.

34 131 stages, he is able to propose a single set of reliability metrics across all TfL modes and for full intermodal journeys.

7.2 Cross-Modal and Full-Journey Applications

The applications discussed in the previous section were centered on buses, although similar analyses have also been applied to London’s rail modes. By inferring interchanges, however, travel can be studied across modes, including the activity of passengers who span multiple modes in a single journey. The 7 million daily full passenger journeys revealed by the inter- change-inference process typically follow approximately 1.7 million dif- ferent full-journey itineraries. Full journey matrices are therefore cum- bersome to present in their entirety, but can be aggregated to reveal the flow to or from specific nodes or zones. For example, the origins of all full journeys destined for Oxford Circus Underground station during an AM peak period are shown in Figure 7-5.3 The busiest AM destination on the Oyster rail network, Oxford Circus (highlighted in yellow, near the city’s center) can be seen to attract riders from throughout Greater Lon- don. Origin node counts are color coded to denote the mode of the first stage: all bus origins (red) therefore entail transfers to rail lines, since all activity on the map terminated at an Underground station. Rather than indicating origins and destinations at the level of indi- vidual bus stops or rail stations, these nodes can also be spatially aggre- gated by zones—such as postcodes, census areas, or traffic analysis zones. Additionally, intermodal full-journey matrices can be aggregated by time, enabling the visualization of the entire network’s travel activity. Figure 7-6 captures a frame of an animated time-lapse full-journey matrix (Gordon 2011). An optional function built into the matrix-expan- sion module tracks all passenger flows over the course of a day and inter- polates each cardholder’s location every minute. Customers’ activities are broadly inferred, as each passenger is either in a transit vehicle, between transit trips (and therefore either performing an activity or transferring), or at home (before their first or after their last journey of the day). Such

3 7–10 AM, Wednesday, 19 October 2011

132 Figure 7-5. Origins of full journeys ending at Oxford Circus station (base map: TfL).

133 Figure 7-6. Frame from a time-lapse animation of a full day’s inferred Oyster activity.

animations could be generated on demand for specific areas or times, en- abling analysts to scan for patterns that might inspire more detailed ad- hoc analyses of the data.4

7.3 Observing Changes in Rider Behavior and Demand

Chapter 6 demonstrated that the algorithms developed in this thesis are efficient enough to be applied every day on the full set of Oyster, iBus and transaction-count data. A key benefit of acquiring and retaining full populations of passenger data is the ability to track travel behavior over time (Jones et al 1998). Muhs (2012) uses the outputs of the software developed in this thesis to observe changes in travel behavior and service quality resulting from the reopening of the East London Line (ELL). The former Underground line was extended and integrated into the Overground network, and the study observed riders’ responses to the new service as well as the impact that it had on other transport services.

4 Examples of the animation can be viewed at www.jaygordon.net

134 134 Following Ng (2011), who used Oyster data to study changes in travel time, route choice, and interchange location (using methods developed by Seaborn [2009]), Muhs studied the effects of the line by observing a panel of 54,000 Oyster cardholders who used the ELL in October of 2011 and whose cards were also active in April of 2010, before the line opened. By conducting various analyses before and after the opening of the line—such as riders’ mode choice, travel times, journey frequen- cies, and the analysis of boarding/alighting/flow profiles on parallel and intersecting bus routes—the study was able to quantify many of the project’s effects while testing the predictions of the ELL business case. In addition to comprehensive studies such as those conducted by Ng and Muhs, the analyses mentioned in the previous sections can be con- ducted on an ad-hoc basis to get a quick sense of changes in the trans- port system. Figure 7-7 illustrates the ridershed of the 205X, a special

Figure 7-7. First origins of day for riders of route 205x, Sunday, 28 August 2011.

135 bus service operated to serve the Carnival in the summer of 2011. By mapping the first daily boarding locations of the route’s riders, a coarse estimate of their places of residence is obtained. Similarly, boarding/alighting/flow profiles can be quickly generated to compare travel behavior over time. The profile in Figure 7-8 was gen- erated for route 488, which was extended in 2011 to serve Dalston Junc- tion, the northern terminus of the East London Line. The chart shows the changes in boardings, alightings, and flows, but the ad hoc generation of a matching OD matrix reveals the OD flows that constitute this activity (Figure 7-9).

