Application of Gtamodel V4.0 to a Range of Transit Scenario Analyses

Application of GTAModel V4.0 to a Range of Transit Scenario Analyses

Matthew David Austin

A thesis submitted in conformity with the requirements for the degree of Master of Applied Science Department of Civil Engineering University of Toronto

Application of GTAModel V4.0 to a Range of Transit Scenario Analyses

Matthew David Austin

Master of Applied Science

Department of Civil Engineering University of Toronto

2016 Abstract

This thesis investigates the use of GTAModel V4.0, an activity- and agent-based integrated operational travel demand model recently developed at the University of Toronto. It uses the model to analyze a wide variety of transit policy scenarios in order to evaluate the effectiveness and appropriateness of the use of the model at various scales of policy impact. Some of these policies were chosen in order to examine their impact on affordable transit improvements in a study area – South Etobicoke and the corridor connecting it to Downtown Toronto. The thesis illustrates that, while GTAModel V4.0 allows for new analysis of transit congestion and fare structures, the general scale of appropriate policy scenarios is unchanged from previous models.

Additionally, it provides suggestions for potential TTC improvements for the study area including a new express branch and targeted headway improvements, and offers observations on some planned and under-construction projects.

Acknowledgments

I must first acknowledge the support of two scholarships that supported me financially during my thesis work: the Ontario Graduate Scholarship (OGS) and the NSERC Canadian Graduate Scholarship (CGS).

I’d like to thank my supervisor, Prof. Eric Miller. It has truly been an honour to work with him and I cannot stress enough my gratitude for the trust he has placed in me and for the professional opportunities that our work together has provided. I’d also like to thank Prof. Amer Shalaby for offering his time to be the second reader on this thesis.

Thank you to my colleagues, past and present, working in and alongside TMG: Trajce Nikolov, Monika Nasterska, David King, Peter Kucirek and especially James Vaughan. We’ve been through many adventures together and I owe them a major debt of gratitude for what they’ve taught me about transportation modelling (and other things).

Thank you to my good friends, Alec Knowles and Graeme Pickett. The three of us met on the first day of grad school and I am forever grateful for that. They helped get me through what was, at times, a struggle. Mainly, though, I thank them because they each did the same in their theses and it would be rude of me not to.

Finally, I would like to thank my wife, Kaori Austin. Her hard work on her own graduate studies has been an inspiration and she has always helped push me towards the finish line. Ever willing to listen to my ranting about my work, she still has no idea what my thesis is about. And nor do I about hers.

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Table of Contents

Abstract ...... ii

Acknowledgments...... iii

Table of Contents ...... iv

List of Tables ...... viii

List of Figures ...... x

List of Appendices ...... xii

Chapter 1 Introduction ...... 1

Introduction ...... 1

Background ...... 1

Motivation and Existing Analysis Tools...... 1

Research Objectives ...... 2

Document Structure ...... 3

Chapter 2 Literature Review ...... 5

Literature Review ...... 5

Demand Modelling ...... 5

2.1.1 The Four Step Model ...... 5

2.1.2 Activity-Based Modelling ...... 7

2.1.3 Integrated Operational Travel Demand Models ...... 8

2.1.4 Integrated Land Use and Transportation Models...... 8

Transit Network Problems (TNP) ...... 9

Chapter 3 Software Descriptions ...... 12

Software Descriptions ...... 12

Software Interaction ...... 12

Emme ...... 13

3.2.1 Project Elements ...... 13

3.2.2 Emme Desktop ...... 14

3.2.3 Emme Modeller ...... 14

3.2.4 Tool Development and the Emme APIs ...... 14

3.2.5 TMGToolbox ...... 15

XTMF ...... 15

GTAModel V4.0 ...... 16

3.4.1 Calibration and Validation Data ...... 16

3.4.2 Model Algorithm ...... 17

Chapter 4 Case Study Introduction ...... 28

Case Study Introduction ...... 28

Transit in Toronto Today ...... 28

4.1.1 GO Transit ...... 28

4.1.2 TTC ...... 29

Study Area ...... 31

4.2.1 Road Network ...... 32

4.2.2 Transit Network ...... 33

Gardiner Expressway ...... 34

Opportunities...... 34

Chapter 5 Methodology and Workflow ...... 36

Methodology and Workflow ...... 36

Summary ...... 36

Scenario Typology ...... 39

5.2.1 Route Modification ...... 39

5.2.2 Operational Changes ...... 39

5.2.3 Fare Structures ...... 39 v

Proposals and Precedents ...... 40

5.3.1 Regular Operation Reviews ...... 40

5.3.2 Additional Service Reviews ...... 41

5.3.3 Express Routes ...... 41

5.3.4 Transit Priority ...... 42

5.3.5 Fares ...... 43

Networks ...... 44

5.4.1 Base Network ...... 44

5.4.2 Additions to the Network ...... 46

Full Network Set Generator ...... 51

Input and Run Organization ...... 56

Headway and Speed Calculations and Assumptions ...... 58

Fare Modification Scenarios and Processes ...... 59

Outputs ...... 60

5.9.1 Post Household Iteration ...... 61

5.9.2 Post Household ...... 61

5.9.3 Post Iteration ...... 62

5.9.4 Post Run ...... 62

Evaluation ...... 66

5.10.1 Summary Sheets...... 66

5.10.2 Quick Evaluation Tools ...... 69

5.10.3 Cost and Revenue Data ...... 69

5.10.4 Consumer Welfare ...... 70

5.10.5 Intervention-Specific Tests ...... 71

Known Bugs...... 72

Computer Performance ...... 72 vi

Chapter 6 Results and Discussion ...... 73

Results and Discussion ...... 73

Repeatability Testing ...... 73

Large-scale Headway Reductions and Speed Increases ...... 74

Targeted Route Headway Reductions ...... 78

Headway Sensitivity ...... 79

Minor Route Modifications...... 81

Express Services ...... 82

Ferries ...... 83

Funded LRT Projects ...... 85

Fare Schema Modification ...... 93

Congestion Testing ...... 96

Chapter 7 Conclusions ...... 101

Conclusions ...... 101

Interventions ...... 101

Use of GTAModel V4.0 ...... 104

Future Work ...... 105

References ...... 107

Appendices ...... 117

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List of Tables

Table 1: Bus planning process. From (Ceder & Wilson, 1986)...... 9

Table 2: TTC line rebranding ...... 30

Table 3: Regression results for daily bus operating costs model ...... 64

Table 4: Consolidated Runs Sheet summary values ...... 67

Table 5: Summary statistics for selected variables resulting from repeatability testing ...... 74

Table 6: Selected data for all major headway reduction scenarios (Scenarios 1-8) ...... 75

Table 7: Aggregate AM boarding changes relative to base for headway reduction scenarios (Scenarios 1-8) ...... 77

Table 8: Selected data for maximum all-day headway improvement scenarios (Scenarios 76-78) ...... 77

Table 9: Aggregate AM boarding changes relative to base for speed increase scenarios (Scenarios 9-11, 79-80) ...... 78

