Transportation METRO 31/08 /201 1
Leeds Public Transport Model LMVR
AECOM in association with the Denvil Coombe Practice
Prepared by: ...... Checked by: ...... Adam Truman Paul Hanson Consultant Regional Director
Approved by: ...... Stuart Dalgleish Associate Director
Leeds Public Transport Model LMVR
Rev No Comments Checked by Approved Date by
1 2 Draft version for PH 15/03/11 3 Draft version for DCP 05/04/11 4 Final PH SD 31/08 /11
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Job No 60048928 Reference Date Created 31/08/2011
This document has been prepared by AECOM Limited for the sole use of our client (the “Client”) and in accordance with generally accepted consultancy principles, the budget for fees and the terms of reference agreed between AECOM Limited and the Client. Any information provided by third parties and referred to herein has not been checked or verified by AECOM Limited, unless otherwise expressly stated in the document. No third party may rely upon this document without the prior and express written agreement of AECOM Limited.
Table of Contents
1 Introduction ...... 2
2 Proposed Uses of the Model ...... 4
3 Model Standards ...... 8
4 Key Features of the Model ...... 10
5 Data Collection...... 25
6 Trip Matrix Development ...... 31
7 Network Development ...... 40
8 Calibration and Validation ...... 47
9 Summary of the Model Development and Standard Achieved...... 54
Appendix A – Validation Results ...... 57
Appendix B – Rail Station Inventory ...... 70
Appendix C - Fares...... 73
Table 1: Anticipated Uses of LTM Table 2: Sectors Table 3: Model Time Periods Table 4: Generalised Cost Weightings Table 5: Initial Wait Times for Both Bus and Rail Table 6: Half Headway Transfer Wait Times for Both Bus and Rail Table 7: Values of Time Table 8: Boarding Penalt Table 9: Mode-to-Mode Transfer Penalties Table 11: Enumeration Parameters Table 12: Evaluation Parameters Table 13: Assignment Parameters Table 14: Crowding Penalties Table 15: Leeds Station Questionnaire Bus Interview Survey Samples Table 16: Bus Interview Survey Sample Size Table 17: Bus Interview Survey Sample Rates Table 18: Time Period Combinations for Tour Matrices Table 19: Distribution of Rail Tickets Types – Leeds Survey data Table 20: Rail Matrix Component Proportions Table 21: Merged Bus and Rail Concessionary Traveller proportions Table 22: Combined Demand Matrix Totals Table 23: Link Types Table 24: Bus Vehicle Types Table 25: Rail Service Capacities Table 26: Non-Transit Table 27: Non-Transit Legs Table 29: Matrix Adjustments Table 30: Combined Cordon Trips: Bus and Leeds Station Trips Table 31: Numbers of Bus Locations Meeting Validation Criteria
Table 32: Minor Rail Station Boarding and Alighting Table 34: Metro card prices (2011) Figure 1: Zone Plan – All zones across the UK Figure 2: Zone Plan – Within West Yorkshire Figure 3: Zone Plan – Within Leeds Figure 4: Zone Plan – Within Leeds Outer Ring Road Figure 5: Sector Plan Figure 6: Initial Wait and Initial Transfer Time Figure 7: Crowding Figure 8: Bus Corridor Counts Sites and Cordon Figure 9: Public Transport Network in Leeds Figure 10: Initial Matrices Compared With Observed Count Data Figure 11: Scatter Graph of Sectored Initial and Adjusted Matrix Demand Figure 12: AM Matrix - Trip Length Distribution Figure 13: IP Matrix - Trip Length Distribution Figure 14: PM Matrix - Trip Length Distribution Error! Reference source not found.
1 Introduction
AECOM Leeds Public Transport Model LMVR 3
Capabilities on project: Transportation
1 Introduction
1.1 Context, Objectives and Scope This Model Development and Validation Report has been prepared to document the development of the Public Transport element of the Leeds Transport Model (LTM_PT). AECOM, in association with the Denvil Coombe Practice (DCP), were appointed to undertake this work in September 2008. Surveys to support the model development of bus and rail use were undertaken in 2008; bus surveys and passenger questionnaire surveys at Leeds station were both undertaken in October 2008; the bus surveys supplementing earlier surveys undertaken in 2007. The model development work commenced in 2008 and was completed in 2010. DCP has provided technical advice on established and emerging good practice in model development during the project, primarily at the specification stage 1. DCP has also reviewed this report but was not involved in the calibration and validation of the model. As the lead contractor, AECOM takes overall responsibility for the model development and project deliverables. LTM_PT is part of a wider suite of models that make up the Leeds Transport Model (LTM). The other two components are the Highway Assignment Model (LTM_H) and the Demand Model (LTM_D). LTM_H is a SATURN highway assignment model covering the Leeds area and representing all road traffic except for powered two-wheelers and cycles. LTM_D provides the demand responses to changes in costs in the two assignment models. LTM_D also includes a parking model. Within LTM_PT bus and rail public transport modes are represented as services operating on separate bus and rail networks. Walking is also represented where passengers would access egress and interchange with the bus and rail networks. The bus network and modelled bus services were constructed from a comprehensive GIS database of bus services in Leeds, the bus network comprising links forming routes between bus stops throughout Leeds and connections to neighbouring authorities. The rail network and modelled rail services represent all METRO lines and services within West Yorkshire. Outside of this area, a degree of rail network and service connectivity is provided to key stations and in accordance with the sparser model detail. 1.2 Document Structure The remainder of this document sets out the various steps undertaken to develop and then validate LTM_PT. It comprises the following chapters: • Chapter 2 – Model Uses and Design Considerations; • Chapter 3 – Model Standards; • Chapter 4 – Summary of Model Features; • Chapter 5 – Survey Data; • Chapter 6 – Matrix Development; • Chapter 7- Network Development; • Chapter 8 – Model Calibration and Validation; • Chapter 9 – Summary.
1.3 Status of Report This report is final. It has been reviewed by Denvil Coombe.
1 As set out in the Leeds Transport Model Scope and Specification, dated February 2009.
2 Proposed Uses of the Model
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2 Proposed Uses of the Model 2.1 Proposed Uses of the Model
At the start of the commission to build this model the focus was on developing a model capable of being used to develop and support a Transport Innovation Fund (TIF) bid for Leeds. While this funding mechanism no longer applies, the general requirements for a model to support development of transport strategy and funding applications in support of major transport schemes remain similar. The range of anticipated uses for the LTM model at the inception of the project is set out in Table 1. Many of these uses are addressed in the demand or highway models and those of direct relevance to public transport model are shaded.
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Table 1: Anticipated Uses of LTM Policy Area Scheme Type Description Assumed Charging Structure Single cordon Single cordon applying to all traffic £charge to inbound AM peak and crossing a predefined cordon defined. outbound PM peak traffic.
Area charge Area charge applying to all vehicle within £charge for all traffic in charging area defined area which could be city centre, an throughout the day (0700-1900). inner cordon or a wider area.
Corridors Variable charge applying to individual Variable charge dependent on corridor corridors in isolation. Charge depends on and distance travelled. distance travelled.
Workplace Parking Levy Levy applying to all PNR parking places in Annual charge defined area. High Occupancy Tolling Apply discounts/exemptions to HOV n/a Fiscal Measures Measures Fiscal Point Charges Point charges at individual nodes or n/ a turning movements.
Parking management Parking supply management (inc. Park Changes to the management of car - and Ride) parking spaces to influence demand.
Parking charge management Changes to the management of parking charge structures to influence demand for parking and change the ratio between parking charges and public transport fares. Park & Ride Implementation of rail, tram and bus - Payment for either or both of public based Park & Ride sites in key corridors. transport leg of journey and parking.
Road Space Reallocation Dedicated carriageway space for a range n/a of modes and vehicle types, such as buses, HGVs, cycles, high occupancy vehicles, etc
UTMC Assess the impact of better tactical control n/a strategies for principal highway links.
Bus Rapid Transit Assess specific major scheme n/a investments in Leeds such as the New Generation Transport (NGT) Proposals Demand Management Measures Measures Management Demand Smarter Choices Assess the impact of soft measures such n/a as workplace , school and residential travel plans, personalised travel planning, public transport information and marketing, car clubs car sharing, teleworking, teleconferencing, home shopping
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Policy Area Scheme Type Description Assumed Charging Structure Developments Model the impacts of new development n/a across the city and surrounding areas in terms of trip generations and consequential model switch, trip retiming, redistribution and re-routing
2.2 Key Model Considerations Within the modelling suite, the demand model represents all modes of travel, including walking and cycling. The demand model responds to changes in travel time and cost changes forecast by the detailed highway and public transport models. While the integration of the public transport model within LTM does not impose additional requirements on the model development, there is a need for network consistency and linkage with the highway model: • at a zonal level to ensure that any demand is representative of trips to/from the same geographic location in either model; and • at a network level to allow for highway congestion effects modelled in the highway model to be applied to the PT model in future year scenarios. Many of the original anticipated uses of the model have a fiscal element to them. These measures collectively determine a requirement for demand to be segmented by income, so as to enable the demand model and the highway assignment model to represent different demand responses according to individuals’ income. The LTM_PT needs to: • include all public transport services, bus and rail within the Leeds area so that the policy areas listed in Table 1 can be assessed; • model route choice and assign passenger trips to public transport modes and services; • have a fine level zoning system to support the modelling of all bus and rail services in Leeds; • represent both peak and inter-peak conditions for operational, economic and financial outputs; • model the effects of crowding which influence travel conditions and have an impact on route choice; • link in with the other LTM models so that information can be passed between them as part of generating the demand and influencing PT model bus journey times with highway congestion information from the highway model; • be compliant with WebTAG Unit 3.11.2 to support funding applications; and • represent fares so that the model can be used to test fare changes as part of the proposed NGT scheme. It was decided that the LTM_PT would be based on the existing public transport model developed in 2008 for the Leeds NGT Study. The NGT model was developed using the CUBE Voyager software, which has been retained for the LTM_PT. The Leeds NGT model was developed to investigate threes specific bus corridors, comprising an extensive network with sufficient coverage for most of the study area. Basing the LTM_PT on the NGT network allows for the service quality and facility SP analysis undertaken by SDG for the NGT study to be used within the model.
3 Model Standards
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Capabilities on project: Transportation
3 Model Standards 3.1 Guidance Guidance on the methods used to develop public transport assignment models is set out in WebTAG Unit 3.11.2. 3.2 Validation Criteria and Acceptability Guidelines The process of validation involves comparing model results with observed data. Any adjustments to the model intended to reduce the differences between the modelled and observed data should be regarded as calibration. Measures, criteria and acceptability guidelines for validation are set out in some detail in Department for Transport (DfT) guidance. The WebTAG Unit 3.11.2 specifies the following validation criteria and acceptability guidelines. “The validation of a public transport assignment model using the following three kinds of checks: • Validation of the trip matrix; • Network and service validation; and • Assignment validation The trip matrices should be validated by assignment on the public transport network and comparison against passengers counted across screen lines and cordons. Differences between assigned and counted flows should in total be within 15%. Validation of the network and services should involve checks on the accuracy of the coded geometry and by comparing modelled flows of public transport vehicle roadside counts respectively. Validation of the assignment should involve comparing modelled and observed: • Passenger flows across screen lines and cordons, usually by public transport mode and sometimes at the level of individual bus or train services; and • Passenger boarding and alighting in urban centres On individual links of the network, modelled flows should be within 25% of counts, except where observed flows are particularly low (less than 150). Wherever possible a check should be made between the annual patronage derived from the model and annual patronage derived by the operator from revenue records. Precise comparisons may be difficult but may be sufficiently accurate to provide a cross-check on the general scale of patronage. However operator data wasn’t available wasn’t available to vary out this check for this model.”
3.3 Convergence It is important that model convergence is taken into account where a public transport passenger assignment model uses certain features that may change the generalised costs used to build paths through the network, and therefore change the levels of demand assigned throughout the network. In the LTM_PT crowding will be modelled which is one of these generalised cost changing features. WebTAG Unit 3.11.1, Section 8.2 advises that, where crowding is modelled, convergence of a public transport assignment model should be monitored in the same way that the convergence of a highway assignment model should be monitored. Citilabs, the CUBE Voyager software developers, have recommended the ‘Excess Demand’ is used to monitor crowding model convergence. Excess Demand is defined as trips (from the demand matrix) which are unable to readily reach their destination due to bottleneck points in the network where demand exceeds capacity, and no viable alternative route is available. When it occurs, Excess Demand is reported for each iteration and tends to fluctuate most during the first few iterations of an assignment when the crowding costs vary most. It has been assumed that the model reaches a satisfactory level of convergence when the Excess Demand output is maintained at similar levels on subsequent iterations.