7.4 Summary

The examples presented in this chapter illustrate just a few of the analyses to which the methods of this thesis can be applied. The outputs of the bus origin- and destination-inference processes alone enable several useful types of analysis, and the application of full-journey expansion factors to those data enable bus ridership profiles and OD matrices to be scaled in a way that makes their outputs compatible with other studies. The interchange-inference process enables the construction of full- journey matrices, which can be used to measure and map various aspects of ridership at a range of spatial and temporal resolutions. Ad-hoc analy- ses of special events, service closures,5 or system performance can be un- dertaken by staff using simple database tools, while large projects such as the East London Line Extension, the 2012 Olympic and Paralympic Games, and Crossrail can be studied in detail longitudinally in order to assess their impacts and inform future projects.

5 AFC data have been used to study passenger behavior during maintenance closures on Chi- cago’s metro system (Mojica 2008).

136 May boardings 1800 Passengers October boardings May alightings MOctoberay aligh alightingstings 1500 May flow October Flow 1200 Flow change

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Bow Church Clapton Pond

Brooksby’sHomerton Walk Grove Dalston Junction Hackney Hospital Rendlesham Road Bow Fairfield Road Sandringham Road Bromley High Street ld Ford Ruston Street Old Ford Parnell Road Bromley-by-Bow Tesco ld Ford Hand &O Flower Bromley-by-Bow Station O Jodrell Road / Wick Lane Dalston Kingsland Station Urswick Road / Sutton Place Bromley-by-Bow Station (SB) Chapman Road / Wick Road Rothbury RoadKenworthy / Wallis Road Road / Wick Road Priory Tavern (NB) / Tesco (SB) Fairfield Road / Tredegar Road Wansbeck Road / Monier Road RectoryShacklewell Road / Amhurst Lane Road/ Cecilia Road Kenninghall RoadDowns / Clapton Road Way/ Rendlesham Road Kinglsand Road / Stamford Road Homerton High Street / Link Street Homerton High Street / Digby Road Lower Clapton Road / Downs Road Kenworthy Road / Hackney Hospital Lower ClaptonLower Clapton Road / CoultonRoad / Linscott Road Road

Shacklewell Lane / Kingsland High Street

Figure 7-8. Boarding/alighting/flow profile for route 488, before and after its extension to Dalston Junction.