Table 10: Selected data for targeted headway reduction scenarios ...... 79

Table 11: Selected data for minor route modification scenarios ...... 82

Table 12: Combined AM boardings on various routes before and after implementation of express service ...... 82

Table 13: Differences from the base case for a number of outputs from express service scenarios ...... 83

Table 14: Utility data for Scenarios 81-83, difference from base case ...... 86

Table 15: Selected data for Downtown Express surcharge changes (Scenarios 55-57) ...... 94

Table 16: Selected data for distance-based fare schemes (Scenarios 53 and 54) ...... 96

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Table 17: Selected outputs from Scenarios 84 and 85 ...... 99

Table 18: Boardings by GO Train line, Scenario 85 ...... 100

List of Figures

Figure 1: Transit network problems (TNP). From (Guihaire & Hao, 2008)...... 10

Figure 2: Relationships between the modelling software ...... 12

Figure 3: GTAModel V4.0 overview. Adapted from (Travel Modelling Group, 2015a)...... 18

Figure 4: Schedule hierarchy. Adapted from (Roorda, 2005)...... 21

Figure 5: Standard transit network. From (Travel Modelling Group, 2015a)...... 25

Figure 6: Transit hypernetwork. Each colour represents a system's hyperplane. From (Travel Modelling Group, 2015a)...... 25

Figure 7: GO system map, from (GO Transit, n.d.) ...... 29

Figure 8: TTC subway map, from (Toronto Transit Commission, 2015c) ...... 30

Figure 9: TTC streetcar map, from (Toronto Transit Commission, 2015c) ...... 31

Figure 10: Map of Toronto Planning Districts with study area highlighted, adapted from (Data Management Group, 2013) ...... 32

Figure 11: Overall process flow chart ...... 38

Figure 12: Flow chart of Full Network Set Generator ...... 53

Figure 13: Speed increases in TMG base network for AM peak period TTC express branches relative to the local branch. Average and median = 12%...... 59

Figure 14: Daily TTC ridership across different headway scenarios for two routes, relative to base case...... 80

Figure 15: Daily TTC profit across different headway scenarios for two routes, relative to base case ...... 80

Figure 16: Daily TTC profit (net of utility) across different headway scenarios for two routes, relative to base case ...... 81 x

Figure 17: AM travel patterns for passengers on 900 HUMBER FERRY, Scenario 47. Transit volumes shown in blue, auxiliary transit volumes in red. Western terminus shown on the left, eastern terminus on the right...... 84

Figure 18: AM travel patterns for passengers on 901 KIPLING FERRY, Scenario 48. Transit volumes shown in blue, auxiliary transit volumes in red. Western terminus shown on the left, eastern terminus on the right...... 84

Figure 19: Various ridership metrics for LRT routes in Scenarios 81-83, difference from base case ...... 86

Figure 20: Aggregated origins for trips using 32 EGLINTON WEST and 34 EGLINTON EAST, Scenario 0 (base case) ...... 88

Figure 21: Aggregated origins for trips using 601 EGLINTON CROSSTOWN, 32 EGLINTON WEST and 34 EGLINTON EAST, Scenario 81 ...... 89

Figure 22: Aggregated origins for passengers using Eglinton corridor, difference between Scenario 81 and Scenario 0 ...... 90

Figure 23: Aggregated origins for passengers using Finch corridor, difference between Scenario 82 and Scenario 0 ...... 91

Figure 24: Aggregated origins for passengers using Sheppard corridor, difference between Scenario 83 and Scenario 0 ...... 92

Figure 25: Aggregated origins for passengers using Eglinton corridor, difference between AM transit demand matrices from Scenario 81 and Scenario 0 assigned to the Scenario 81 network 93

Figure 26: Daily GO and TTC ridership compared to Scenario 0 for GO-TTC co-fare tests, Scenarios 58-63 ...... 94

Figure 27: Capacity utilization on subways (aggregated on links), Scenario 0 ...... 97

Figure 28: Capacity utilization on GO trains (aggregated on links), Scenario 0 ...... 98

List of Appendices

Full Scenario List ...... 118

Consolidated Runs Sheet (Standard) ...... 125

Consolidated Runs Sheet (AM Boardings) ...... 126

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Chapter 1 Introduction Introduction Background

Global population is expected to grow by approximately three billion people by 2050; in the aggregate, all of this growth will be in urbanized areas. While much of this growth is expected to come from rapidly developing nations in Africa and Asia, North America will see both urban population proportion and total population growth. Canada is expected to be 88% urban by 2050, up from 82% in 2014 (United Nations, 2014). This growth is especially apparent in the Greater Toronto Area (GTA). In their reference scenario, the Ontario Ministry of Finance (2014) projects an increase in population for the GTA of 2.95 million between 2013 and 2041, representing a growth of 45.8% over that time. Within the City of Toronto, the Ministry projects an increase of approximately 868,000, or 31.3%.

With this concentration of growth, public transit, along with active modes of transportation, becomes increasingly more vital to the functioning of urban areas. Without solid planning and investment in public transit, the economic impact of vehicular congestion will continue to grow. Even today, a Metrolinx (2008) report pegged the economic impact of congestion in the Greater Toronto and Hamilton Area (GTHA) at $6 billion per year and a follow-up report from Benjamin Dachis (2013) suggests that the initial Metrolinx report did not take into account a number of externalities, which he priced at $1.5 to $5 billion annually. The City of Toronto’s Official Plan (2015b) directs growth to areas of the city that are well-served by transit. In order to successfully integrate the growth and transit objectives of the city, robust planning and policy analysis tools are needed.

Motivation and Existing Analysis Tools

Currently, the Toronto Transit Commission (TTC) uses a model called MADITUC for its planning analysis (Wang, Wahba, & Miller, 2010). MADITUC uses a fixed demand input and can incorporate pre-specified transit itineraries (Idris, 2013). The existing model, therefore, cannot predict the impacts of changes to the network on origin-destination travel demands. A

2 strength of MADITUC is its ability to demonstrate inconveniences (or conveniences) to passengers under a given intervention. As the time horizon moves forward, this strength becomes less impactful, as the fixed demand matrices become less realistic.

Additionally, TTC policy dictates a reactive approach to frequency setting. Routes become eligible for higher frequencies if and when they exceed the official loading standards; minimum frequencies are set by policy (Toronto Transit Commission, 1984). This leaves little room for optimization of headways. The possibility of a route’s headway being increased beyond standard in order to improve the broader network is not regularly examined.

Transportation planning in Toronto, apart from TTC service planning, is now under the purview of the City of Toronto’s City Planning Division. City Planning has been using a four-step model, GTAModel V2, but is working to support the University of Toronto in its development of the agent- and activity-based GTAModel V4.0 and is currently using the new model in its analysis of the proposed SmartTrack rail project (Livey & Stambler, 2015).

Potential exists for GTAModel V4.0 to be used more extensively in the transit planning processes in the Toronto area. A streamlined modelling approach between the City and the TTC would almost certainly have some interoperability benefits. Having been in use for decades, existing MADITUC procedures are well-integrated into TTC operation, but the option to increase usage of a broader model is worthy of investigation.