4 Key Features of the Model
AECOM Leeds Public Transport Model LMVR 11
Capabilities on project: Transportation
4 Key Features of the Model
4.1 Introduction This section covers the key features of the model in terms of the geographical scope and detail of the model, time periods and user classes, assignment and generalised cost assumptions and methodology, capacity restraint mechanisms and the relationships and interfaces the model has with the other elements of the overall modelling suite. 4.2 Model Area The aim of LTM is to model travel demand and network conditions within the Leeds district. The model extends outside of this area but the level of spatial detail reduces. The LTM_PT reflects public transport travel network conditions within the Leeds district. 4.3 Zoning System The zone system comprises 830 zones which are the same as in the highway model but with a different numbering system. The PT model zone numbers are sequential whereas the highway model zones are numbered hierarchically. While the PT zones would ideally be positioned centered on bus stops and rail stations, this is not the case in order that clear linkages are maintained between the PT model and the highway and demand model zones. Figure 1, Figure 2, Figure 3 and Figure 4 show the zone system across the whole of the UK, within West Yorkshire, within Leeds and within the Leeds Outer Ring Road. Consideration was given to the fact that the LTM_PT might require more detail than the highway model in certain areas, and the zones were examined to investigate this. The conclusion was that the level of detail was sufficient to support modelling of public transport services in Leeds. This was based on judgment looking at the homogeneity of access to the network and potential for alternative routes from each zone. Taking the Leeds central area as an example: • 98% of zones are connected to the network with a 3 minute access time or less; and • 67% of zones are connected to the network with a 2 minute access time or less. Further away from Leeds centre, the network access times generally increase as the model network becomes less detailed and the zones represent larger areas. Within the fully modeled area the zones remain sufficiently small that they do not include catchments beyond normal walking distance.
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Figure 1: Zone Plan – All zones across the UK
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Figure 2: Zone Plan – Within West Yorkshire
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Figure 3: Zone Plan – Within Leeds
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Figure 4: Zone Plan – Within Leeds Outer Ring Road
A sector system was set up and used in the matrix building process and for analysing the trip demand. The sectors are shown in Table 2 and Figure 5 below. Table 2: Sectors Sector Number Sector Location Number of Model Zones in Sector
1 City Centre 49
2 Inside ORR (North West) 54
3 Inside ORR (North) 86
4 Inside ORR (North East) 92
5 Inside ORR (South East) 65
6 Inside ORR (South West) 31
7 Rest of Leeds District (North) 49
8 Rest of Leeds District (East) 48
9 Rest of Leeds District (South) 37
10 Kirklees 53
11 Calderdale 18
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12 Bradford 73
13 Wakefield 69
14 Harrogate 8
15 Selby 25
16 York 1
17 Rest of Country (North West) 10
18 Rest of Country (North East) 28
19 Rest of Country (South East) 7
20 Rest of Country (South West) 7
- Park and Ride and spare zones for 20 future developments
Total 830
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Figure 5: Sector Plan
14
7
8 16
12 3 4
2 1 6
5
9
15
11
13 10
4.4 Network Structure The LTM_PT network was based on the NGT model network built in 2008 as part of the Leeds NGT study. This comprises a bus network which was combined with a newly developed rail network to form the LTM_PT. The bus network, taken from the NGT model was revised and checked for use in the LTM_PT Within Leeds the model provides a full representation of the scheduled bus service network. All stops are included as nodes with services coded to stop where they are scheduled to stop. There is some bus stop clustering in situations where stops are located on opposite sides of the road. Outside Leeds, the bus network is less detailed, providing connectivity to/from locations on the periphery of the model. The network connects Leeds with all centres within the Leeds district via bus and to other centres further afield within West Yorkshire via a comprehensive rail network. The model provides a full representation of the rail network throughout the Metro West Yorkshire area. Beyond this, the rail network extends to represent connectivity to station locations centres on the periphery of the model via stations included as part of the rail network. Appendix B provides a list of all stations outside represented in the network outside of West Yorkshire.
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The model includes a walking network which runs adjacent to, and is essentially a shadow copy of, the bus network. It provides connecting links to bus stops and rail stations for access to the transit services. 4.5 Centroid Connectors The placing of centroid connectors has been carefully designed in order to ensure the loading of demand onto the public transport network is realistic. The number of centroid connectors per zone has been minimised to limit excessive reassignment effects through model calibration and forecasting. The following zone connector types are included in the model: • walk centriod connectors; and • drive centriod connectors.
These have been defined in the model as described in section 7.8.1. 4.6 Model Year The base year for the model is 2008. 4.7 Time Periods The LTM_PT has been set up with 3 model time periods as shown in Table 3 below. Table 3: Model Time Periods Period Name Modelled Hour
AM Peak Period Average hour 0700-1000
Inter Peak Period Average hour 1000-1600
PM Peak Period Average hour 1600-1800
Note that the PM peak only represents an average hour of the two hours 16:00-18:00, as the bus frequencies were found to change after 18:00. 4.8 User Classes The LTM_PT assigns two user classes: • non-concessionary ticket users; and • concessionary ticket users. An explanation of how demand has been segmented is described later in section 6.4.2. The demand model is disaggregated by journey purpose, car availability and income. 4.9 Generalised Cost Formulations and Parameter Values The assignment generalised cost formulations expressed in units of time were derived based on WebTAG Unit 3.11.2 which states that: “Generalised costs are used in the calculation of the utility of paths as perceived by travellers and therefore in determining the assignment of passenger flows to the paths. It is a combination of a number of attributes of a path with each being given its own weight or coefficient. These attributes will normally be a sub set of the following list: • Walk access time • Walk egress time • Walk transfer time • Origin wait time • In vehicle time • Fare • Transfer penalty • Distance • Overcrowding.”
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Table 4 sets out the weightings applied to represent in vehicle time, wait time and walk time. Wait times and walk times (and bus boarding penalties and interchange penalties shown later) were based on generalised cost parameters derived by SDG as part of their stated preference work. The parameters were provided by journey purpose which were averaged using demands as weights to derive values for each period. Table 4: Generalised Cost Weightings Component AM IP PM
In vehicle time 1.00 1.00 1.00
Wait time 1.30 1.30 1.30
Walk time 1.67 3.30 2.26
Table 5 and Table 6 set out the initial wait and transfer wait curves applied in the model. The same curves were applied to both bus and rail. The wait times are computed based on the combined headway of available transit services at a given boarding point, and represent a passenger’s ability to control wait time at the start of a trip but less so at interchanges. Table 5: Initial Wait Times for Both Bus and Rail Headway (H) Factor Wait Time (mins)
1 1/2 0.5
16 1/2 8
30 5/2 12
45 1/3 15
60 1/4 15
Table 6: Half Headway Transfer Wait Times for Both Bus and Rail Headway Factor Wait Time (mins)
1 1/2 0.5
16 1/2 8
30 1/2 15
60 1/2 30
Figure 6: Initial Wait and Initial Transfer Time Curves
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Fares were included in the LTM_PT to represent fare costs incurred in travelling by bus and rail. Fares are described at the end of this chapter and in further detail in the Appendix. Values of time were used to convert fares into units of generalised time. The values were based on local values and are set out in Table 7 below: The same value was applied across all PT modes, bus and rail, and model periods. Table 7: Values of Time AM IP PM
Value of Time (2002 prices) 9.52 8.65 9.46
Table 8 and Table 9 set out the boarding and transfer penalties applied in the model. These parameters were later refined as the model developed. Final values are out in Chapter 8. Table 8: Boarding Penalties Mode AM IP PM
All transit modes 5.47 5.51 5.48
Table 9: Mode-to-Mode Transfer Penalties Mode AM IP PM
All mode interchanges 5.00 5.00 5.00
4.10 Bus Stop and Rail Station Quality Factors Bus stop and rail station quality factors were included in the model in the form of time penalties. These were assigned to the connecting links located between the walk and transit networks. Each time a passenger arrives at a bus stop or rail station from the walk network to board a service they pass along a connecting link and incur the station quality time penalty. The quality factors were put forward by SDG following a review of their stated preference research undertaken for the NGT Major Business Case Submission. They were provided for bus stops across the Leeds district area. For rail, no penalties were provided and so an intermediate penalty value was estimated to counter the impact at the bus stops. The bus stop penalties were put together base on a series of ‘package component’ penalties representing features such as poor lighting, no real times information and no cctv. Table 10 below shows the bus stop penalty values and their frequencies. Table 10: Bus Stop Penalties and Frequency Bus Stop Penalty (minutes) Frequency
17.28 2836
14.94 501
10.52 211
15.06 4
9.96 6
1.1 3
10.16 1
4.11 Assignment 4.11.1 Assignment Parameters The model comprises three average hour periods representing the AM, IP and PM which are assigned separately. The assignment process involves the following three components: • route enumeration; • route evaluation; and • modelling of crowding.
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The route enumeration part involves identifying a set of discrete routes between zone pairs which have some probability of being used by passengers to travel between zones. The route evaluation part involves examining the enumerated routes and identifying routes that have a computed probability of being used by passengers. During the process of modelling crowding, the Public Transport program iteratively balances loaded demand against capacity. At each iteration, the program completes the route evaluation and modelling of crowding processes, which are repeated for all user classes, and then adjusts the costs in the model to reflect the assigned loads. The iterative process uses the crowd modelling procedures: • link travel times; and • wait times The model was set to run with the enumeration, evaluation and assignment parameters set out in Table 11, Table 12 and Table 13. The default values from the Citilabs manual are shown in brackets. Table 11: Enumeration Parameters Parameter Value Description
BESTPATHONLY F (F) When true the evaluation process iden tifies a single best path, onto which all demand is loaded, and the enumeration process changes its mode of operation AONMAXFERS 8 (45) Max number of permitted transfers on the minimum-cost all-or-nothing-routes MAXFERS 2 (5) Max number of transfers allo wed in routes between origin -destination pairs with more than one enumerated route EXTRAFERS1 1 (3) Number of transfers at which the program stops exploration of less direct routes EXTRAFERS2 1 (2) Maximum number of transfers explored in excess of the number of transfers required by the minimum-cost route SPREADFUNC 1 (1) Integer specifying the function used to compute spread. SPREADFACT 1.05 (1.2) Multiplicative Factor used in multirouting function to compute spread SPREADCONST 40 (5) Constant used in multirouting function to compute spread Table 12: Evaluation Parameters Parameter Value Description
ALPHA 1 (1) Determines the relative weights for the genera li zed costs of the walk leg and the remainder of the route in the walk-choice model LAMBDAW 0.2 (0.2) Determines the proportion of trips allocated to each walk choice at a node. LABDAA 0.2 (0.2) Determines the proportion of trips allocated to each alighting node. Table 13: Assignment Parameters Parameter Value Description
NOROUTEERRS 20 (10) No of zone pairs that the program can process with trips between them but no valid routes NOROUTEMSGS 20 (5) Number of messages the program reports for loaded zone pairs that have trips between them but no valid routes APPLY T (F) Runs the crowding model ADJUSTLINK T (F) Invokes travel -time adjustment, which reflects higher behavioural costs associated with travelling in crowded conditions ADJUSTWAIT T (F) Invokes the calculation of additional wait time PERIOD 60 (60) Length of the modelled period in minutes ITERATIONS 20 (1-99) Specifies the number of iterations performed during the crowd-modelling run
4.11.2 Model Software Issue During the course of the LTM_PT model development work, an issue was identified with the CUBE Voyager software. At the time this was being investigated by CItilabs, the software developers. The issue relates to the route evaluation process. During assignment, routes between a given O-D pair are allocated on the basis that wait time is half the headway which appears to be
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inconsistent with the application of the wait curves used to estimate average wait time. Citilabs, subsequent to the model validation, have introduced a beta version of CUBE that may address this inconsistency. Capacity Restraint Mechanisms – Crowding Crowding is used as the capacity restraint mechanism in the model. WebTAG 3.11.2 guidance on the modelling of crowding for PT is limited. Web TAG Unit 3.11.2 states: The introduction of crowding has significant practical problems for PT assignment namely the need for the assignment to be an iterative procedure with a consequent impact on run times, the need to achieve convergence and the need to calibrate overcrowding curves. For these reasons crowding should only be modelled where it is likely to have a significant effect on traveller behaviour or where an effect on crowding is one of the objectives of the scheme. Where crowding is not modelled it is still important to monitor volume to capacity ratios when forecasting to determine whether crowding will become a problem in the future. (Paragraph 6.4.4)
Crowding functions for rail modelling have been researched and recognised functions are in common use. The nature of rail travel is such that crowding is an important issue in users travel choice. There is significantly less empirical information to construct crowding curves for bus travel. The fact that the majority of journeys are much shorter, while loading surveys have identified much higher levels of demand relative to capacity on buses which suggests that crowding has potentially a lesser impact on choice for bus travel. Certainly the functions derived for rail are not directly transferable to bus. Following a review of the advice in WebTAG, it was considered that the use of crowding curves for rail in this model was necessary, since services operating to/from and through Leeds currently experience significant levels of crowding which may be expected to continue into the future. Modelling crowding reflects real users’ responses to increases in crowding over time. Modelling crowding on buses was not such a clear cut issue. There are arguments that suggest that it should be used, as for rail to reflect behaviour, whilst there are counter arguments stating that the use of bus crowding curves in scheme forecasting testing leads to an underestimate of potential public transport demand and suppresses modal transfer to public transport. Also, it can be argued that buses should not have crowding curves allocated because bus operators, unlike rail operators, can readily provide more frequent or larger buses to cater for over-demand. Thus carrying out future year tests with bus crowding may bias unrealistically in that it fails to account for the operator’s natural response to growth in demand. Clearly the omission of crowding modelling from buses could lead to situations in which the assigned demand on a route exceeds the capacity of that route. In forecasting it will be necessary to identify and highlight these occurrences as the model is used to test scheme proposals, to ensure that the assigned flows are realistic, given the operators ability to provide additional capacity to meet demand. However, it may be noted that the NGT schemes under consideration would lead to reductions in demand on existing bus services and consequently crowding on bus services would be less critical. In each case sufficient NGT services are provided to ensure crowding would not be a significant problem. Consequently the crowding model operates for the rail services, but not for the bus services. Each test should identify any bus routes where overcapacity is potentially a problem and ensure that sufficient extra capacity could be provided to deal with the issue. While bus and rail services do provide for the same movements in some cases, these movements are considered to make up only a relatively small proportion of all O-D demand, particularly the shorter distance trips contained within the Leeds district area. Crowding functions are assigned to vehicles which are in turn assigned to services. All rail services operate with the same crowding function. While the rail services extent outside of the Leeds district area they retain the same crowding function throughout their length of their route. It is noted that this is a limitation in the model as full demand should really be provided where crowding is modelled. All rail services were subject to a crowding function in CUBE Voyager format. This is based on consultant’s calculations of PDFH 5 recommended time multipliers. The shape of the curve was defined by a crowding factor of 1 at 60% capacity, 1.1 at 80% and 1.4 at 100% as shown in Table 14 and Figure 7. The crowding factors have the effect of increasing the perceived in vehicle time.