Boa rdings Ali ghtin Flow gs

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tr d d d a h S o y d d Roa ig a d d n Roa tt d a H o Wa to o o l d u R Roa on d R nsc e owns R Roa a r t n pt slan ar Co for t Li D a rs ili g g l tio we d u n / / / lesham Roa i o a C ta ree d d d d o t k Lan Cec K a a a / S d c d Stam Fl S o o o / / d d a d Amh l R a / R R R o n & l Ren on o an d / l o t / ane ane R i a R d s on on on d o Roa a d Roa and t g ct l n ll ad ad / Wi pt pt o ll L ll L n o i H Parne o Rus o ap a a l l l R ham P K Jun rfie d i R g r R C C C y l esham n n n ord ord ngha r r r i on l sand R or ri F F to to Fo rel gl pt wns w Fa d d d d a n l l l l als als i owe owe owe  Jo Bromley-by-Bow TescoBromley-by-Bow StationBromley-by-Bow (SB) StationPriory Tavern (NB) / TescoBromley (SB) High Street Bow Church Bo Fairfield Road / Trede O O O Wansbeck Road / MonierRothbury Road Road / WallisChapman Road Road / Wick KenworthyRoad Road / WickKenworthy Road Road / HackneyHackney Hospital Hospital Brooksby’s Walk Homerton Grove Homerton High Street Homerton/ Digby Road High Street /Urswick Link Street Road / SuttonL Place L L Kenn C Do Rend Rect Shacklewe Shacklewe Sand D D K Bromley-by-Bow Tesco – 0 8 3 1 13 8 1 1 13 1 1 0 0 3 0 0 1 0 0 0 0 3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 62 Bromley-by-Bow Station (SB) – – 0 0 0 6 0 0 0 0 0 0 0 0 0 1 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 13 Bromley-by-Bow Station – – – 0 3 21 27 8 11 22 3 14 31 18 7 7 17 15 6 39 15 3 10 3 4 6 24 3 0 3 0 0 0 0 4 1 0 326 Priory Tavern (NB) / Tesco (SB) – – – – 0 17 1 0 1 7 0 1 1 0 0 0 0 0 1 3 1 0 3 0 0 0 1 0 0 0 0 1 1 0 1 1 0 45 Bromley High Street – – – – – 10 1 0 3 0 0 4 0 0 0 1 1 0 3 3 6 0 0 1 0 0 4 0 0 0 0 0 0 0 1 1 0 41 Bow Church – – – – – – 13 1 6 27 4 1 4 15 13 11 10 8 3 14 7 4 7 8 4 10 32 1 0 0 0 0 0 1 3 8 1 219 Bow Fairfield Road – – – – – – – 7 28 41 7 14 18 8 11 8 13 15 1 7 11 0 6 4 1 11 20 0 0 0 0 0 0 0 1 3 0 237 Fairfield Road / Tredegar Road – – – – – – – – 6 3 0 0 0 8 0 0 0 3 0 4 1 0 0 3 3 3 3 0 0 0 0 0 0 3 3 0 1 44 Old Ford Hand & Flower – – – – – – – – – 0 0 0 0 14 0 3 0 1 0 11 1 4 6 4 1 7 18 0 0 0 3 3 0 0 3 3 0 83 Old Ford Parnell Road – – – – – – – – – – 0 0 0 6 3 0 0 1 0 4 0 1 0 1 0 3 6 0 0 0 0 0 0 0 1 0 0 27 Old Ford Ruston Street – – – – – – – – – – – 0 0 11 1 0 3 3 0 4 1 0 0 0 0 4 17 0 0 0 0 0 0 0 1 1 1 49 Jodrell Road / Wick Lane – – – – – – – – – – – – 0 3 1 0 0 1 0 0 4 0 1 1 0 1 10 0 0 0 0 0 0 0 0 0 1 25 Wansbeck Road / Monier Road – – – – – – – – – – – – – 0 6 1 0 0 0 0 0 0 0 4 0 0 13 0 0 0 0 0 0 0 0 0 0 24 Rothbury Road / Wallis Road – – – – – – – – – – – – – – 0 0 0 4 3 1 0 0 0 6 0 1 8 0 3 0 0 0 0 0 0 0 0 27 Chapman Road / Wick Road – – – – – – – – – – – – – – – 3 1 10 6 10 10 10 3 1 18 15 25 4 1 0 0 0 0 3 1 0 0 122 Kenworthy Road / Wick Road – – – – – – – – – – – – – – – – 1 35 6 31 8 6 27 10 24 17 22 8 0 1 1 0 4 0 6 3 4 215 Kenworthy Road / Hackney Hospital – – – – – – – – – – – – – – – – – 1 1 3 1 3 7 7 35 6 7 0 0 1 0 0 0 0 3 4 14 94 Hackney Hospital – – – – – – – – – – – – – – – – – – 1 6 1 11 35 42 25 17 56 0 6 3 4 1 6 3 8 7 6 239 Brooksby’s Walk – – – – – – – – – – – – – – – – – – – 1 1 1 4 13 17 4 8 4 6 0 0 0 1 1 1 1 1 67 Homerton Grove – – – – – – – – – – – – – – – – – – – – 4 4 8 6 0 4 28 3 7 0 3 3 3 4 1 1 0 80 Homerton High Street / Digby Road – – – – – – – – – – – – – – – – – – – – – 4 8 38 14 20 38 1 1 0 6 0 3 6 0 8 0 148 Homerton High Street / Link Street – – – – – – – – – – – – – – – – – – – – – – 3 0 15 7 17 0 0 0 0 1 0 0 1 0 0 45 Urswick Road / Sutton Place – – – – – – – – – – – – – – – – – – – – – – – 0 3 3 6 1 0 0 0 0 0 0 0 1 0 14 Lower Clapton Road / Coulton Road – – – – – – – – – – – – – – – – – – – – – – – – 4 3 21 11 7 7 3 1 1 0 1 7 3 70 Lower Clapton Road / Linscott Road – – – – – – – – – – – – – – – – – – – – – – – – – 0 7 1 10 0 1 3 1 0 0 4 0 28 Lower Clapton Road / Downs Road – – – – – – – – – – – – – – – – – – – – – – – – – – 7 0 1 0 3 8 4 7 11 3 3 48 Clapton Pond – – – – – – – – – – – – – – – – – – – – – – – – – – – 11 14 6 27 13 22 7 8 14 7 129 Kenninghall Road / Clapton Way – – – – – – – – – – – – – – – – – – – – – – – – – – – – 1 1 10 6 15 10 34 42 27 146 Rendlesham Road – – – – – – – – – – – – – – – – – – – – – – – – – – – – – 6 20 8 13 32 31 110 24 243 Downs Road / Rendlesham Road – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – 4 1 0 3 8 10 4 31 Rectory Road / Amhurst Road – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – 0 4 22 32 58 8 125 Lane / Cecilia Road – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – 3 1 18 25 7 55 Shacklewell Lane / Kingsland High Street – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – 1 4 4 0 10 Sandringham Road – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – 0 24 6 30 Dalston Kingsland Station – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – 4 3 7 Dalston Junction – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – 6 6 Kinglsand Road / Stamford Road – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – 0 0 0 8 3 4 66 51 18 56 112 15 37 55 84 45 37 46 101 31 145 79 52 131 155 171 142 399 51 58 28 84 51 83 105 191 351 128 3173