Research Objectives

The goal of this thesis is to examine the feasibility and efficacy of using an integrated, activity- and agent-based microsimulation regional travel demand model – namely, GTAModel V4.0 – for a variety of transit policy analyses, from fine-grained route and headway setting, to large infrastructure investments, to broad policy assessments.

To do so, a series of policy scenarios1 and experiments will be developed, geared both towards identifying potential transit improvements in a case study area, as well as towards effective evaluation of the use of GTAModel V4.0 in policy analyses.

The case study area is Etobicoke (the western-most portion of the City of Toronto) and the corridor connecting it to Downtown Toronto. Currently available transportation options are heavily saturated in the corridor. Prohibitively expensive projects, namely the creation or extension of subway lines, are avoided; near-term improvements are preferred. Improvements include frequency changes, route modifications and additions and operational improvements. I propose to step back from existing policies and examine potential improvements broadly, similar to the aims of the solutions to the transit network problems (TNP).

Additionally, GTAModel V4.0 will be tested under a number of different transit policy scenarios in other parts of Toronto. Some of these are large, funded investments in new routes, such as the Eglinton Crosstown LRT, while others are less centralized, such as changes to fare policy.

The thesis will attempt to provide recommendations regarding the implementation of GTAModel V4.0 in policy analysis and regarding the implementation of transit improvements in Etobicoke and the Etobicoke-Downtown Toronto corridor.

Document Structure

This document is organized in a collection of eight chapters. Chapter 1 provides background information on the research topic and objectives and on the current state of public transit policy analysis in Toronto. Chapter 2 contains a brief literature review of the development of travel demand models, as well as an overview of the class of problems referred to here as transit network problems (TNP). Chapter 3 provides high level discussions on the various software used, namely Emme, XTMF and GTAModel V4.0. Chapter 4 is an introduction to the case study area. It provides an historical background of the development of the Toronto transit network up to and including the near future, and provides context for the transportation situation in the

1 I will also refer to these scenarios as “interventions” occasionally when there is chance for confusion with the other form of scenarios mentioned throughout – Emme scenarios.

4 specific study area. Chapter 5 gives a technical overview of the modelling workflow and the methodologies used in network construction, modelling and evaluation. Chapter 6 presents the results of the modelling work. Chapter 7 provides conclusions, recommendations and suggests opportunities for further research and model development.

Chapter 2 Literature Review Literature Review

This chapter is intended as a quick primer for some of the academic research that preceded and helped inform the work in this thesis. This first section of this chapter aims to provide an overview of the history and state of the art of transportation demand modelling in order to give context for the model being used in this investigation, GTAModel V4.0. The second section gives an overview of the class of problems known as the Transit Network Problems (TNP). The motivation behind TNP is similar to the motivation for this thesis, and some of the approaches used to solve TNP provide potential avenues for extension of the work presented in this document.

Demand Modelling

2.1.1 The Four Step Model

Briefly, transportation demand modelling is the use of mathematical models to predict travel behaviour under some set of conditions (land use, new infrastructure, etc.). Modelling is a tool to help planners and engineers make informed decisions about planning policy and infrastructure investment. Travel behaviour literature is extensive and many commercial products exist for demand modelling. The modern field of transportation demand modelling began in the 1950’s with the introduction of the four step model. Its structure is simple and sequential, using the following four elements:

Trip Generation. Trip generation is the estimation of trip frequencies, both from the production end and from the attraction end. Generally, these estimates are based on land use and socio- economic inputs and are divided by traffic analysis zone (TAZ).

Trip Distribution. Trip distribution involves matching trip productions to trip attractions, yielding origin-demand trip matrices. Balancing is performed using an impedance function, typically based on time or cost.

Mode Choice. Mode choice, or modal split, is the calculation of the likelihood of taking a certain travel mode for a given trip.

Trip Assignment. Trip assignment, or route choice, is the process of allocating trips along paths in the model transportation network. Trip assignment involves the use of an equilibration technique to balance use of the finite capacity available along network links. Assignments are typically performed (separately) for auto and transit modes. Results of a trip assignment can be passed back into earlier model components to better inform trip distribution and mode choice and move towards convergence (McNally, 2007).

There is wide variation of structure within each sub-model. The modeller’s decision to use a given structure depends on the intended purpose and application of the model and on the available input data. The book Modelling Transport by Ortzar and Willumsen (2011) provides an excellent overview of the common methods used for each sub-model. For trip generation it is common to use either a cross-classification (also called category analysis) approach or linear regression to compute generation rates. Trip distribution is almost universally calculated through some form of gravity model. Mode choice methods vary widely. Models can use empirical diversion curves, direct demand approaches or logit models, among others. Assignment approaches are also extremely varied. For example, there are numerous ways to treat equilibrium, including user- or system-equilibrium objectives. Alternatively, there are also dynamic assignments that do not reach a single static equilibrium, but rather attempt to equilibrate user costs over many time intervals. Often, these approaches incorporate time-of- departure choice. Given the more complex nature of transit systems, transit assignment is somewhat more simplified relative to traffic assignment. Optionally, it can include such additions as the calculation of on-board congestion effects.

Beyond these important sub-model structural choices, the basic four step model can also be expanded in a number of ways. For example, a vehicle availability model can be incorporated with trip generation and output from an external trip model can be applied to the network during trip assignment. Complications such as time of day and vehicle occupancy can also be introduced, depending on the intended use of the model. The sequential nature of this class of model makes implementation and customization simple. This feature has certainly helped maintain the popularity of the four step model. (Transportation Research Board, 2012).

2.1.2 Activity-Based Modelling

The fundamental unit of the basic four step model is the trip, which is a movement from origin to destination. The weakness of trip-based modelling lies in the concept of a trip. Specifically, people do not generally make trips solely for the purpose of making trips. Trips are a means to access something, be it work, school, shopping, etc.; they are a derived demand (Ortzar & Willumsen, 2011). The utility of making a trip lies not in the trip itself (which is actually a disutility), but in the access of – and the participation in – the desired activity. It follows, then, that modelling travel behaviour should not start at the resultant travel element (the trip), but rather at the motivating reason for travel (the activity). From the concept of an activity, we logically (and necessarily) extend to other key elements of a travel day. These include the tour, the trip chain, the agenda and the activity schedule.

A tour is “a sequence of trips starting and ending at the same location” (Ortzar & Willumsen, 2011). Most tours are home-based (e.g. travelling to work and back, potentially completing other activities along the way), though some are non-home-based. An example is the work-based business concept from TASHA (Miller & Roorda, 2003), which encapsulates tours made from the place of work. A similar concept to the tour is the trip chain, which is also a sequence of trips, but does not require the same start and end location. Early complete disaggregate replacements for the four step model, such as the Netherlands National Model (Gunn, 1994), utilized a tour-based approach.