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Table 14: Crowding Penalties (Rail only) Utilization Crowd Factor
0 1.00
60 1.00
80 1.10
100 1.40
Figure 7: Crowding Penalty Curve (Rail only)
4.12 Relationships with Demand Models and Public Transport Assignment Models The PT model is only one element of the overall modelling suite. Data transfer between the model elements is essential for the overall operation of the model. The PT model needs to exchange data with both the demand and highway models. Travel costs from the PT model are transferred to the demand model as skimmed costs. In return, the demand model supplies new PT travel demand. Congested journey time information is also supplied from the highway model to the public transport model. This is done at a corridor level by time period. Each bus route is split into sections between major junctions with the cumulative highway time over each section being transferred to the PT model and then proportioned between the bus stops on that section. The zone system in the public transport, highway and demand models are common thereby simplifying the process of transferring data between them. 4.13 Fares Fares are included in the model based on the following four separate fare systems: • rail fares – matrix-based; • First Bus fares – distance-based; • Arriva Bus fares – distance-based; and • Free City Bus – free travel. These were derived based on an analysis of current (2011) fare structures for bus and rail services operating within the Leeds district area. In the model base year (2008), First Bus and Arriva operated the majority of services in the Leeds area and, while others exist, the decision was made to maintain a degree of simplicity and reflect bus fares based on these two operators only. Consideration was given to merging the two bus fare curves to improve the cross-city fares which involve trips on both operators. These would
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generally be made using a Metro card rather than paying two separate fares. However, this was discounted due to the very low proportion of trip demand undertaking cross city movements. The public transport model demand is segmented into fare paying passengers and concessionary passengers. The bus fares systems described were applied to the fare paying segments except in the AM peak where concessionary travellers pay full fares. Rail fares were applied to both segments in all time periods. Details of how the fare systems were constructed are provided in the Appendix.
5 Data Collection
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5 Data Collection
5.1 Overview This section describes the survey and count data used to build and validate the LTM_PT. 5.2 Passenger Survey Method Data collection for both bus and rail was carried out by Jacobs by means of passenger interviews and control counts. The Leeds Transport Model Report of Survey describes this process in detail. WebTAG Unit 3.11.2 describes the various methods of collecting demand data for public transport models and provides advice on the quality of data that may be obtained. The conclusion is: In summary, the best approach is for an adequate sample of face-to-face interviews to be conducted on board a sample of public transport vehicles on each service in the modelled area. However operators may not allow interviews to be conducted on their vehicles. The next best approach is to conduct face-to-face interviews at stops and stations.
For the Leeds model, bus demand data were collected by surveys on-vehicle, whilst rail data were collected by interview at Leeds City the station. It is considered that in both cases the approach taken was appropriate. 5.3 Rail Data Rail data were obtained from the following sources: • Leeds station questionnaire survey; • Leeds station footfall count; • Northern Rail ticket sales data; and • Northern Rail loading counts. 5.3.1 Leeds Station Rail Passenger Questionnaire Survey The passenger questionnaire survey was undertaken by Jacobs on 1 st October 2008. Self-completion questionnaires, requesting information about the trip being made by the passenger as they received the form, were handed out to every passenger entering the station concourse through the automatic ticket barriers. Forms were offered to every passenger exiting the station between 0700 and 1900 on the survey day. A total of 4000 useable completed forms were returned, representing an 11% sample of the 36,000 passengers counted entering the station during the survey period. Further details are provided in the Report of Survey. 5.3.2 Leeds Station Footfall Count The Leeds station footfall count was carried out by Jacobs on 1 st October 2008 to coincide with the questionnaire survey. Counts were conducted of passengers exiting at the controlled exit barriers. This enabled the survey to exclude pedestrians passing through the station or meeting travellers, since only ticket holders can pass through the barriers. Separate counts were undertaken at the barriers in the main concourse and at the additional exit on platform 8. Counts were recorded in 15 minute time slices between 0700 and 1900. 5.3.3 Data Provided by Northern Rail Ticket Sales LENNON (ticket sales records) data were provided by Northern Rail for the year between 2008(P11) and 2009(P10). Data were provided for the following stations: • Bramley; • Burley Park; • Cottingley; • Cross Gates; • Garforth; • Headingley; and • Horsforth.
For each station, sales of tickets from that station to all other UK stations were recorded. Sales were split by three ticket types: full, reduced and season.
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Boarding and Alighting Counts Northern Rail provided boarding and alighting and on vehicle counts for all Northern Rail services passing through Leeds District. Data were collected at seven stations for the period 14 December 2007 to 13 December 2008. Infra red beam technology was used to count passengers onto and off train units at the following stations: • Leeds; • Bramley; • Burley Park; • Cottingley; • Cross Gates; • Garforth; • Headingley; and • Horsforth.
For each train through these stations the following information was recorded (annual average data): • arrival time; • average boarding; • average alighting; • average load on departure; and • highest/lowest load on departure. 5.3.4 Samples Table 15 shows the sample sizes and rates taken from the Leeds rail station questionnaire surveys. Table 15: Leeds Station Questionnaire Bus Interview Survey Samples AM IP PM
Sample Size 2998 671 328 Sample Rate 15.5% 5.8% 6.1%
5.4 Bus Data For the bus demand, the following data were collected: • on-bus surveys; • ticket sales (ETM) data; and • cordon counts.
5.4.1 On-Bus Surveys SDG carried out on-bus surveys on 28 services in October 2007 for the NGT study. These data were augmented by surveys on the remaining services later in 2008 (3 rd September and 17 th November). Within the two data sets approximately 31,000 useable records were obtained, which is a 14% sample of approximately 220,000 journeys made daily by bus in the Leeds District. 5.4.2 Ticket Sales (ETM) Data METRO supplied a complete record of on-bus ticket sales data for a sample of services. Data were provided for two complete months, February and June 2008. Ticket sales data (ETM) were provided by METRO to enable the survey data to be expanded to represent the average weekday demand (the June data were used for this purpose). 5.4.3 Cordon Counts Figure 8 shows a series of 18 cordon sites located around central Leeds where bus passenger counts crossing the cordon were carried out. The surveys were carried out for one complete 12 hour day at each cordon crossing point. In most cases, the data were collected by boarding the bus at a stop close to the cordon and carrying out a count of the passengers on board. For a very few buses (4%) an estimate of the loading was made from outside the bus without boarding.
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Figure 8: Bus Corridor Counts Sites and Cordon
For every bus crossing the cordon the following information was recorded: • route number; • time; • bus type; and • either actual or estimate of number on board.
Surveys were carried out over a three week period on weekdays during October 2008. 5.4.4 Bus Journey Time Data Metro provided two files of automatic vehicle location (AVL) derived journey time data. These data represent bus journey times on 17 corridors and were collected during the Autumn of 2007. The data do not distinguish between services, but they provide average, minimum and maximum run times on each route section for each half hour period during the day. In addition, bus journey time information was sourced from timetables times from the Metro website. 5.4.5 Samples Table 16 and Table 17 show the sample sizes and rates for the on-bus passenger surveys. They demonstrate that a robust sample has been achieved across the majority of the bus corridors. The exceptions are Harehills Road, Compton Road, Lavender Walk and Burley Road and where the samples rates are relatively low.
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Table 16: Bus Interview Survey Sample Size Corridor Sample AM IP PM Total Woodhouse Lane 492 1107 433 2032 Meanwood Road 147 304 110 561 Scott Hall Road 160 251 123 534 Chapeltown Road 233 601 306 1140 Roundhay Road 79 201 114 394 Harehills Road 99 172 74 344 Compton Road 41 64 26 131 A64 York Road 307 940 661 1908 Lavender Walk 22 41 22 85 A61 Hunslet Road 369 870 408 1647 A653 Dewsbury Road 338 992 595 1925 Top Moor Side 179 513 198 890 A58 Domestic Road 128 182 32 341 B6154 Wellington Road 199 537 243 979 A647 Armley Road 291 721 308 1319 Kirkstall Road 258 800 408 1466 Burley Road 130 266 99 494 Moorland Road 84 106 90 279
Table 17: Bus Interview Survey Sample Rates Corridor Sample Rate AM IP PM Average Total Woodhouse Lane 19% 17% 14% 17% Meanwood Road 13% 19% 11% 15% Scott Hall Road 46% 39% 29% 3837% Chapeltown Road 33% 46% 32% 37% Roundhay Road 23% 25% 8% 1913% Harehills Road 11% 6% 4% 76% Compton Road 6% 4% 2% 4% A64 York Road 10% 10% 25% 1513% Lavender Walk 5% 14% 5% 87% A61 Hunslet Road 15% 12% 11% 1312% A653 Dewsbury Road 22% 49% 38% 3637% Top Moor Side 16% 39% 23% 2625% A58 Domestic Road 49% 45% 20% 3841% B6154 Wellington Road 15% 18% 12% 15% A647 Armley Road 12% 17% 7% 12% Kirkstall Road 17% 22% 13% 17% Burley Road 9% 9% 3% 76% Moorland Road 22% 17% 65% 3422% AverageTotal 1914% 2317% 1813% 2015%
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5.4.6 Limitations Due to the nature of the survey design there is a risk that not everyone was given a form and captured in the survey. No count data were collected at the time to correct this potential issue. 5.5 Data for Model Validation Of the data described in this chapter, the following were used to validate the model: Bus Data • Bus cordon counts.
These datasets were independent of the data used to build the model.
Rail Data • Leeds station footfall count; and • LENNON ticket sales data.
These datasets were not independent of the data used to build the matrices; no independent supplementary data were available.
6 Trip Matrix Development
6 Trip Matrix Development
6.1 Overview The public transport matrices represent all trips, whether made by bus or rail. This section describes the development separately for each mode from the available survey data. 6.1.1 Assignment Matrices The assignment matrices require segmentation into the following two classes: • non-concessionary ticket users; and • concessionary ticket users Trips were required in units of average hourly demand for each period. Matrices were developed for assignment for the three time periods: • AM Peak (average 0700 – 1000); • Inter Peak (average 1000 – 1600); and • PM Peak (average 1600 – 1800).