2009 May B 1915 Figure 7-9. Origin–destination matrix for the extended Route 488 inbound, 7–10 AM OctA 3173

137 138 Conclusion 8

The methods presented in this thesis have been shown to infer aspects of public-transport passenger activity that are not directly recorded by au- tomated data-collection systems. By providing results that are consistent with those of manual recording techniques, this work provides a means of obtaining similar information at a far lower cost, covering entire systems on all service days rather than relying on small and infrequent samples. This chapter summarizes the results and performance of these meth- ods and assesses the degrees to which the research objectives were satis- fied. Recommendations are then proposed for Transport for London or others who adopt these methods, and suggestions are put forth for future research that could build upon this work.

8.1 Summary and Findings

The bus origin-inference algorithm, previously validated against TfL’s BODS survey for a sample of London bus routes (Wang et al 2011), was refined and applied to complete daily sets of AFC and AVL data for ten consecutive weekdays. Boarding locations were inferred for over 96 per- cent of the system’s 6.3 million daily bus transactions when applying a maximum timestamp-matching error of ±5 minutes. Alighting locations and times were inferred for 75.6 percent of bus transactions, using a set of spatial and temporal parameters chosen after evaluating the distributions of inter-stage distances and passenger speeds.

139 Interchange status was then inferred for 91.6 percent of Greater Lon- don’s 9.8 million daily journey stages (comprising the aforementioned 6.3 million bus stages plus several of the region’s rail modes). Approximately 30 percent of all journey stages were inferred to have been linked to their following transactions, yielding approximately seven million daily full passenger journeys. As with the destination-inference process, parameters were chosen after exploring distributions of temporal and spatial proper- ties of the data set. Results were compared to the London Travel Demand Survey, revealing a difference in journey length (by number of stages) which may indicate sampling bias in the survey or that the interchange- inference parameters were set too conservatively. Processed Oyster data were used to construct seed matrices of full passenger journeys, including travel spanning multiple public modes, for five consecutive weekdays. Control totals from bus routes and rail stations were adjusted to account for journeys that span multiple time periods, and expansion factors for each full-journey itinerary were then estimated for a set of both full-day and AM-peak-period matrices. When aggregated by route or station, scaled passenger flows matched all control totals to within .005 percent. Passenger flows were then derived for each itiner- ary’s constituent rail journey stages, and the scaled flows were validated against a rail-stage matrix constructed using the well-established iterative proportional fitting method. All algorithms were implemented in a Java application, which typi- cally performs the origin-, destination-, and interchange-inference pro- cesses on a complete daily set of data in less than 20 minutes on a con- sumer-grade computer. The outputs were then used to construct and expand full-journey origin-interchange-destination matrices, typically in less than 10 minutes. By appending origin, destination, and interchange information to a copy of the original Oyster records and storing full- journey expansion factors separately, information is stored at the reso- lution of the individual passenger transactions, enabling the analysis of both granular and aggregate travel information. In addition to being demonstrated for ad-hoc analyses of route- and station-level ridership and service areas, the tools developed in this the- sis have been applied to the visualization of daily system-wide passenger activity, a rigorous evaluation of the effects of a recent capital project (Muhs 2012), and the measurement of service quality and reliability (Schil 2012). TfL staff have been testing these tools as a potential replacement for

140 one or more travel surveys and are in the process of industrializing these methods for use on a daily basis by multiple divisions of the organization.