During the 1980s and 1990s, more complete implementation of the activity-based concept was developed. An agenda is the list of activities planned for a given time period (usually a day) and an activity schedule is the time-specific implementation of an agenda. The first operational model to include activity schedules is STARCHILD (Recker, McNally, & Root, 1986). Another widely cited activity-based model is one developed for Boston by Bowman & Ben-Akiva (2000). This model represents a complete 24-hour period; activity patterns are chosen with a logit model from a list of 54 possibilities. An alternative to the pre-selected activity pattern list are the rule- based approaches used in ALBATROSS (Arentze & Timmermans, 2004) and TASHA (Miller & Roorda, 2003), a model that features heavily in the operational model used in this thesis, GTAModel V4.0.

2.1.3 Integrated Operational Travel Demand Models

As activity-based modelling improved over the past decades, so too did computing power and software development. Most importantly, developments in agent-based modelling provided a platform on which to continue refining disaggregate transportation modelling. With these tools, researchers developed broad models that integrate the entire transportation modelling process. This integration allows for better behavioural feedback, as well as, from a practical standpoint, easier software deployment and use.

Many integrated models have been presented. Some of these are intended as general modelling platforms, including MATSIM (Balmer, et al., 2009), TRANSIMS (Cetin, Nagel, Raney, & Voellmy, 2002) and POLARIS (Auld, et al., 2015). Notably, all three of these models utilize dynamic assignment, allowing, for example, robust testing of ITS applications. Some implementations of integrated activity-based modelling focus more heavily on other applications and use static assignments to compensate for added complexity. This category includes GTAModel V4.0 (Miller, Vaughan, King, & Austin, 2015), which aggregates the computed demand matrices from the TASHA-based core of the model before passing them to an external program (Emme) for assignment. Work has been done on pairing such activity-based models with more robust assignment procedures, including the integration of TASHA with the MATSIM assignment module (Hao, Hatzopoulou, & Miller, 2010).

2.1.4 Integrated Land Use and Transportation Models

An extension of the integrated travel demand model concept is the class of integrated land use and transportation models, also referred to as integrated urban models by Badoe & Miller (2000). It is important to understand that, in order to run a travel demand model, one needs population and employment inputs. Traditionally, these are treated as exogenous inputs. However, development, both residential and otherwise, is not independent of the transportation conditions of the area. Nor are transportation demands independent of the local built form and urban characteristics. Products that model these interaction effects include ILUTE (Salvini & Miller, 2005) and UrbanSim (Waddell, 2002). Unlike in the transportation-only case, there are no fully integrated urban models in the literature; instead, many integrated urban models are built to interface with a variety of potential transportation models (Hunt, Kriger, & Miller, 2005).

Transit Network Problems (TNP)

The complete bus network planning process is described by Ceder and Wilson (1986) as a five- level process (Table 1). Much of the applied literature is focused on the final scheduling steps and this is where agencies tend to focus broad improvement efforts. Rarely do agencies step back to the network design level and re-evaluate the overall network. Similarly, frequency setting, either demand- or policy-based, is infrequently addressed by transit agencies, with the exception of automatic reactive demand-based changes. The focus on stepping back and evaluating the network from a different perspective is a common theme in this thesis. In the following paragraphs, systematic approaches to this motivating theme are discussed.

Table 1: Bus planning process. From (Ceder & Wilson, 1986). Inputs Activity Output Demand data Network Design Route changes Supply data New routes Route performance indices Operating strategies Subsidy available Setting Frequencies Service frequencies Buses available Service policies Current patronage Demand by time of day Timetable Development Trip departure times Times for first and last trips Trip arrival times Running times Deadhead times Bus Scheduling Bus schedules Recovery times Schedule constraints Cost structure Driver work rules Driver Scheduling Driver schedules Run cost structure

A great deal of research has been done in the past few decades developing algorithms for large- scale, “from-scratch”, overhauls of network design and frequencies. These address a class of problems referred to as the transit network problems (TNP) by Guihaire and Hao (2008) in their extensive review of the subject. The broadest problem – TNDSP – encompasses the upper three levels of the bus planning process: network design, frequency setting and timetable development. Each individual component has its own associated problem, as does each pair of adjacent steps (Figure 1). The acronyms used by Guihaire and Hao do not necessarily align with the most commonly used descriptors for these classic problems, but they are consistent between categories

10 and will be used throughout this document. Note that what is termed the transit network design problem (TNDP) here is often referred to as the transit route network design problem (TRND).

Figure 1: Transit network problems (TNP). From (Guihaire & Hao, 2008).

Some form of solution has been developed for all of the problems noted in Figure 1. Guihaire and Hao (2008) provide an extensive list of research performed up to 2007. For this thesis, the most relevant problems are TNDP, the transit network frequency setting problem (TNFSP) and their combined problem, the transit network design and frequency setting problem (TNDFSP). These have all been investigated using a number of different approaches. The most common approaches in the literature are mathematical, heuristics and evolutionary algorithms. Neighbourhood search techniques and other unique approaches are also used. While the general problems may be the same across different studies, including across those using different techniques, the objectives and constraints vary widely. Similarly, while some methods are applicable for large-scale implementation on real networks, others may only be useable on small examples or benchmark networks. Another important differentiation is the origin-destination (OD) demand used for the network. Many authors search for methods to optimize a fixed set of transit OD demands. The complexity of the problem means that very few incorporate the demand changes that may result from changes to the transit network. Those that do allow for level-of- service feedbacks, including Cipriani, Gori and Petrelli (2012), Lee and Vuchic (2005) and Fan

11 and Machemehl (2006), do so only with respect to modal split. The lack of feedback to trip distribution and generation likely weakens the applicability of these models to long-term planning and to the interaction with other elements of the transportation planning process.

Chapter 3 Software Descriptions Software Descriptions

This chapter explains the various software used in the main modelling process of this thesis. These are: Emme, eXtensible Travel Modelling Framework (XTMF) and GTAModel V4.0. Section 3.1 provides an overview of how these pieces of software interact with each other. The sections that follow give more detailed explanations of the software structure and usage. Certain software components require further explanation specific to the thesis. These explanations are included in relevant sections of Chapter 5.

Software Interaction

The three pieces of software used in this modelling exercise are heavily interconnected. GTAModel V4.0 is, by strict definition, a single “model system” within XTMF. It is a collection of sub-models called modules that are organized and executed by XTMF. GTAModel V4.0, in its current state, cannot exist without XTMF. Some component modules of GTAModel V4.0 call the Emme API and may send or receive data from Emme. Data transmission between GTAModel V4.0 and Emme is handled by what is called the “Emme Bridge.” Figure 2 provides a diagrammatical overview of these relationships.

Emme

Module

Figure 2: Relationships between the modelling software

Emme

Emme is described as a “transportation forecasting system.” It is a platform on which one can develop a variety of transportation models. There are two basic sub-platforms in the Emme environment. One is Emme Desktop, which provides a visual interface between the modeller and the physical transportation network. Within this program, the modeller can edit the network, import and export maps and create other visualizations of model and network data. The other platform is Emme Modeller, which provides the main framework for running models (INRO, n.d.).

3.2.1 Project Elements

There are a number of components that make up an Emme project. The primary ones are described here in order to provide the reader with context.

A project is what contains all data belonging to the Emme model. This includes the database, worksheets, tables, media files, etc.