6.1.2 Demand Model Matrices The demand model matrices on the other hand require a high degree of segmentation, to identify the different markets within the overall pattern of trip making. Matrices were segmented by time, purpose and income as explained below. For the demand model, matrices were required in the form of tour-based, production – attraction (PA) matrices. In this format each home based tour in the matrix represents a linked pair of trips going away from home (the production zone) in the first time period and return to home from the attraction zone during a second time period. For the demand model, separate matrices were required for each combination of linked time periods. This effectively resulted in a set of 45 timed matrices on the basis of combining outbound and return journey times. Table 18 shows the time period combinations for the tour matrices. Table 18: Time Period Combinations for Tour Matrices Early AM1 AM2 AM3 Inter PM1 PM2 PM3 Late 0000- 0700- 0800- 0900- 1000- 1600- 1700- 1800- 1900- 0659 0759 0859 0959 1559 1659 1759 1859 2359
Early 0000-0659
AM1 0700-0759
AM2 0800-0859
AM3 0900-0959
Inter 1000-1559
PM1 1600-1659
PM2 1700-1759
PM3 1800–1859
Late 1900-2359
Matrices were also required to be subdivided into six trip purposes: • home-based: commuting, education, business and other; and • non-home-based: business, other.
The final matrices were then further segmented into three household income levels, low, medium and high. This income division was carried out on a zone by zone basis rather than a cell by cell basis. Information on income levels in each zone was drawn from the bus and rail survey data: respondents were asked to state their income band. 6.1.3 Non-Surveyed Time Periods All public transport surveys were carried out during the time period 0700 to 1900. The early-early (0000-0659 to 0000-0659) and late-late (1900-2359 to 1900-2359) time periods were not covered by the passenger surveys. It was necessary to create synthetic matrices for these time periods. This was done by using trip patterns from the inter peak period and scaling these to the bus ETM data and Northern Rail count data for the off peak period. The scaling factors were by journey purpose. 6.2 Creation of Rail Matrices 6.2.1 Data Cleaning The first stage of the matrix development was to process the survey of rail passengers to develop a partial matrix of rail trips outbound from Leeds station. The data were first interrogated to identify illogical responses, either in terms of origins and destinations or to questions regarding trip purpose and access/egress modes. Illogical data were corrected where possible, or otherwise deleted from the data set. Incomplete data records were also deleted, except in cases where sufficient information was provided to define the trip. 83% of the raw data was taken forward to build the matrices. 6.2.2 Zoning Origin and destination zone numbers were added to the survey records using a correspondence between postcodes and zones. Where one trip end purpose was defined as “home”, this zone was considered the production zone for the trip and the other trip end the attraction. Where neither trip end was defined as “home” then the origin for the first leg of the trip was considered the production zone (note that the survey is of passengers travelling by rail to central Leeds). 6.2.3 Journey Timing For the rail data, the survey form used at Leeds Station requested journey times for both the outbound and inbound journeys. All return journeys were assumed to be completed in one day; thus, where the reported time of the return journey was earlier than the surveyed journey, it was assumed that the surveyed journey was the second leg of a return trip. For records where no return time was given, a return time was generated, based on the distribution of times from the complete survey records. 6.2.4 Expansion Expansion factors were calculated for the rail survey data, where passengers had been asked about all legs of their journey to capture information about inbound and outbound trips. Each expansion factor was derived from the ratio of valid returned survey forms to inbound passenger counts during the survey day. Separate expansion factors were calculated for each quarter hour of the survey. It was recognised that trips arriving in Leeds and travelling onwards by bus would be double-counted in the data set once rail and bus matrices were merged. An analysis of the data showed that around 9% of reported journeys used bus for their onward journey. For the purpose of correction for double-counting (when combining with data from bus surveys), it was assumed that, for any given journey, it was equally likely to have been surveyed on any of the sequence of modes used for the journey. Consequently, the expansion factor for each of these records was halved. Similarly in the bus dataset, the expansion factor for any trip reporting a rail leg to the journey was halved.
6.2.5 Infilling All trips to and from Leeds Station were identified from the survey data. There were, however, seven other stations in the Leeds District area for which survey data were not available. Trips using these stations were identified by ticket sales data (Lennon data) supplied by Northern Rail. The ticket sales data provided a breakdown by ticket type of the number of tickets sold for journeys from each station in the UK to each of these seven stations on a four weekly basis. The data provides numbers of tickets sold split into three ticket types: • full; • reduced and other; and • season.
Ticket sale journeys are always recorded as originating at the station where the ticket was sold. Thus, a return ticket from Garforth to Leeds is recorded as two tickets from Garforth to Leeds. The station of issue was assumed to be the production station and the destination station the attraction. For full, reduced and other tickets, it was assumed that each trip represented a return journey. However, for season ticket sales, it is not clear how many journeys are made with each ticket. This is further complicated by the fact that no disaggregation was made by weekly, monthly and annual tickets. To overcome this, the Leeds survey data were compared with the Leeds station ticket sales data to establish the average number of times a season ticket is used on weekdays to create factors to adjust season ticket sales to trips. 6.2.6 Journey Timing The trips obtained from the LENNON data are given in terms of daily trips, with no disaggregation by either time of day or trip purpose. To enable these data to be disaggregated the purpose and time splits, by ticket type, observed at Leeds was applied to the trips to and from the other stations. 6.2.7 Zone Allocation LENNON data are provided on a station to station basis. The trip ends were converted to zone equivalents before matrix building. For external zones, there is a direct correlation between stations and zones such that each station serves a single zone. In these cases, there is no need to disaggregate the trip data to zones. For trips in the West Yorkshire area, all zones were allocated to the nearest station. In some instances, access services at a given station could be from more than one zone. Trips to and from each station were divided between the zones linked to that station. Data from the Leeds Station surveys were used where possible to represent the distribution of trips over the catchment area for the local stations. Certain stations were identified as park and ride stations serving a wide area, such as the station at Garforth. For LENNON data, trips originating at these stations zones were allocated following a similar distribution to the patterns observed for trips arriving in Leeds from those stations. 6.2.8 Use of Metrocards The survey data and LENNON data provide full information for rail trips to and from the Leeds urban area, with one exception. LENNON data do not contain any details of Metrocard usage. Metro informed us that no central database of trips using the Metrocard was available. For trips to Leeds station, Metrocard use was captured by the survey returns. For trips between suburban stations, data do not exist. It was assumed that the proportion of Metrocard users to overall users would be the same as for trips to Leeds, as shown in Table 19. The matrices of rail trips between local stations (excluding trips to Leeds) were accordingly factored up by nearly 50% to reflect Metrocard usage. This assumption was tested during the model validation (Chapter 8) to compare the modelled and observed numbers of passengers boarding and alighting at each of the suburban stations.
Table 19: Distribution of Rail Tickets Types – Leeds Survey data Ticket Type From Survey
Single 5.1%
Return 37.5%
Metrocard 31.2%
Rail Operator Season 18.1%
Concession 4.5%
Free 0.1%
Other 3.5%
Total 100%
6.2.9 Through Trips The demand matrices were required to contain full information for trips originating or terminating within the Leeds district area. To allow the model to represent the impacts of crowding on rail services, it was also necessary for the assigned train loads to be representative of all trips using the services. This includes trips with neither trip end in the Leeds area. To achieve this it was necessary to infill the matrices to take account of these through trips using the count data described below. 6.2.10 Northern Rail Services The majority of Northern Rail services either start or terminate at Leeds. Consequently, the matrices will contain details of all trips on board for the parts of the journey within the Leeds district area. The only two exceptions to this are the services between: • Blackpool North and York; and • Wakefield and Selby.
For these services, the automatic passenger count data provide information on the number of passengers on board as the trains enter and leave Leeds area. A comparison between the numbers boarding and alighting in the Leeds area and the train counts provided an estimate of the number of passengers on these trains making journeys through Leeds. These trips were added to the matrices using a simple gravity model approach to distribute trip origins and destinations between the stations and zones represented outside Leeds that form stops on the routes. Table 20 sets out the total rail matrix proportions contributed from the Survey and LENNON data. The through trips are included in the LENNON data proportions. Table 20: Rail Matrix Component Proportions AM IP PM
Survey 32% 16% 32%
Lennon 2% 1% 2%
6.3 Creation of Bus Matrices 6.3.1 Data Cleaning The data were interrogated to identify illogical responses, either in terms of origins and destinations or to questions regarding trip purpose and access/egress modes. Illogical data were corrected where possible, or otherwise deleted from the data set. Incomplete data records were also deleted, except in cases where sufficient information was provided to define the trip.
6.3.2 Zone Allocation Origin and destination zone numbers were added to the survey records using a correspondence between postcodes and zones. 6.3.3 Expansion Three expansion factors were required for bus services, these relate to the fact that: • only a proportion of people given survey forms actually returned them; • only a proportion of people on a surveyed bus were given forms; and • only a proportion of buses on each route was surveyed.
This section describes how the expansion factors were derived. • The surveyors kept records of the serial numbers of forms handed out on each individual bus. These records were used to estimate the number of forms handed out on each bus. The serial numbers on the returned forms were used to link the form to the bus it was handed out on. From this, the number of forms handed out and returned on each bus was identified. The first factor was therefore the ratio between forms handed out and forms returned. • Electronic ticket machine (ETM) data were provided by Metro which provided one complete month’s data for each bus service operating in the Leeds District. From these data, it was possible to estimate the total number of passengers boarding each service during each time period on an average weekday. This was used to calculate the average number of passengers boarding each bus during each time period. The second factor was therefore the ratio of the average weekday boarding to the number of forms handed out. This factor was calculated separately for each service and for each model time period. • The third factor was calculated using known bus headways. From the headway, an estimate was made of the number of buses operating each service during each time period. The third factor was therefore the ratio between the total number of buses operating the service and the number of buses that were surveyed. This factor was calculated separately for each service for each modelled time period. • Finally, an overall expansion factor was calculated for each survey record by multiplying together the three factors related to the service and the time period.
6.3.4 Correction for Double-Counting Double counting could occur where journeys involve a change of bus or a combination of bus and rail. Both bus and rail surveys included a question concerning access and egress modes so that the number of such trips could be identified. In the bus database, around 18% of passengers interviewed were making a journey that included one or more changes of bus. A further 1% included both a train and a bus leg in their journeys. A small proportion, less than 1%, of passengers surveyed reported that they would have two or more changes. For the purpose of correction for double-counting, it was assumed that for any given journey, it was equally likely to have been surveyed on any of the sequence of buses used for that journey. This was supported by the finding that the numbers of records of passengers interchanging were relatively evenly distributed between passengers who had already used a bus prior to that they were surveyed on, and those who would transfer to another bus following the survey. Consequently, once the expansion factors were calculated, a further factor of 0.5 was applied to any trip reporting an interchange. The same principle was also used for rail to bus transfer. For any bus trip that reported that rail had been the access mode, the expansion factor was multiplied by 0.5. 6.3.5 Corrections for Bias by Ticket Type in Survey Samples A comparison was carried out between the distribution of ticket types in the survey and the distribution of ticket types in the ticket sales data to check for any biases in the response by ticket type. As the distribution of ticket types observed in the survey appeared similar to the records from the ETM data, it was concluded that no correction was necessary to compensate for bias by ticket type in the survey data.