8.2 Recommendations

This research demonstrates that complete populations of automatically collected public-transport data can be processed efficiently enough to support analysis on a daily basis, even for a large system such as London’s. The long-term cost of developing the required software and allocating approximately 30 minutes of computing time daily (on a server or merely a workstation) is far less than that of conducting quarterly or annual sur- veys, which have much smaller sample sizes. It is therefore recommended that transport agencies with AFC and AVL systems consider adopting these methods as a replacement for many of their existing surveys, which would enhance their analytic capabilities while making the majority of their survey budgets available for targeted, supplemental studies. While the methods in this thesis are largely generalizable to other public transport systems, the implementation developed for this work was designed specifically for TfL and can benefit from several additional enhancements and validations. Since the rate of origin inference directly affects the rates of destination and interchange inference, it is recom- mended that TfL consider correcting for erroneously defined vehicle trips (as proposed by McCaig and Yip [2010]).1 Interchange inference sim- ilarly affects the reconstruction of full journeys and daily travel histories, and the 91.6 percent inference rate can be improved—beyond the gains from improved origin and destination inference—by obtaining AVL or track data for trams. Doing so would enable the inference of tram alight- ing locations, and would enable that mode to be included in full-journey analyses. Attempts should be made to acquire control totals from stations for which they are currently unavailable, such as some newer Overground stations and a number of ungated National Rail stations. Where control

1 Some Oyster bus transactions occur after a vehicle trip’s final stop, suggesting that in some cases iBus may have erroneously recorded a vehicle as serving a trip prior to the one that it was actually serving.

141 totals cannot be obtained, TfL staff should develop a method for estimat- ing default totals. These defaults could be applied to those stations during the extraction of gateline data, or the software could be easily modified to apply them at runtime. Additional validation is recommended, especially against travel-diary surveys such as LTDS. Respondents are now asked to provide their Oyster card numbers voluntarily, and the matching of survey responses to Oys- ter activity could help find correlations between the spatial and tempo- ral data provided by Oyster and the qualitative trip-purpose information provided by the survey, thereby enabling the interchange-inference algo- rithm to more accurately distinguish interchanges from activities. The analyses of spatial and temporal data conducted in this research— such as the assessment of inter-stage distances and out-of-system speeds— should be conducted at smaller scales, such as during specific time periods, in different geographic areas, or on different types of transport services. The parameters of the three inference processes could then be disaggre- gated if appropriate, allowing them to be set differently to account for the variations observed across the system and over time. This research was concerned primarily with the methodology and im- plementation of the above processes, and has only demonstrated a small sample of its possible applications. The processes should be executed and the results stored daily in a database to enable analysts throughout the organization to perform ad-hoc queries, but reporting tools should also be developed to satisfy the different analytic needs of various groups. For example, bus service planners might benefit from an interface that dy- namically constructs boarding/alighting/flow profiles or OD matrices for user-specified routes and time frames, while bus travel speeds could be aggregated by route segment and weighted by inferred passenger flows in order to target the most critical areas for travel-time improvements.2 Similarly, capital planners might use a tool that maps the origins and des- tinations associated with user-specified interchange locations in order to assess demand for new facilities and services. It is recommended that TfL keep complete sets of processed data in a live database for some reasonable duration, such as one or two years, depending on the server infrastructure available. Older data should be

2 Sánchez-Martínez (2012) provides robust analysis tools for bus running time variability and service quality, which could be combined with OD-inferred Oyster data for this purpose.