A database is the fundamental container for data in Emme. Scenarios, networks, matrices, etc. are in the database.

A scenario can be described as one instance of a network. Many elements are changeable between scenarios and comparing results between scenarios is a useful analysis.

A network is a representation of the physical transportation network. It includes a number of elements: turns, links, nodes/centroids, modes, transit segments, transit lines, transit vehicles.

A turn is a representation of available intersection movements.

A node is a point such as a transit stop, intersection, etc., a centroid is a node representing a zone, a link is a pathway between nodes (called a connector when a centroid is on at least one end) and modes enable definition of the types of traffic (be it regular auto, buses, walking, etc.) that can travel on a link.

A transit line consists of a transit vehicle and an itinerary. Lines also have a defined headway and speed. The line itinerary defines the route and is an ordered list of transit segments. Segments define characteristics for a specific line on a specific link.

A transit vehicle represents a specific class of vehicle and can have a number of attributes, the most important being capacity. A vehicle can be associated with only one mode (INRO, n.d.).

3.2.2 Emme Desktop

Emme Desktop is the visual platform for the project, handling all mapping, visualization, charts, tables, etc. A large variety of these come pre-built. Users can fully customize the pre-built assets or build their own from scratch. Many assets can be printed to file and table data can be exported for use in other programs. One important tool is the Network Editor, which allows for editing of most network components inside a visual interface. Network Editor is particularly useful when building networks from scratch, or when making small changes. Batch edits or importing from other data sources is better handled using Emme Modeller (INRO, n.d.).

3.2.3 Emme Modeller

Emme Modeller is the modelling framework in Emme. Its HTML-based user interface is a marked improvement over the older Emme Prompt. Modeller is made up of Toolboxes, which contain tools. Toolboxes can be organized further using a nested directory structure. Combining tools into a Toolbox allows for easy sharing. The Emme Standard Toolbox contains all of the standard tools developed by INRO. Many of these tools are similar to what is available in Emme Prompt (INRO, n.d.).

3.2.4 Tool Development and the Emme APIs

Users can expand the capabilities of Emme Modeller beyond the standard INRO tools by developing tools in Python. Facilitation of tool development is provided through eight application program interfaces (APIs): the Desktop API, the Database API, the Network API, the Prompt API, the Matrix API, the Analysis API, the Core API and the Modeller API. Of particular note, the Modeller API provides the framework for making Python code accessible as a Modeller tool, as well as allowing for calling of other Modeller tools. Most project data access and manipulation is performed using the Database and Network APIs (INRO, 2015).

3.2.5 TMGToolbox

The Travel Modelling Group (TMG) at the University of Toronto is the primary developer and maintainer of GTAModel V4.0 and XTMF. TMG currently develops an Emme Toolbox called TMGToolbox. This houses a variety of Python tools using the Emme APIs. Tools from this Toolbox are referenced throughout this document. TMGToolbox tools are used extensively in GTAModel V4.0. The TMGToolbox is open-source and accessible on GitHub, and can be used under the GPLv3 license. It is also available packaged with XTMF and can be updated via the XTMF update tool.

XTMF

XTMF stands for eXtensible Travel Model Framework. The main purpose of XTMF is to provide a unified platform on which to easily build and extend – as well as rapidly prototype – transportation models. XTMF is open source and can be used under the GPLv3 license.

There are three basic elements in XTMF: modules, model systems and projects. Modules are the “building blocks”; TMG packages a number of these with its distribution of XTMF. Users can also develop their own modules to fit their needs. Modules are typically written in C#, though developers can use any .NET-based language. Model systems are collections of modules that the user can execute. In the XTMF GUI, users can easily add, delete, move and edit modules and their parameters in order to define the model. Projects group together model systems and model system runs.

XTMF is well-integrated with Emme; careful development of callable Emme tools (and a corresponding XTMF module) allows for data transfer between XTMF and an Emme database. Emme is currently the sole modelling platform available for bridging, though development using other software is in progress.

It is possible to set up a Host/Client networking system in XTMF. This is an invaluable tool for estimating model parameters using both traditional clusters and clusters of desktop computers (also known as Beowulf clusters).

Serialization of model runs is also possible in XTMF. Using the Multi-Run Framework, one can set up a sequence of model runs and commands, changing any parameters between runs as

16 desired. The Multi-Run Framework was used extensively for this thesis; further explanation can be found in Section 5.6 (Travel Modelling Group, 2015b).

GTAModel V4.0

GTAModel V4.0 is the latest operational travel demand model developed and used by TMG. GTAModel V4.0 is designed for use in XTMF and all components of the model are available as XTMF modules. The model system itself is property of the University of Toronto and is available for use by students of the University as well as by TMG funding agencies.

Previous versions of GTAModel have used the traditional four step transportation modelling approach. Version 4.0 is therefore unique in its application of full-day, agent- and activity-based modelling. The core component of GTAModel V4.0 is TASHA (Travel and Activity Scheduling for Household Agents), an algorithm developed at the University of Toronto (Miller & Roorda, 2003; Travel Modelling Group, 2015a).

Another notable upgrade of GTAModel V4.0 include its use of fare-based, congested transit assignment.

3.4.1 Calibration and Validation Data

3.4.1.1 Transportation Tomorrow Survey

The Transportation Tomorrow Survey (TTS) is a quinquennial 5% household survey of the Greater Toronto and Hamilton Area (GTHA), as well as most of the surrounding Greater Golden Horseshoe (GGH). TTS has been conducted since 1986 and is organized by the Data Management Group at the University of Toronto. The most recent survey, referred to as the 2011 TTS, was conducted in the autumns of 2011 and 2012. Data from the 2011 TTS is used for virtually all calibration of GTAModel V4.0 (Data Management Group, 2014b).

3.4.1.2 Cordon Counts

The GTA municipalities, along with the Province, coordinate the collection of cordon count data in the region. The Data Management Group is responsible for maintaining this data in a unified database, accessible using the online Cordon Count Data Retrieval System. Data from this

17 system is used to validate the number of cross-screenline trips generated by GTAModel V4.0 (Data Management Group, 2014a).

3.4.1.3 Other Data

Other data sources are also used for validation of model results. These include station counts from TTC and GO Transit and riding counts from TTC.

3.4.2 Model Algorithm

A complete explanation of all elements of the model is not appropriate for this document. However, an overview of the model algorithm and an introduction to the components are useful for understanding the modelling efforts of this investigation; these are provided in the subsections that follow. For more detailed information of GTAModel V4.0, please review the background documentation available from the Travel Modelling Group (2015a).

3.4.2.1 Algorithm Overview

The general GTAModel V4.0 process is shown in Figure 3. It begins with an initial network assignment using pre-processed demand values direct from TTS data. This primes the model with an initial set of level-of-service values such as travel times. Next, zonal data is loaded in. This includes aggregate population and employment data. All of the other main sub-models are contained within what is termed the “outer loop.” The outer loop is iterated a number of times. In this thesis, four outer loop iterations are used, optimizing satisfactory convergence and run time.