6.4 Combining the Rail and Bus Matrices Having been developed separately, the bus and rail matrices were then combined. For the assignment model matrices the demand was required in two user classes; however, for the demand model (LTM_D) matrices further segmentation was required. 6.4.1 PT Matrices for the LTM_D The LTM_D matrices were required in a PA (tour-based) format disaggregated by journey purpose. Sectors were used to combine zones and form the larger sector areas which could be used as a basis for deriving factors to split the demand used to form the assignment model matrices. Sector level proportions were derived for each journey purpose which were applied individually to the matrices on a zone to zone basis. Car available / non-available proportions were applied globally as 57% car available and 43% no car available, as recorded in the rail and bus surveys. To restructure the matrices into tours, information about times of travel for inbound and outbound journeys from the bus and rail surveys was used. These data were used to generate an estimate of the proportion of trips in each pair of time periods. These were applied separately to each of the time periods prior matrices to create a set of tour matrices for each time period. To reconcile these into a single demand set, adjustment factors were used to effectively balance out (average) the demand between pairs. Tours were established for each time period and then averaged between time periods to balance differences in the demand generated from the individual times periods. The method used to create the tour matrices, using sector proportions, ensured that the matrices were effectively smoothed out and avoided ‘lumpiness’. 6.4.2 PT Matrices for the LTM_PT The LTM_PT matrices were created after the LTM_D matrices. This involved first aggregating the LTM_D matrices to form O-D matrices by time period, journey purpose and income level. These matrices were then split into non-concessionary and concessionary demand sets based on factors derived from the bus and rail survey datasets: • on-bus surveys carried out by SDG for 28 services in October 2007; and • Leeds Station Passenger Self Completion Questionnaire Survey carried out on 1st October 2008, also undertaken by SDG. The bus and rail survey data included information about income level specified by the following categories: • <£15,000 Low • £15,000 - £19,999 Low • £20,000 - £24,999 Medium • £25,000 - £29,999 Medium • £30,000 - £39,999 Medium • £40,000 - £49,999 High • >£50,000 High
Segmentation involved deriving single global factors for each purpose and by income level. This was a relatively simplistic approach; however, it was considered reasonable for the PT model. The two bus and rail survey datasets were analysed with the aim of deriving concessionary and non-concessionary proportions for each journey purpose. Initially this was done separately generating a set of proportions for bus and a set for rail, which revealed that some of the rail proportions were notably different from bus. This was particularly the case for the home-based other purpose which forms a considerable proportion of the overall PT demand.
So, while the bus survey records far outweighed the fewer rail records; the decision was made to merge the bus and rail proportions using demand weighted averages. However, as the focus for rail was on local area trips (within West Yorkshire), as opposed to the longer distance trips, the longer distance rail trip records were ignored. For rail, the main focus was to ensure that the local shorter-distance trip proportions were accounted for, whereas the longer- distance trips were of less of an issue within the PT model. The overall impact is that the longer-distance rail trips will comprise a larger proportion of concessionary travellers in the model than was observed. The trips in the concessionary business matrix were set to zero because there were very few concessionary business purpose trip survey records. Concessionary commuting travellers also represented a small proportion; however it was decided to retain the proportions from the survey records as they represented a larger proportion than the business concessionary proportion and were considered significant. Table 21 below shows the proportions used to form the two matrices for the PT model.
Table 21: Merged Bus and Rail Concessionary Traveller proportions
Concessionary Travellers
Purpose Income AM IP PM
No Yes No Yes No Yes
Low 0.981 0.019 0.929 0.071 0.932 0.068
Commute Medium 0.989 0.011 0.917 0.083 0.964 0.036
High 0.994 0.006 0.958 0.042 0.983 0.017
Education All 0.895 0.105 0.900 0.100 0.933 0.067
HB Business All 1.000 0.000 1.000 0.000 1.000 0.000
Low 0.443 0.557 0.446 0.554 0.568 0.432
HB Other Medium 0.677 0.323 0.652 0.348 0.874 0.126
High 0.850 0.150 0.787 0.213 0.897 0.103
NHB Business All 1.000 0.000 1.000 0.000 1.000 0.000
Low 0.849 0.151 0.740 0.260 0.870 0.130
NHB Other Medium 0.928 0.072 0.844 0.156 0.939 0.061
High 0.989 0.011 0.917 0.083 0.933 0.067
These proportions were then applied to form a set of PT model matrices by journey purpose and income which were then further aggregated to form the required PT model matrices for assignment. Table 22 below shows the combined LTM_PT matrix Origin-Destination trip totals. Table 22: Combined Demand Matrix Totals (Average Hour) Period Non-concessionary Concessionary Total
AM 20,337 1,866 22,202
IP 14,761 4,945 19,706
PM 23,280 2,285 25,565
7 Network Development
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7 Network Development
7.1 Network Overview This section describes how the LTM_PT model network was constructed, specifying the development of the network, transit services and non- transit modes. Link types: • bus network; • rail network; • walk network; and • centroid connectors
Transit modes: • bus; and • rail;
Non transit modes: • walk; and • car (centroid connectors only).)
The LTM_PT network was based on the NGT model network built in 2008 as part of the Leeds NGT study. This comprised a bus network which was combined with a newly developed rail network to form the LTM_PT. The bus network taken from the NGT model was revised and checked for use in the LTM_PT. Bus network link and node numbering was retained from the NGT model so that comparisons could be made against the previous modelling work. To fulfil the public transport model’s role within the Leeds multi-modal model (LTM),) significant revisions were necessary to create the new public transport network. These were as follows: • revise network coding to match highway model; • update the zoning system and centroid connectors; • review and update the bus services; and • add the rail network and services.
In CUBE Voyager there are three main elements involved in a public transport network. These are as follows and discussed in more detail later in this chapter: • a representation of the physical network, either highway or track, that the vehicles run on; • the details of the individual bus or rail services, representing routes, service times and stops; and • a representation of the physical walk network that enables passengers to access or transfer between services.
Figure 9 shows LTM_PT public transport bus and rail network.
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Figure 9: Public Transport Network in Leeds
7.2 Links A summary of all the link types and attributes included in the model is provided in Table 23.
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Table 23: Link Types Link Type Class Distance (km) Speed (kph) Time (mins) Capacity
Bus Network 1 Varies > 0 Varies > 0 Distance*60/Speed 99999
Rail Network 21 Varies > 0 Varies > 0 Obtained from Timetable 99999
Walk Network 33 Same as bus 4.8 Distance*60/Speed 99999
Walk Network to Bus 32 Varies >0 4.8 Distance*60/Speed 99999 Network (Walk)
Walk Network to Rail 34 Varies >0 4.8 Distance*60/Speed 99999 Network (Walk)
Centroid Connector to 30 Varies < 2 4.8 Distance*60/Speed 99999 Bus (Walk)
Centroid Connector to 31 Varies >= 2 40 Distance*60/Speed+10 99999 Rail (Drive)
7.3 Nodes In creating the LTM_PT, the node numbering was maintained with the NGT model and where new network was added, node numbering conventions and sequences were retained. The nodes represent the following: • zones; • bus stops; and • rail stations. Each of the individual bus stops and rail stations in Leeds are represented by a node. 7.4 Links to the Highway Model Network The LTM_PT network incorporates link travel time information from the LTM_H. To do this, a linkage was formed between the two model networks. This was concentrated on the bus corridors and in the city centre where link correspondence was set up to allow information to be transferred from the highway to the PT model network. 7.5 Bus Network and Services The base bus network was obtained from SDG at the outset of the model development work. It had been constructed using an existing bus registration GIS database held by Metro. For each service, the database provided a list of stops the service passes through, with associated grid references. SDG used this database to develop a complete network structure, with nodes representing each individual bus stop and links representing the highway network joining these stops. The bus services in the model represent all services within Leeds; outside of this area services were only included that connect other areas to Leeds. A considerable degree of network cleaning and simplification was required since the data held within the GIS databank was considerably greater than that required for the Voyager model. Bus services were checked and, as the data received did not include service stopping points; these were coded in and applied to the model bus services. To ensure that the current model reflected any changes in services implemented since the development of the original NGT model, a revised copy of the COSA (2008) database was obtained from Metro and the services checked to identify where changes had occurred. Each bus operator was allocated a separate identity code for services within the model to allow model outputs to be interrogated by operator. As the bus network had been formed based on linking routes between bus stops, it consequently differed from the LTM_H SATURN highway network as the LTM_PT nodes were defined by bus stop location rather than junction location. While this does not pose any particular problem, it does mean that there is a small degree of inaccuracy where journey time sections are matched between the two models. The AVL data was used to determine bus speeds in the model. Links were coded to correspond with the AVL section start to end times from where an average speed was calculated for each section. For sections off the main radial routes, and average AVL
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speed of 23 kph was used. In the city centre, speeds were set to 9 kph which was set using judgement based on a review of cross city time tabled times. Bus speeds on the network were initially related to corresponding highway speeds and, based on this, speeds within the Leeds city centre area were set at 20 kph and outside this area at 35 kph. The bus corridor speeds were then further refined using journey time factors derived by comparing the PT model speeds with the highway model. Two bus vehicle types were defined as shown in Table 24 below. below. Bendy buses were modelled as double-decker vehicles. Note that the crush capacity (maximum seating and standing capacity) has been set very high to ensure that crowding effects were not modelled. Table 24: Bus Vehicle Types Vehicle Type Line Seat Capacity Crush Capacity
31 Single Decker Bus 50 999
32 Double Decker Bus 80 999
7.6 Rail Network and Services The rail network represents all METRO lines and stations. Outside of the METRO area the network coverage is less detailed representing only major stations on connecting routes into Leeds where one rail station per model zone. Rail services, their journey times and headways were obtained from 2008 weekday timetable information. The model includes the following rail operators representing all franchises that operate within or though Leeds: • Northern Rail; • Transpennine Express; • East Coast; • Cross Country; and • Transfer.
Note that the ‘Transfer’ operator represents combined services included in the model that use non-metro stations and where a passenger would require at least one transfer to undertake a journey. These services were modelled with a runtime which takes into account the entire journey including transit times, transfer penalties and wait times - the latter having been estimated based on timetabled information. Each service has been allocated a vehicle type which defines its speed and capacity as shown in Table 25. The information set out has been taken from an AECOM rolling stock analysis undertaken in 2007/8. Note that DMU = Diesel Multiple Unit and EMU = Electric Multiple Unit in the table.
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Table 25: Rail Service Capacities Vehicle Type Rolling Stock Type Speed (mph) Seat Capacity Crush Capacity
1 144 DMU 75 99 177
2 142 DMU 75 106 190
3 170 DMU 100 124 226
4 158 DMU 90 138 280
5 150 DMU 75 149 273
6 144(3) DMU 75 157 293
7 155 DMU 75 160 268
8 185 DMU 100 181 329
9 142+144 DMU 75 205 367
10 158(3) DMU 90 210 426
11 150+153 DMU 75 224 404
12 153+155 DMU 75 235 399
13 170+170 DMU 100 248 451
14 221 DMU 125 250 455
15 142+150 DMU 75 255 463
16 158+158 DMU 90 276 560
17 150+150 DMU 75 298 546
18 321 EMU 100 307 567
19 333 EMU 100 360 648
20 185+185 DMU 100 362 659
21 91+Mk3 LOCO-HAULED 125 531 966
22 HST LOCO-HAULED 125 539 981
7.7 Walk Network The model was structured with a separate walk network. This is consistent with the NGT model developed by SDG. The walk network is essentially a shadow copy of the bus network running adjacent with connecting links to bus stops and rail stations. Only walking is allowed using non-transit leg modes as described in the following section. 7.8 Network Access This section describes the centroid connectors and walk network that provide access to and transfer between the transit modes and services. 7.8.1 Centroid Connectors Given that the existing NGT model zoning system was replaced with the LTM zoning system, it was necessary to remove the existing zone connectors and replace them with new ones defined as follows: • walk to bus; • walk to rail; and • drive to rail.
Walk to bus : Connectors were included to all bus stops located within a given zone. Connectors were also included to any other stops within 400m; however, an exception was made for zones in the centre of Leeds where an excessive number of connectors would have resulted and where this rule was not therefore applied (access to other stops is provided by the walk network). Walk to rail : Connectors to the nearest rail station were included for a given zone within a 1km radius.
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Drive to rail : Connectors to the nearest rail station were included for a given zone outside of a 1km radius. Additional connectors were included on the periphery of the model to non-Metro stations to provide greater route choice within the model. Connection between zones and public transport services (bus stops/stations) occurs via non-transit leg modes. The assumptions on maximum access times and length of access used to generate these connections (termed non transit legs in Voyager) are shown in Table 26 below. Table 26: Non-Transit Legs - Network Access from Centroids Centroid Connector Travel Cost Max No Non-Transit User class Non transit Leg Expression Max Legs
Walk Time (Walk Speed = 4.8 Walk: Zone to Bus 30 30 mins 12 kph)
Drive Time +10 mins (Drive Drive Zone to Bus 31 60 mins 1 Speed = 4.8 kph)
Varies depending on rail station Walk: Zone to Train 30 80 mins 4 (Walk Speed = 4.8 kph)
Varies depending on rail station Drive Zone to Train 31 500 mins 2 location (Drive Speed = 40 kph)
7.8.2 Transfers The criteria used to generate non-transit legs for transfers are set out in Table 27 below. Table 27: Non-Transit Legs - Transfers Transfer Non transit Travel Cost Max No Non-Transit User class Leg Expression Max Legs
Bus to Rail (Leeds Walk Time (Walk Speed = 4.8 32 12.5 mins 2 Station) kph)
Bus to Rail (Other) 32 Walk Time (Walk Speed = 4.8 8 mins 3 kph) Bus to Bus (City centre 32 Walk Time (Walk Speed = 4.8 12.5 mins 20 only) kph)
Rail to Rail 32 Walk Time (Walk Speed = 4.8 12.5 mins 3 kph)
The maximum cost of 12.5 minutes equates to a travel time of 1km which was considered a reasonable maximum distance for a passenger to walk during an interchange. To make the model more manageable by reducing the model run times, the transfer non-transit legs were restricted. Careful analysis was carried looking at where interchanges occurred throughout the model network, and it was found that the key interchanges locations were focused in Leeds city centre. Bus-bus interchanges were therefore restricted to Leeds city centre and other mode-mode transfers were restricted to a maximum of either two or three non-transit leg options. The route enumeration parameters were also changed to impact on the interchanging behaviour in the model, as described in the section below.