142 archived offline, but it is recommended that some subset is retained on- line, such as one week per month for more recent years and one week per quarter for earlier years. Doing so would allow planners and analysts to explore historical trends when considering service or infrastructure ini- tiatives, and the selection of system-wide data over many points in time could assist in the calibration of the organization’s various travel-demand models. Perhaps the most important determinant of the application and fur- ther development of this work will be the replacement of Oyster and other AFC cards with contactless bank cards (RFID-enabled credit and debit cards). TfL and other agencies are presently developing fare payment systems which will allow customers to register bank cards for fare pay- ment, then use the cards as they would AFC cards (Lau 2009, Brakewood and Kocur 2012). Since fare transactions will be processed by banks rather than transport providers, it is critically important that transit operators contractually retain ownership of the transaction data. Failure to do so would render the tools developed in this thesis unusable, or would put agencies in the regrettable position of having to purchase their own data from third parties.

8.3 Future Research

The methods developed in this thesis can enable a number of studies that require large volumes of disaggregate ridership information, and the methods themselves could be improved by synthesizing them with other existing research. The processing of Oyster journey stages enables the observation of bus passengers’ in-vehicle travel time and speeds, travel paths, and inter- change locations, but the ability to transfer between rail lines behind the gate obscures customers’ paths while making in-vehicle travel time indis- tinguishable from platform wait time (or from in-system interchange, ac- cess, or egress times). Integration of this work with a path-choice model (as demonstrated by Rahbee [2008]) would enable the inference of this information, allowing analyses to be conducted at even finer levels of granularity.

143 While the assumptions used to infer passenger alightings have been validated against empirical data on several public transport systems (Barry et al 2002 and 2009, Navick and Furth 2002, Zhao et al 2007, Wang et al 2011), it should be expected that not all passengers alight buses at the closest stop to the start of their next journey stage. Combining the near- est-stop rule with observed data from automatic passenger counters (APC) could constrain the possible alighting locations for some bus journey stages, resulting in a more accurate destination-inference algorithm. The inference of both bus boardings and destinations enables the in- ference of vehicle load, which can in turn be used to enhance the inter- change-inference algorithm. By estimating whether buses are full, or by determining whether a bus is being closely followed by another, the algo- rithm could more accurately discern whether passengers were waiting for buses or engaged in activities. This research estimates spatial and temporal information about the in-system and interchange portions of full passenger journeys, but access and egress information could be inferred as well. By using the postcodes of registered Oyster users (Ng 2011, Muhs 2012) to infer access or egress distances (Alshalalfah and Shalaby 2007), a more complete model of pas- senger journeys can be constructed while providing empirical feedback that could be used to calibrate the distance parameters of the destination- and interchange-inference processes. Contactless bank cards, if their data are retained by transport agencies, will necessitate changes to the destination- and interchange-inference algorithms. Future research could address the benefits and challenges of these new systems by distinguishing riders from cards (since a card could be used to pay the fare of multiple riders) and by incorporating non-tran- sit purchases if possible, to better discern journey purpose. A wealth of information could be inferred by applying data mining techniques to several days of processed Oyster records (Fayyad et al 1996, Zhao 2009). Following Morency et al (2007) and Chu et al (2011), the daily patterns of cardholders could be studied over time to infer broad activ- ity types such as work, school, and recreation by analyzing the duration, frequency, and regularity of visits to specific locations or zones. Travel behavior variability could also be studied in this manner—for example, by observing riders’ preferences between using the same service repeat- edly or choosing from among services (perhaps serving the same corri- dor) based on arrival time, crowding, or even weather.

144 Observing a panel of individuals longitudinally could also reveal the pat- terns in which riders adapt to service changes or switch to new services. Finally, the methods developed in this thesis can be applied to other public transport systems. The software has been preliminarily tested with data from the Massachusetts Bay Transportation Authority (MBTA), who operate bus, subway, streetcar, and commuter-rail services in the Boston metropolitan area. Such testing reveals the differences between the vari- ous inputs and parameters of the two systems. For example, MBTA subway customers tap upon station entry only, necessitating a rail destination- inference process that will be similar in many ways to the process for buses. Furthermore, the configuration and density of Boston’s transport system (and underlying land use) is substantially different from that of London, which is likely to require a different set of parameter values. By applying this work to the MBTA and other transit networks, its methods can be made more robust and adaptable, which might ultimately provide a flexible means of enhancing the analytical capacity of transit agencies throughout the world.

145 146 References

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