Initial Network Assignment

Load Zone System

Load Network Data

Compute Compute Compute Access PoRPoW PoRPoS Station Probabilities Probabilites Model

Load Household

Assign Assign Work

School Zone Zone Compute Next Iteration Next Compute Scheduler

Mode Choice

Network Assignment

Finish

Figure 3: GTAModel V4.0 overview. Adapted from (Travel Modelling Group, 2015a).

The outer loop starts with aggregate probability calculations. Zonal probabilities are then applied to the population, which is the output of a population synthesis procedure. Next, the scheduler component generates trips for persons. These trips are fed through a mode choice model and then aggregated into time period demand matrices to be passed to Emme for network assignment (Travel Modelling Group, 2015a).

3.4.2.2 Aggregate Zonal Probabilities

There are three aggregate models calculating aggregate zonal origin-destination probabilities: Place of Residence Place of Work (PoRPoW), Place of Residence Place of School (PoRPoS) and Access Station Choice.

Separate PoRPoW probability matrices are calculated for all combinations of occupation and employment types. Each of the matrices is calculated using a triply constrained gravity model. The first two dimensions of the gravity model are standard – residence and job availability. The third is worker category probability. Here, worker category refers strictly to automobile access. One category is for those with no access to a vehicle, either due to the lack of a driver’s license or the lack of any vehicles in the household. The second category is for workers with a driver’s license, but whose household has more potential drivers than vehicles. The final category is the vehicle saturation condition. That is, the worker has a driver’s license and the number of vehicles in the household meets or exceeds the number of licenses. As persons are loaded in to the model, an assignment procedure assigns them to a discrete place of work.

PoRPoS is a very simple procedure that uses the same school attendance distribution as in the 2011 TTS data, updated for future year population totals. As persons are loaded in to the network and assigned school zones, probabilities are updated accordingly to avoid substitution.

The Access Station Choice model calculates the utility of using a particular station as an intermediate point in a given origin-destination Drive-Access-Transit (DAT) trip. It is tour- based; return trips must use the same station in order to retrieve the vehicle. The systematic utility for access station 퐴푥 is given by the following equation:

푉 = [푎푡𝑖푚푒] + [푎푐표푠푡 + 푃푎푟푘𝑖푛푔퐶표푠푡 + 푡푓푎푟푒 ] 퐴 훽푎푡푖푚푒 훽푐표푠푡 퐴 퐴푥 퐴 [ ] [ ] + 훽푡푖푣푡푡 푝푒푟푐푒𝑖푣푒푑푇푟푎푛푠𝑖푡푇𝑖푚푒퐴 + 훽퐶푎푝푎푐푖푡푦 퐶푎푝푎푐𝑖푡푦퐴 [ ] + 훽퐶푙표푠푒푠푡푆푡푎푡푖표푛 퐶푙표푠푒푠푡푆푡푎푡𝑖표푛퐴

Both access and egress trip data are summed to yield each variable, with the exception of the various station data, which only apply for access. The probability of choosing a particular station for a trip is computed using a multinomial logit model:

푉퐴푥 퐶푎푝푎푐𝑖푡푦퐹푎푐푡표푟퐴푥 ∗ 푒 푃(퐴푥) = 푉 ∑ 퐴푖 푖[퐶푎푝푎푐𝑖푡푦퐹푎푐푡표푟퐴푖 ∗ 푒 ] where the CapacityFactor variable is a scaling term relating how full the station’s parking lot is. This factor uses Speiss’ (1990) conical function, which is also used in transit assignment (Travel Modelling Group, 2015a).

3.4.2.3 Scheduler

The GTAModel V4.0 scheduler sub-model is constructed from Miller and Roorda’s (2003) work on TASHA (Travel and Activity Scheduling for Household Agents). The scheduler handles the agent- and activity-based equivalent of the trip generation and trip distribution steps of a four- step model. Within the sub-model, there is a general hierarchy of schedule elements. The basic element is called an episode. This is a discrete activity and includes a number of potential types, including travel. Episodes can be either individual or joint. Above an episode is a project. Projects are broad collections of related activities. For example, the work project may contain both a “primary work” episode and a “work-based business” episode, among others. A third element is the person schedule. This is created by combining all projects and defines all episodes in the day for a given person. A diagram of the general schedule hierarchy is provided in Figure 4.

Households

Persons

Household Person Projects Projects

Household Person Person Project Project Schedule Schedule Schedule

Individual and Individual Joint Activity Joint Activity Travel Episodes Activity Episodes Episodes Episodes

Figure 4: Schedule hierarchy. Adapted from (Roorda, 2005).

Activity episode generation for persons is based on a number of personal attributes including age, employment status, occupation, home zone and work zone; household-level activity generation is affected by household structure. The first step of the process is to generate activity frequency. Given this, a start time for the activity is generated. Finally, an activity duration is generated based on the start time. If the start time and duration are feasible within the project schedule, the activity is carried forward. Otherwise, the start time and duration are re-drawn. This continues until a maximum number of failures is reached.

Episodes generated by projects are then combined into a person schedule. The order of precedence for project scheduling is based on student status. For full-time students, the School project is scheduled first; for part-time or non-students, the Work project takes priority. Next are joint Other and Market trips, followed by individual Other and Market trips. When building the person schedule, the scheduler will adjust start times in order to fit episodes into the provisional

22 schedule. It will also shorten episode durations to as short as half of the original length if necessary. It is during this person schedule construction that the location choice model is run. Location choice is run for three categories: Work-Based-Business, Market and Other. Note that Home and Work locations are set prior to the scheduler. Utilities for travel between all zones are generated for each time period from a number of inputs. For a given episode, the person must be able to leave the previous location at that episode’s end time, perform the current episode and reach the next location before that episode’s start time. Therefore, the number of feasible locations is limited. Zonal probabilities are truncated to this list of feasible locations and the final location choice is randomly chosen based on the truncated matrix.

In the completed person schedule, travel episodes are the equivalent of trips. Trips are formed into trip chains, ending when the person returns to their starting point. If, at any point during their schedule, a person has no current activity and is able to go home and spend a specified amount of time there (currently 45 minutes), a return home activity is added (Travel Modelling Group, 2015a).

3.4.2.4 Mode Choice

The other sub-model adapted from TASHA is the mode choice model. The GTAModel V4.0 mode choice is performed at the household level. The following modes are analyzed in the model:

Auto. Auto refers to trips taken by the driver of any motorized vehicle owned by a household.

Carpool. Carpool is the inter-household passenger mode. This includes taxi.

Drive-Access-Transit (DAT). DAT is a compound mode that includes both an auto and a transit portion. The separate Access Station Choice model computes the access station. That access station must be the egress station on the return trip in order to facilitate the return of the vehicle.

Walk-Access-Transit (WAT). WAT is the standard transit mode, where the traveler walks to access and egress the transit service.

Walk. The Walk (or Walk-All-Way) mode is for trips where the person walks from origin to destination.

Bike. The Bike mode is for trips where the person rides a bicycle from origin to destination. This mode must be used for the return trip, as well, in order to return the bicycle.