8 Calibration and Validation
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8 Calibration and Validation
8.1 Model Calibration WebTAG Unit 3.11.2 offers the following means of calibrating an assignment model: • adjustments may be made to the zone centriod connector times and costs; • adjustments may be made to the network detail and any service amalgamations made in the interests of simplicity may be reconsidered; • the in-vehicle time factors may be varied; • the interchange penalty may be varied; • the parameters used in the trip loading algorithms may be modified; • the path building and trip loading algorithms may be changed; and • the demand may be segmented by ticket type. 8.1.1 Network and Services A validation of the services was undertaken by comparing the number of buses in the model with the observed roadside counts recorded in the survey data. The results demonstrate that the model compared reasonably against the count data. Table 28 below provides a summary of the model – observed data differences on the bus corridors; the full tables by individual corridor are set out in Appendix A. Table 28: Model –Bus Passenger Counts Compared with Modelled Flows Sites with a Sites with a Time Period Direction Average % Difference difference <=15% difference <=25%
Inbound -7% +53% +88% AM Peak Outbound +7% +29% +100%
Inbound -8% +63% +87% Inter Peak Outbound -9% +50% +92%
Inbound +0% +38% +83% PM Peak Outbound -9% +53% +100%
8.1.2 Journey Time Calibration Highway model journey times from the LTM_H were transferred across to the PT model and incorporated as factors on the bus corridors. The aim was to adjust link speed in line with the congested times reflected in the highway model. 8.1.3 Matrix Calibration Figure 10 shows the initial matrices (before the model was calibrated) assigned bus corridor flows compared against the observed data. It shows a good comparison between the prior matrices and the observed data.
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Figure 10: Initial Matrices Compared With Observed Count Data
However, the following observations were made: • the demand in the IP period seemed too high; and • the demand along the Woodhouse Lane corridor was too low. Therefore, to bring the demand closer to the observed data, factors were applied as set out in Table 29 below. Table 29: Matrix Adjustments AM IP PM
Global matrix adjustment 1.0 0.9 1.0
Woodhouse Lane adjustment 1.1 1.1 1.1
Following these changes, some further matrix amendments were made on an individual corridor and model period basis to better match the observed data. Figure 11 illustrates the magnitude of the matrix demand changes at a sector level. The sectors were defined in section 4.
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Figure 11: Scatter Graph of Sectored Initial and Adjusted Matrix Demand
Figure 12, Figure 13 and Figure 14 illustrate the trip length distributions before and after these adjustments. For the AM, there are no changes. As illustrated the changes in the IP and PM are small. The adjusted matrix distributions show a reasonable pattern of trips. Figure 12: AM Matrix - Trip Length Distribution
AM Matrix Mean Trip Length: Adjusted 18.4 km (Initial 18.4 km)
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Figure 13: IP Matrix - Trip Length Distribution
IP Matrix Mean Trip Length: Adjusted 16.8 km (Initial 16.9 km)
Figure 14: PM Matrix - Trip Length Distribution
PM Matrix Mean Trip Length: Adjusted 20.3 km (Initial 20.8 km)
8.2 Centriod Connector Changes Centriod connectors were investigated as part of the calibration process. Where it was considered that the connectors were unnecessarily restricting valid routes or favouring unrealistic routes, changes were made to improve the model.
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8.3 Matrix Validation The matrix was validated using the bus corridor count data and Leeds central station boarding and alighting data. The bus corridors collectively form a cordon capturing all movements into and out of Leeds by bus, which, together with the rail movements at Leeds central station, represent all modelled public transport demand into and out of Leeds. Table 30 shows the combined bus and rail station trips crossing the cordon compared against the observed data. These results demonstrate the matrix has been well calibrated with all modelled trips within 15% of the observed data. Table 30: Combined Cordon Trips: Bus and Leeds Station Trips Time Period Direction Observed Trips Model Trips Diff % diff
AM Peak Inbound 16,240 14,997 -1243 -8%
Outbound 3,968 3,900 -67 -2%
Inter Peak Inbound 7,384 7,340 -44 -1%
Outbound 7,717 6,854 -864 -11%
PM Peak Inbound 5,719 5,849 +130 +2%
Outbound 16,788 16,187 -601 -4%
8.4 Assignment Validation The assignments were validated using inbound and outbound bus flows on the individual corridors. Detailed results comparing modelled and observed flows on each crossing point are set out in the Appendix. Table 31 provides a summary of the proportion of modelled flows within a 25% difference of the observed data and where the observed flow is in excess of 150 passengers. This demonstrates that the bus flows reproduce the observed counts on the individual corridors in the majority of cases, but that not all sites satisfy the validation criterion. Table 31: Numbers of Bus Locations Meeting Validation Criteria Sites with flow >150 Number of sites Proportion of sites Time Period Direction pax/hr within 25% within 25%
AM Peak Inbound 17 15 88%
Outbound 7 7 100%
Inter Peak Inbound 16 14 88%
Outbound 14 13 93%
PM Peak Inbound 13 11 85%
Outbound 17 17 100%
Table 32 below shows the results of boarding and alighting trips at the minor rail stations in Leeds. Values are shown where observed counts are greater than 150 passengers/hour. Table 32: Minor Rail Station Boarding and Alighting Time Period Direction Observed Volume Assigned Volume Diff % Diff
AM Peak Alighting - - - -
Boarding 406 397 -9 -2%
Inter Peak Alighting - - - -
Boarding - - - -
PM Peak Alighting 570 519 -50 -9%
Boarding - - - -
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Bus journey times were validated using the AVL data which provides run time information on radial sections connecting peripheral area locations of the Leeds district with the city centre. The results are summarised in Table 34 below and shown in more detail in the Appendix. Overall the journey times in the model on each of the AVL sections are <=5% of the observed times. Table 33: Numbers of Bus Locations Meeting Validation Criteria
Time Period Direction Mean % dif Min % dif Max % dif
AM Peak Inbound 0% -3% +2%
Outbound 0% -1% +2%
Inter Peak Inbound 0% -5% +3%
Outbound 0% -4% +2%
PM Peak Inbound 0% -4% +3%
Outbound 0% -5% +5%
Excess demand was monitored to ensure that the crowding model was reaching a reasonable level of convergence. No excess demand was reported in any of the model periods.
9 Summary of the Model Development and Standard Achieved
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9 Summary of the Model Development and Standard Achieved
9.1 Summary of Model Development This report has described the process of constructing the public transport model (LTM_PT) within the LTM suite of models. It sets out the position reached in July 2011 when the model calibration was finalised. The model at that stage had been shown to validate well against observed data to provide a good representation of the public transport conditions in Leeds. 9.2 Summary of Standard Achieved Guidance set out in WebTAG Unit 3.11.2 has been applied in developing the model, including: • data collection; • model calibration; and • validation. 9.2.1 Data Collection The survey data were collected by SDG in 2008 and provided comprehensive data on key bus routes into Leeds city centre and at the Leeds rail station. Collectively these sites form a cordon capturing public transport trips into and out of Leeds. The survey was designed to generate a good sample that could be expanded using count data at corresponding site locations with reasonable confidence that the demand was representative of the actual situation. It has been identified that the data used to develop the model maybe limited in term of the bus survey design. There is a risk that not everyone was given a form and that some individuals might have been missed during the survey; however, no count data were collected at the time to identify or to correct this potential issue. For the rail data, Leeds station was surveyed as part of a questionnaire survey; however, for the minor stations in Leeds, a different approach was taken. Northern Rail data were used for the minor station and, as a result, the quality of rail data will be better for central Leeds. 9.2.2 Matrix Development The matrices were created in a logical manner, with separate matrices built for bus and rail and combined into an estimate of public transport demand. The matrices derived for the LTM demand model were disaggregated by journey purpose car availability and income using factors derived from the survey data. For the LTM_PT the demand was segmented into non- concessionary and concessionary ticket users so that fare changes could be tested as part of the NGT scheme proposals. 9.2.3 Network and Service Development The bus network and services were developed from networks created for the NGT model by SDG. These were reviewed, adapted to the LTM zone system and updated to the 2008 base year. Care was taken to ensure compatibility where possible with the NGT model as part of supporting the business case development. Rail network and services were coded. These were included to represent the entire rail network in the Metro area and to provide connectivity with stations serving routes into Leeds. Timetabled information was used to create the rail services. A validation exercise was carried out to check the services by comparing the number of buses in the model with roadside counts. 9.2.4 Model Validation The matrices validate reasonably well against the count data. The validation criteria have been fully satisfied with all screen line flows within 15% of the observed values. All network and services were validated to ensure that the modelled frequencies corresponded to the counts and journey times. The assignment validation demonstrates that the majority of differences between modelled flows and counts on individual corridors were within validation criterion. However some differences were outside of the 25% validation criteria. The model meets the validation standards set out in Web TAG Unit 3.11.2 in terms of screen line flows, but not for all of the individual corridor flows.