Passenger. The Passenger mode is for persons being driven to a location by a member of the household. This does not include joint tours. Note that the Passenger mode is not used to assist a passenger on a portion of compound trip (i.e. Kiss-and-Ride, or Passenger-Access-Transit, is not feasible).

Rideshare. Rideshare is the mode assigned to the passenger of a joint tour.

The mode choice algorithm aims to optimize combined household utility. The initial, non- iterating step is to find all possible non-shared tours and compute the utilities. The rest of the algorithm loops for a given number of household iterations; the random utility term is recomputed at the end of each iteration. The first step in the loop is to pick the best auto tour and the best non-auto tour to carry forward to the next iteration. Next, DAT tours pull data from the Access Station Choice model results in order to choose a station and compute the utility of the tour. Then, vehicles are allocated to persons or tours (Travel Modelling Group, 2015a).

3.4.2.5 Traffic Assignment

Mode choice results are aggregated into auto and transit demand matrices for each time period with the exception of Overnight, which is run only once to extract initial auto travel times. These matrices are then passed across to Emme via the Emme Bridge. For each time period, auto assignment is run first, followed by transit assignment.

GTAModel V4.0 uses a Static User Equilibrium (SUE) single-class auto assignment. The TMGToolbox tool is called “Toll Attribute Transit Background”. This tool calls, depending on the user’s Emme version, either the INRO “SOLA Traffic Assignment” or the “Standard Traffic Assignment”. The tool also handles the necessary pre- and post-processing of data. Generalized cost in the assignment includes two monetary inputs – toll costs and fixed unit length costs. Toll costs are applied only on links representing Highway 407, a toll facility currently running from Burlington to Pickering. Tolls on Highway 407 vary by location and time. In order to respect the model time periods, toll rates are blended. For each time period, there are two blended rates – regular and light – which correspond to the toll zones on the highway. Fixed unit length costs are

24 a blend of fuel, maintenance and tire costs and are applied equally across all auto link types. The two monetary inputs were calculated in 2011 dollars and are converted to perceived time using a defined parameter. For all links, the tool calculates the effect of transit vehicles. This is done by multiplying the frequency of service by the auto equivalent of the given transit vehicle. These auto equivalents are added to the network as background traffic, reducing the available capacity. For the purposes of assignment, time period demands are converted to a single hour demand using calibrated peak hour factors. Outputs from the assignment include the following matrices, all of which use OD demand-weighted averages: auto in-vehicle travel time (AIVTT), auto costs and auto tolls. These are passed up into subsequent model outer loop iterations (Travel Modelling Group, 2015a).

3.4.2.6 Transit Assignment

During the development of GTAModel V4.0, Emme did not have a satisfactory method of handling complex fare structures for transit assignment2. In the GTHA, there are multiple transit agencies; the fare systems – and the interaction between fare systems – differ widely. To handle this, a concept called the transit hypernetwork was developed. Typically in an Emme network, all transit lines along a road would be encoded on the same series of links. This is adequate for a single fare structure, as most structures can be accommodated within the INRO transit assignment specifications in Emme. However, when a single network contains multiple transit systems with different fare rules and different transfer arrangements, this is no longer possible. The transit hypernetwork procedure isolates transit systems from each other by putting each system in its own virtual hyperplane, which is a copy of the necessary parts of the base network reserved solely for that system. The concept of the hypernetwork is illustrated in Figure 5 and Figure 6.

2 The journey levels feature introduced in Emme 4.2 is promising and allows for a good deal of customization, but the degradation in performance experienced even at relatively low fare complexity was enough to dissuade the team from using the feature beyond disallowing walk-all-way “transit” users.

Figure 5: Standard transit network. From (Travel Modelling Group, 2015a).

Figure 6: Transit hypernetwork. Each colour represents a system's hyperplane. From (Travel Modelling Group, 2015a).

Hypernetwork creation is performed by using a TMGToolbox tool called “Generate Hypernetwork from Schema”. This tool reads in transit system definitions and fare structures from an .xml file. Transit “system” in this context can also refer to a subset of a broader system with a separate fare structure (e.g. TTC Downtown Express). A variety of flat, distance-based and zonal fare structures are available and discounted transfers between systems are possible. Each defined transit system is placed in its own hyperplane, directly above the existing links and

26 nodes. All possible boardings and transfers, except for within-system transfers at a shared node, are coded as transfer links. These links allow passage for a special Auxiliary Transit transfer mode and have a link fare extra attribute which stores the appropriately discounted boarding or transfer fare cost. For systems with a distance-based fare component, line segments are encoded with a segment fare extra attribute corresponding to the unit fare multiplied by the segment length. Similarly for zonal-based fare structures, segments crossing fare boundaries have a non- zero segment fare extra attribute corresponding to the cost of travel to an additional zone. Both the link fares and segment fares are useable in the standard INRO transit assignment specifications.

For transit assignment – similar to with auto assignment – GTAModel V4.0 uses a standard INRO tool (“Congested Transit Assignment”) wrapped inside a customized tool called “V4 Fare Based Transit Assignment”. This tool is run on the hypernetwork scenario previously created by “Generate Hypernetwork from Schema” and handles a number of pre- and post-processing tasks. A notable pre-processing task is the assignment of walk perceptions. Instead of using a global value, GTAModel V4.0 uses different walk perception values for Planning District 1 (Downtown Toronto), the rest of Toronto, the rest of the GTHA, centroid connectors in Toronto and centroid connectors in the rest of the GTHA.

Also notable is the handling of expected wait times. In Emme, expected wait time is calculated by multiplying the headway fraction by the effective headway. Headway fraction represents variability in arrivals. A value of 0.5 represents perfectly spaced arrivals (i.e. inter-arrival times equal to half of the headway). Larger values correspond to irregular service and smaller values can model scheduled services (INRO, n.d.). Effective headway is the headway perceived by the rider. In the model, a 0.5 headway fraction and an effective headway equal to the actual headway are used for short headway services. For longer headway services, like commuter rail, this is an unreasonable model, as customers should not arrive randomly. Headway fraction is assigned at the node, so customization of wait times is easier through changing the effective headway, which is a transit line attribute. Beyond 15 minute headways, GTAModel V4.0 introduces a slope factor for all additional headway. This approach is based on work by Faber Maunsell (2004). The resulting formulation is:

퐸푓푓푒푐푡𝑖푣푒 퐻푒푎푑푤푎푦 = 15 + 2 ∗ 푠푙표푝푒 ∗ (퐴푐푡푢푎푙 퐻푒푎푑푤푎푦 − 15)

An important element of the GTAModel V4.0 transit assignment is that it also considers transit congestion. This has implications on a customer’s perceived and/or actual travel time due to discomfort or the inability to physically board a vehicle. Modelling congestion effects allows for better capture of parallel route ridership. As of Emme version 4.1.5, it has been possible to use custom functions to calculate congestion. “V4 Fare Based Transit Assignment” uses this capability to assign different congestion parameters to different types of transit service. This helps capture differences that customer’s may perceive or experience. For example, a customer finding one of the last seats on an all seated service such as GO Bus may be less likely to experience or perceive additional travel time than a subway customer boarding a similarly close to capacity vehicle. Under this example, the congestion function for GO Bus would be steeper than the subway function close to capacity, but lower at smaller capacity fractions. In the model, Speiss’ (1990) conical function – the same class of functions available from INRO in the underlying assignment tool – is adapted for multiple service types. “V4 Fare Based Transit Assignment” can output a number of matrices for use in the model: In-Vehicle Travel Time, Wait Time, Walk Time, Fares, Congestion and Total Impedance. These are passed up into subsequent model outer loop iterations (Travel Modelling Group, 2015a).