Appendix A – Validation Results
-
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Appendix A – Validation Results
Tables Comparison of Counts and Assigned Volumes by Corridor (Passengers per hour)
AM PEAK VOLUMES – INBOUND Corridor Count Modelled % Difference Woodhouse Lane 1,045 1,176 +12.5% Meanwood Road 280 197 -29.6% Scott Hall Road 452 418 -7.5% Chapeltown Road 561 661 +18.0% Roundhay Road 390 319 -18.2% Harehills Road 359 294 -18.0% Compton Road 498 462 -7.1% A64 York Road 1,136 1,039 -8.6% Lavender Walk 32 12 A61 Hunslet Road 852 685 -19.6% A653 Dewsbury Road 608 616 +1.3% Top Moor Side 515 524 +1.8% A58 Domestic Road 220 182 -17.2% B6154 Wellington Road 631 509 -19.3% A647 Armley Road 610 637 +4.5% Kirkstall Road 633 628 -0.8% Burley Road 666 448 -32.8% Moorland Road 292 276 -5.3%
Total 9,779 9,084 -7.1%
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AM PEAK VOLUMES – OUTBOUND Corridor Count Modelled % Difference Woodhouse Lane 342 413 +20.8% Meanwood Road 82 105 Scott Hall Road 103 91 Chapeltown Road 200 154 -22.9% Roundhay Road 126 122 Harehills Road 121 13 2 Compton Road 134 77 A64 York Road 325 350 +7.8% Lavender Walk 37 9 A61 Hunslet Road 313 266 -15.2% A653 Dewsbury Road 288 288 -0.1% Top Moor Side 74 149 A58 Domestic Road 78 84 B6154 Wellington Road 187 226 +20.7% A647 Armley Road 91 160 Kirkstall Road 204 173 -15.3% Burley Road 134 162 Moorland Road 39 105
Total 2,877 3,066 +6.6%
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INTER PEAK VOLUMES – INBOUND Corridor Count Modelled % Difference Woodhouse Lane 627 641 +2.2% Meanwood Road 168 129 -23.2% Scott Hall Road 22 3 247 +10.9% Chapeltown Road 318 333 +4.9% Roundhay Road 226 162 -28.5% Harehills Road 218 192 -11.9% Compton Road 306 238 -22.1% A64 York Road 596 584 -1.9% Lavender Walk 20 12 A61 Hunslet Road 490 446 -9.1% A653 Dewsbury Road 378 384 +1.6% Top Moor Side 248 226 -9.0% A58 Domestic Road 114 147 B6154 Wellington Road 372 254 -31.5% A647 Armley Road 228 252 +10.7% Kirkstall Road 404 317 -21.6% Burley Road 367 281 -23.3% Moorland Road 200 205 +2.7%
Total 5,502 5,051 -8.2%
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INTER PEAK VOLUMES – OUTBOUND Corridor Count Modelled % Difference Woodhouse Lane 697 602 -13.6% Meanwood Road 127 135 Scott Hall Road 221 207 -6.4% Chapeltown Road 285 225 -20.9% Roundhay Road 226 199 -11.5% Harehills Road 223 213 -4.5% Compton Road 332 212 -36 .4% A64 York Road 646 789 +22.1% Lavender Walk 23 13 A61 Hunslet Road 569 490 -14.0% A653 Dewsbury Road 494 378 -23.5% Top Moor Side 120 164 A58 Domestic Road 127 154 B6154 Wellington Road 389 327 -16.0% A647 Armley Road 254 192 -24.4% Kirkstall Road 337 264 -21.6% Burley Road 357 352 -1.3% Moorland Road 174 180 +3.3%
Total 5,602 5,097 -9.0%
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PM PEAK VOLUMES – INBOUND Corridor Count Modelled % Difference Woodhouse Lane 405 477 +17.9% Meanwood Road 135 90 Scott Hall Road 122 75 Ch apeltown Road 192 211 +9.7% Roundhay Road 219 200 -8.5% Harehills Road 190 136 -28.3% Compton Road 220 213 -3.3% A64 York Road 477 513 +7.5% Lavender Walk 41 25 A61 Hunslet Road 450 339 -24.6% A653 Dewsbury Road 373 365 -2.2% Top Moor Side 270 40 0 +48.1% A58 Domestic Road 140 296 B6154 Wellington Road 312 253 -18.9% A647 Armley Road 169 237 +15.2% Kirkstall Road 280 237 -15.5% Burley Road 277 211 -23.8% Moorland Road 136 140
Total 4,406 4,417 +0.3%
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PM PEAK VOLUMES – OUTBOUND Corridor Count Modelled % Difference Woodhouse Lane 1,450 1,197 -17.5% Meanwood Road 345 424 +22.9% Scott Hall Road 467 412 -11.7% Chapeltown Road 659 596 -9.5% Roundhay Road 530 469 -11.4% Harehills Road 395 322 -18.3% Compton Road 554 478 -13.7% A64 York Road 1,320 1,116 -15.4% Lavender Walk 24 4 A61 Hunslet Road 1,025 1,211 +18.2% A653 Dewsbury Road 886 734 -17.2% Top Moor Side 420 367 -12.7% A58 Domestic Road 367 357 -2.8% B6154 Wellington Road 675 572 -15.3% A647 Armley Road 524 533 +1.7% Kirkstall Road 643 512 -20.3% Burley Road 801 812 +1.3% Moorland Road 357 314 -12.0%
Total 11,441 10,431 -8.8%
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Bus Service Travel Times (minutes) AM PEAK TIMES – INBOUND Section Observed Modelled Difference % Difference
A58S 10.20 10.2 -0.0 0%
A58N_1 16.91 16.8 -0.1 -1%
A58N_2 16.59 16.6 -0.0 0%
A61N 25.31 25.5 +0.2 1%
A61S 15.47 15.8 +0.4 2%
A62 8.62 8.5 -0.1 -1%
A63_A64 18.38 18.6 +0.2 1%
A64 15.11 14.7 -0.4 -2%
A65 28.60 29.0 +0.4 1%
A639 19.29 18.8 -0.5 -3%
A643 15.88 15.87 -0.0 0%
A653 17.23 17.3 +0.1 0%
A657_A65 34.22 33.7 -0.5 -1%
A660 29.49 29.5 +0.0 0%
B6154 20.35 20.3 -0.0 0%
B6157_A647 28.15 28.1 -0.0 0%
Burley Rd 25.29 25.3 -0.0 0%
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AM PEAK TIMES – OUTBOUND Section Observed Modelled Difference % Difference
A58S 6.0 6.1 +0.0 0%
A58N_1 12.7 12.7 -0.1 -1%
A58N_2 13.1 13.2 +0.1 0%
A61N 16.7 16.7 +0.1 0%
A61S 11.2 11.3 +0.1 1%
A62 6.5 6.5 -0.0 0%
A63_A64 15.1 15.4 +0.2 1%
A64 14.3 14.1 -0.2 -1%
A65 20.3 20.6 +0.3 2%
A639 11.8 11.6 -0.1 -1%
A643 18.8 18.7 -0.1 0%
A653 12.1 12.1 -0.1 -1%
A657_A65 24.9 25.1 +0.2 1%
A660 21.3 21.2 -0.1 -1%
B6154 16.2 16.3 +0.0 0%
B6157_A647 24.0 24.0 -0.0 0%
Burley Rd 17.4 17.4 +0.0 0%
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IP PEAK TIMES – INBOUND Section Observed Modelled Difference % Difference
A58S 6.00 6.0 -0.0 0%
A58N_1 15.38 15.2 -0.2 -1%
A58N_2 14.42 14.3 -0.1 -1%
A61N 22.45 22.6 +0.1 0%
A61S 9.83 10.0 +0.2 2%
A62 5.79 5.8 -0.0 -1%
A63_A64 14.45 14.9 +0.4 3%
A64 12.86 12.3 -0.6 -5%
A65 19.49 19.6 +0.1 1%
A639 11.69 11.4 -0.2 -2%
A643 11.36 11.32 -0.0 0%
A653 14.26 14.3 +0.0 0%
A657_A65 21.80 22.1 +0.3 1%
A660 28.47 28.6 +0.1 0%
B6154 17.99 18.0 -0.0 0%
B6157_A647 22.85 22.8 -0.0 0%
Burley Rd 20.39 20.4 -0.0 0%
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IP PEAK TIMES – OUTBOUND Section Observed Modelled Difference % Difference
A58S 5.8 5.8 +0.0 0%
A58N_1 14.4 14.3 -0.1 -1%
A58N_2 13.7 13.7 -0.0 0%
A61N 16.6 16.6 +0.1 0%
A61S 9.5 9.7 +0.2 2%
A62 5.4 5.4 -0.0 0%
A63_A64 14.4 14.7 +0.3 2%
A64 14.1 13.7 -0.3 -2%
A65 19.2 19.3 +0.2 1%
A639 11.1 10.7 -0.3 -3%
A643 18.0 17.2 -0.8 -4%
A653 13.0 13.0 -0.0 0%
A657_A65 22.3 22.7 +0.4 2%
A660 24.7 24.6 -0.1 0%
B6154 18.0 18.1 +0.1 0%
B6157_A647 24.2 24.2 -0.0 0%
Burley Rd 17.6 17.6 +0.0 0%
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PM PEAK TIMES – INBOUND Section Observed Modelled Difference % Difference
A58S 6.79 6.8 -0.0 0%
A58N_1 14.59 14.4 -0.2 -1%
A58N_2 13.64 13.6 -0.1 0%
A61N 21.58 21.7 +0.1 1%
A61S 11.30 11.7 +0.3 3%
A62 7.01 7.0 -0.1 -1%
A63_A64 13.94 14.3 +0.3 2%
A64 12.19 11.7 -0.5 -4%
A65 21.00 21.7 +0.7 3%
A639 14.91 14.4 -0.5 -3%
A643 12.02 11.98 -0.0 0%
A653 13.74 13.8 +0.0 0%
A657_A65 27.25 26.7 -0.6 -2%
A660 28.28 28.4 +0.1 1%
B6154 16.63 16.7 +0.0 0%
B6157_A647 23.38 23.4 -0.0 0%
Burley Rd 20.53 20.5 -0.0 0%
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PM PEAK TIMES – OUTBOUND Section Observed Modelled Difference % Difference
A58S 6.6 6.6 -0.0 0%
A58N_1 16.9 16.8 -0.0 0%
A58N_2 16.8 16.9 +0.1 1%
A61N 23.1 23.0 -0.1 0%
A61S 12.6 13.2 +0.6 5%
A62 6.1 6.1 -0.0 0%
A63_A64 16.5 16.4 -0.1 0%
A64 14.5 14.5 +0.0 0%
A65 24.9 25.1 +0.3 1%
A639 17.5 16.6 -0.9 -5%
A643 19.6 18.7 -0.9 -4%
A653 15.4 15.3 -0.0 0%
A657_A65 29.4 29.5 +0.1 0%
A660 35.0 34.8 -0.2 -1%
B6154 18.7 18.7 +0.0 0%
B6157_A647 28.3 28.3 +0.0 0%
Burley Rd 23.0 22.9 -0.0 0%
Appendix B – Rail Station Inventory
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Appendix B – Rail Station Inventory
List of rail stations included in the model network outside of West Yorkshire: • London; • Bristol; • Swansea; • Gloucester; • Milton Keynes; • Birmingham; • Leicester; • Peterborough; • Stafford; • Stoke-on-Trent; • Derby; • Nottingham; • Liverpool; • Manchester; • Preston; • Barrow-in-Furness; • Carlisle; • Glasgow; • Edinburgh; • Newcastle; • Middlesbrough; • York; • Scarborough; • Hull; • Driffield; • Skipton; • Harrogate; • Knaresborough; • Weeton; • South Milton; • Church Fenton; • Gilberdyke; • Eastington; • Wressle; • Selby; • Goole; • Rawcliffe; • Hensall; • Whitley Bridge; • Doncaster; • Barnsley; • Panistone; • Roterham; • Meadowhal;l • Sheffield; • Hatfield & Stainforth; • Scunthorpe; • Hathersage; and
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• Newark-on-Trent
Appendix C - Fares
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Appendix C - Fares
1. Overview This section describes the fare systems constructed for the LTM_PT. Fares have been represented in the following four components
• rail fares – matrix based; • First Bus fares – distance based; • Arriva Bus fares – distance based; and • Free City Bus – free travel.
In the model base year (2008), First Bus and Arriva operated the majority of services in the Leeds area, and while others exist the decision was made to maintain a degree of simplicity and reflect fares based on these two operators only. Consideration was given to merging the two bus fare curves to improve the cross-city fares which involve trips on both operators. These would generally be made using a Metro card rather than paying two separate fares.. However, this was discounted due to the very low proportion of trip demand undertaking cross city movements.
The Free City Bus recently introduced a charge for non concessionary travellers (50p flat fare); however for 2008 travel is free.
The public transport model demand is segmented into fare paying passengers and concessionary passengers. The bus fare systems described within this section were applied to the fare paying segments only except in the AM peak where concessionary travellers have to pay full fares. Rail fares were applied to both segments in all time periods.
2. Rail Fares It was assumed that rail travel to/from and across Leeds and the wider West Yorkshire Metro area was strongly influenced by Season ticket use. This was supported by a review of the Leeds station questionnaire survey data revealing that the majority of non-concessionary tickets used on a weekday for travel within the West Yorkshire area were Season tickets. Outside of the Metro area, fares were derived based on half-returns. The two separate components were combined in a single fare matrix.
Each rail station was allocated a rail fare zone. Within the Metro area the zones follow the Metro zones (with the exception of zone 2 as explained later). Outside of the Metro area each station is represented as a separate zone.
All rail fares have been converted to 2002 prices and a 2008 model base year using the RPI +1% rule.
2.1 Internal Metro area rail trips Metro cards were available on a weekly, monthly, quarterly and annual basis for travel entitlement within up to 7 defined zones. Assuming they are used regularly i.e. every weekday for 2 trips per day (inbound and return), they offer better value compared to single and return tickets. Figure 15 shows the Metro rail zones and Table 34 the current Metro card prices.
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Figure 15: Metro Rail zones
AECOM Leeds Public Transport Model LMVR 75
Capabilities on project: Transportation
Table 34: Metro card prices (2011) Metro card Weekly Monthly Quarterly Annual Zones 1 – 3 £23.00 £88.00 £260.00 £907.00 Zones 1 – 4 £27.50 £104.00 £306.00 £1,067.00 Zones 1 – 5 £32.50 £122.00 £365.00 £1,280.00 Zon es 1 – 6 £34.00 £131.00 £393.00 £1,363.00 Zones 1 – 7 £39.60 £152.50 £457.50 £1,587.00 Zones 2 – 5 £22.80 £86.00 £254.00 £900.00
Table 35 shows the current Metro card weekly fares which have been used to derive 1-way trip fares assuming that they are used every weekday for 2 trips per day. These form the basis of the internal Metro area rail fares.
Table 35: Metro card – 1 way trip assumptions (2011) Metro card Weekly Days used Trips/ day 1-way trip Zo nes 1 – 3 £23.00 5 2 £2.30 Zones 1 – 4 £27.50 5 2 £2.75 Zones 1 – 5 £32.50 5 2 £3.25 Zones 1 – 6* £34.00 5 2 £3.40 Zones 1 – 7* £39.60 5 2 £3.96 Zones 2 – 5 £22.80 5 2 £2.28 * Season tickets from Zones 6 and 7 were not available in 2008, and therefore these fares have been ignored for the base year fares.
Metro Rail zones 1 and 2 cover the Leeds area. Here, care was taken to ensure that fares were reflected accurately. To achieve this all half-return ticket fares were sourced from the National Rail website for travel between all stations within zones 1 and 2. Where the half-return fares were found to be cheaper than the 1-way trip fares shown in Table 35, they were used instead in the fares matrix. To support this, zone 2 was split in the model so that each zone 2 station occupied an individual zone. Otherwise, the model fare zones followed the Metro rail zones.
To account for the rail operator season, return and single ticket users, the Metro card fares were compared against other ticket type fares as shown in Table 36. Factors were then calculated taking the average fare differences for each ticket type weighted by proportions from the Leeds rail station survey as shown in Table 37. Table 36: Fares into Leeds by Ticket Type (2011) Rail Operator Station Metro card Half-Return Single Season Wakefield £2.30 £2.10 £2.50 £2.80 Keighley £2.75 £2.47 £3.40 £3.80 Huddersfield £3.25 £2.81 £3.65 £4.50 Harrogate £3.40 £3.37 £4.05 £7.00 Skipton £3.96 £3.97 £4.75 £7.90 Table 37: Proportion of Ticket Types Rail Operator Model Period Metro card Return Single Other Season AM 42% 26% 27% 3% 2% IP 20% 4% 62% 10% 4% PM 36% 14% 39% 7% 4%
AECOM Leeds Public Transport Model LMVR 76
Capabilities on project: Transportation
2.2 Other rail trips For external rail trips (with at least one trip end outside of the Metro area) it was important to focus on reflecting fares reasonably well to/from stations located on competing bus routes immediately outside of the Metro area. Half return fare-distance rates (£/km) were derived for AM peak tickets for the following stations into Leeds. • York; • Knaresborough; • Selby; • Barnsley; and • Penistone.
Undertaking some regression analysis on the trip distance and fares between these stations and Leeds Central station showed that the intercept (or boarding cost) was very close to zero (and actually below zero in the peak) therefore it was appropriate to base the fare on distance only.
A 0.17 £/km (2011 prices) fare rate per kilometre was obtained from a regression analysis. This was used in combination with the ticket type proportions shown in Table 37 to provide fares for the AM, IP and PM. Table 38 below shows the half-return and modelled fares calculated using the rates.
Table 38 External Rail Fares to Leeds (2011 prices) IP Modelled Peak Station Peak Half Return AM Modelled Peak Fare PM Modelled Peak Fare Fare York £7.15 £5.22 £5.84 £5.50 Knaresborough £4.50 £4.06 £4.54 £4.28 Selby £4.80 £4.78 £5.34 £5.03 Barnsley £4.55 £4.07 £4.55 £4.29 Penistone £5.15 £4.57 £5.11 £4.82
Table 38 above demonstrates that the fare rates provide a good starting point at least for the external trip rail fares in the model. However if it is found during model calibration that further detail is required then these fares will be refined.
3. Bus Fares Bus services in Leeds were dominated by First and Arriva. They have different fare structures and were therefore treated as two separate fare systems. Other service operators also exist but make up only a very small proportion of services in the model and were therefore allocated the fare structure (First or Arriva) that was closest to their fares. The Leeds Free City Bus was however allocated a separate free travel fare. The Leeds Free City Bus operates in the city centre.
All bus fares were converted to the 2002 prices using RPI to back cast from current 2011 fares. First Bus fare table data provided by Leeds City Council was also used to derive a 2008 – 2011 fare growth factor which was applied to both First and Arriva fares.
3.1 First Bus Services A review of First Bus tickets in the Leeds area revealed the following about fares: • a series of simple fare tickets were offered for single journey tickets including a short hop fare: typically 4 stops (£1), longer hop fare: within the green zone (£1.90), and hop across Leeds: any travel within Leeds (£2.50 peak / £2.20 off- peak); • First Day tickets allow more than one trip within West Yorkshire (£4.30), and Leeds (£3.40 off-peak); and • First season tickets on a weekly monthly 3 monthly and annul basis allow for travel within West Yorkshire (£18.50, £60, £175 and £620 respectively); a weekly ticket for travel within the green zone is available (£12).
The Green zone, Leeds area and West Yorkshire clearly form boundaries within the First Leeds Bus fare structures defining areas of travel entitlement – these were used as a basis for constructing the modelled fares.
AECOM Leeds Public Transport Model LMVR 77
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Figure 16 Existing First Bus Services in the Leeds area
It was decided that a distance-based fare system would be defined. This would be based on half return (short distance), First Day and weekly First season tickets. The distance-fare relationship would be derived based on trip distances and fares to/from Leeds city centre. This approach means that while the shorter distance fares will be reflected well for both cross-city trips and trips to/from Leeds, the First Day and season tickets fares would be reflected well for trips t/from Leeds but slightly less well for cross- city trips.
A more detailed zone-based system similar to rail, where fares could be specified between individual stops was ruled out due to the number of bus stops in the model and its potential complexity.
The distance-based fare relationship was created as follows. Single 1-way trip fares were derived for First Day and season ticket users assuming that 2 trips were made on a weekday basis and that season tickets were represented by the weekly season ticket price. These were then used to establish a distance-fare curve using half return fares from 2008 First bus fare table data provided by Leeds City Council up until First day or season ticket fare prices were reached. At this point, and up to a distance from Leeds city centre to the West Yorks border, a number of different fare costs were possible depending on ticket type (i.e. First Day/ Season, peak/ off-peak tickets). The Skipton – Leeds single fare was then used to extrapolate the relationship beyond the West Yorkshire boundary.
AECOM Leeds Public Transport Model LMVR 78
Capabilities on project: Transportation
Figure 17 illustrates the various components used to form the distance-fare relationship (2011 prices).
Figure 17 First Bus fare curve components
City Green Leeds WY Skipton centre zone
To form fare curves for the AM, IP and PM periods the season and non-season ticket components were merged based on weighted averages calculated using proportions taken from the Bus surveys as shown in Table 39 below.
Table 39 Bus Survey Ticket Type Proportions AM IP PM Single 26% 20% 23% Return 8% 14% 8% Season (Operator) 16% 12% 17% Season (Metro) 23% 24% 21% Day (Metro or Operator) 20% 26% 26% Other 7% 4% 5%
The peak and off-peak components were merged in a similar way using proportions from the bus demand matrices as shown in Table 40 below.
Table 40 Bus Demand Matrix Peak and Off-Peak Proportions
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AM IP PM Peak 83% 25% 44% Off-Peak 17% 75% 56%
Note that in the table above the AM factors were adjusted to account for peak only travel before 09:30, resulting in only a small proportion using off-peak tickets, 3% (the AM model period is 07:00 – 10:00).
Figure 18 shows the First Bus fare curves in 2002 prices.
Figure 18 First Bus Fare curves by model period
3.2 Arriva Bus Services A review of Arriva Bus tickets revealed the following about fares: • Single and return journey tickets are available but not predefined in terms of a rate per distance or rate per number of stops; • Day saver tickets allow for any travel within the Arriva West Yorkshire (£4.20) and Arriva Yorkshire defined areas (£5.00); • Season tickets are offered on a weekly basis for travel in the Arriva West Yorkshire (£16.00 weekly) and Arriva Yorkshire areas (£20.00 weekly);
Note that the Arriva West Yorkshire and Arriva Yorkshire boundaries do not follow the actual geographic boundaries.
To develop the Arriva bus fare system the same approach was used as that described for First Bus, involving a distance based relationship for fares to/ from Leeds city centre. Single 1-way trips were derived for the Day saver and season tickets listed above assuming weekday travel and 2 trips per day, and that season ticket fares were represented by weekly season ticket prices. For
AECOM Leeds Public Transport Model LMVR 80
Capabilities on project: Transportation
short distance single ticket fares a sample of fare prices was taken from the Arriva website and distances estimated to form a relationship. Figure 19 illustrates the distance-fare relationships and the various components considered in developing the fare curve. Figure 19: Arriva Bus fare curve components
West Yorks Arriva WY Arriva Yorks Boundary Boundary Boundary
The proportions presented earlier in Table 39 and Table 40 were used to merge the different components into single curves for each of the AM, IP and PM periods as shown in Figure 20 below in 2002 prices.
AECOM Leeds Public Transport Model LMVR 81
Capabilities on project: Transportation
Figure 20: Arriva Bus Fare curves by model period
4. Comments on the Bus Fares As the model is being developed with the purpose of testing the proposed NGT scheme, it is important that the fares represented on the NGT corridors are as realistic as possible. Combining the bus fares of the two operators was considered as an option to better reflect the cross-city movements and prevent two fares being incurred due to necessary interchanges between First (in the north) and Arriva services (in the south). It is assumed that regular travellers would use a Metro card instead. However an analysis of the demand in the model revealed that only a very small proportion of trips originated from the NGT north and south corridors undertake cross-city trips (approx. 1%). Therefore this option was discounted in favour of maintaining a better fare representation for the to/from Leeds city centre trips which are far more prevalent.
5. Fare Validation This section presents a comparison of actual and modelled fares as shown Table 41 below (2002 prices). It shows that the fares to/from Leeds city centre are reflected well. The cross-city fares are however reflected less well as discussed in section 4.
AECOM Leeds Public Transport Model LMVR 82
Capabilities on project: Transportation
Table 41: Fare Validation Modelled Fares Journey Observed Fares (2002 prices) Period (2002 prices) Description Zones Bus Rail Bus Rail First Day WY £1.42 AM £1.15 £0.99 First Week WY £1.22 First Week Green £0.79 Headingley - Leeds Half Return £0.99 116 - 18 IP £1.05 £0.99 City Centre First Day Leeds OP £1.12 Zone 1-3 Metro card £1.72 First Week WY £1.22 First Week Green £0.79 PM £1.07 £0.88
Hop Across Leeds £1.65 1 First Day WY £1.42 £2.27 AM First Week WY £1.22 £1.36 First Week Green £0.79 Bus Metro card £1.32 Headingley - Hunslet 116 - 328 £2.18 1 Hop Across Leeds £1.65 IP £1.18 First Day Leeds OP £1.12 First Week WY £1.22 £2.20 1 First Week Green £0.79 PM £1.23 Bus Metro card £1.32 Hop Across Leeds £1.65 AM £1.36 First Day WY £1.12 First Week WY £1.22 Adel - Leeds City 382 - 18 IP £1.18 Centre Hop Across Leeds £1.65 First Day Leeds OP £1.12 First Week WY £1.42 PM £1.23
Hop Across Leeds £1.65 1 First Day WY £1.42 AM £2.31 First Week WY £1.22 Bus Metro card £1.32
Adel - Rothwell 382 - 475 1 IP £2.44 Hop Across Leeds £1.65 First Day Leeds OP £1.12 First Week WY £1.22 PM £2.48 1 Bus Metro card £1.32
AM £1.19
Hunslet – Leeds City 328- 18 IP £1.19 Arriva Week WY £1.06 Centre
PM £1.18
AM £1.50 £2.77 Half Return £3.02 Harrogate - Leeds Harrogate-Leeds Gold Card Twelve 720 - 18 Northern Weekly Season City Centre Journey Ticket £1.65 £2.52 IP £1.50 £2.92
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PM £1.50 £2.84
AM £1.95 £3.90
Half Return £5.34 York - Leeds City 752 - 18 IP £1.95 £4.36 Leeds-York Go Twelve Ticket £1.54 Northern Weekly Season Centre £3.47
PM £1.95 £4.11
AM £1.25 £1.76
Wakefield - Leeds City Half Return £1.87 650 - 18 IP £1.26 £1.93 Arriva Week WY £1.06 Centre Zone 1-3 Metro card £1.72
PM £1.25 £1.84
1 Includes interchange between First and Arriva services
Comments on the fare validation:
• the Headingly – Hunslet and Adel – Rothwell modelled bus fares are high for trips where interchanges occur between First and Arriva bus. This demonstrates the issue with cross-city fares in the model as discussed in section 4; and • the rail fares from Harrogate and Wakefield to Leeds city centre show the modelled fares as less in the AM than the IP and PM. This is a function of the proportions used to calculate the average fare paid by a passenger (see Table 4). In the AM there is generally a greater use of Season tickets, which are cheaper on a ‘per trip’ basis than return or single tickets.