Chapter 4 Case Study Introduction Case Study Introduction

In order to evaluate the use of GTAModel V4.0 in analysing fine-grained transit policy, this investigation focuses on a particular area in the City of Toronto: the southern portions of Etobicoke and the corridor connecting them to Downtown Toronto. This chapter introduces Toronto as a whole first by briefly summarizing the current conditions of the local transit network (Section 4.1). It then describes the case study area (Section 4.2), some of the problems present in the case study area (Section 4.3) and the available opportunities that motivated this investigation (Section 4.4).

Sections 4.1 also serves as a useful introduction to the conditions of transit in Toronto as a whole and provides background info pertinent to some of the broader policy interventions investigated herein.

Transit in Toronto Today

Public transit in Toronto today is provided by two agencies, the TTC and GO Transit, as well as portions of lines from surrounding agencies. Elsewhere in the Greater Toronto and Hamilton Area (GTHA), transit is provided by a collection of regional (in York and Durham Regions) and city (Hamilton, Mississauga and Brampton, etc.) agencies, as well as by GO Transit.

4.1.1 GO Transit

GO Transit is one of the operating divisions of Metrolinx and provides transit regional transit service across the GTHA and extends into some of the surrounding Greater Golden Horseshoe. GO operates both trains and buses. The rail network is composed of seven lines calling at 63 stations, the majority of which are only served in the peak periods; the bus network extends over 2799 route-km (Figure 7 provides a schematic map of the network). In 2013, ridership was 65.6 million boardings. Many connecting agencies provide discounted fares for patrons also using GO Transit; the TTC is a notable exception (GO Transit, 2014a; GO Transit, 2014b).

Figure 7: GO system map, from (GO Transit, n.d.)

4.1.2 TTC

Under the City of Toronto Act (2006), the City can enforce a monopoly over public transit. In practice, the TTC provides the vast majority of within-Toronto service, with GO Transit providing some premium service within the city boundaries. The TTC operates 141 bus routes, 11 streetcar routes, three subway lines and one intermediate capacity line. Regular TTC routes served 525 million passenger trips in 2013. Additionally, the TTC operates Wheel-Trans paratransit service, as well as five community bus routes (Toronto Transit Commission, 2014a).

The TTC’s subway network effectively consists of four lines. Chronologically, the first is the Yonge Line, running north-south beneath Yonge Street between Front Street and Finch Avenue. Next is the University-Spadina Line, which runs north-south under University Avenue, Spadina Road and Allen Road from Front Street to Sheppard Avenue. It forms an inter-operational “U”

30 with the Yonge Line. Next built was the Bloor-Danforth Line, which runs east-west mainly beneath Bloor Street and Danforth Avenue between Kipling Avenue and Kennedy Road. The final subway is the Sheppard Line, which runs east-west under Sheppard Avenue from Yonge Street to Don Mills Road. Traditionally, the intermediate-capacity Scarborough Rapid Transit (STR) line is treated as if in the subway network. It connects Kennedy Station to the Scarborough Town Centre area. The subway system (including SRT) is shown in Figure 8.

Figure 8: TTC subway map, from (Toronto Transit Commission, 2015c)

For the purposes of this document, the traditional names of the TTC’s rapid transit lines are used, but it should be noted that they are now branded differently (Table 2).

Table 2: TTC line rebranding Traditional Name Rebranded Name Yonge-University-Spadina Line Line 1 Bloor-Danforth Line Line 2 Scarborough RT Line 3 Sheppard Line Line 4

The TTC operates the largest streetcar network in North America (Van der Laan, 2012). Many of the agency’s streetcar routes run east-west on major streets between Bloor Street and Lake Ontario. There are also north-south routes on Bathurst Street and Spadina Avenue, as well as an east-west route on St. Clair Avenue. The streetcar network is shown in Figure 9.

Figure 9: TTC streetcar map, from (Toronto Transit Commission, 2015c)

The street network in Toronto largely follows the standard North American grid pattern. The majority of the bus network maintains this system, allowing for an almost-zero turning requirement along many routes. All standard bus routes connect with either a subway or Scarborough RT station, with the exception of 99 Arrow Rd and 171 Mt Dennis (Toronto Transit Commission, 2013b). Three types of express services exist in the TTC bus network; all use the conventional TTC bus fleet. The first is the Downtown Express. These five routes run during rush hours between inner suburbs and downtown, with no stops along the way. Service on these routes requires an additional fare. The second type is the Rocket network. This is a dedicated limited stop service, with nine routes currently. Finally, many major routes have express branches that have limited stop service along busy portions of the routes (Toronto Transit Commission, 2014c).

Currently, construction is underway on an extension of the Yonge-University-Spadina Line to Vaughan, as well as the Eglinton Crosstown LRT running from the Mt. Dennis area to Kennedy Station. Three other projects are funded: Finch West LRT, Sheppard East LRT and the Scarborough Subway Extension of the Bloor-Danforth Line. Additionally, planning studies are underway on Mayor Tory’s SmartTrack proposal and the proposed Downtown Relief Line, among others.

Study Area

The area under investigation for this case study is the portion of Etobicoke roughly bounded by Highways 401 and 427, the Humber River and Lake Ontario, as well as the corridor connecting the area to Downtown Toronto. This corresponds to Planning Districts 2, 7 and 8 in the City of

Toronto (Figure 10). The study area contains portions of the former municipalities of the City of Etobicoke, the City of York and the (old) City of Toronto, with the majority lying in Etobicoke. Important geographical features in the study area include: the Humber River, which marks the boundary between Etobicoke and rest of the Toronto; Lake Ontario and Humber Bay, which form the southern boundary; and High Park, a substantial urban park in Planning District 2.

Figure 10: Map of Toronto Planning Districts with study area highlighted, adapted from (Data Management Group, 2013)

4.2.1 Road Network

The study area includes a high density of freeways. The north end of the study area is delineated by Highway 401, a highway spanning the most populated region in the country and an important route within Toronto. Near the western edge of the study area is Highway 427, which follows the Peel-Toronto border north into York Region. At the southern terminus of Highway 427 are the

QEW and the Gardiner Expressway. The QEW travels west and south through Hamilton to the US border at Buffalo. The Gardiner Expressway, maintained by the City of Toronto, connects the 427 and QEW with Downtown Toronto. Its eastern terminus is at the interchange with the Don Valley Parkway, another City-owned freeway. Each of the 401, 427 and Gardiner Expressway have a number of interchanges within the study area.

Much of the study area is dominated by suburban detached homes. As such, the street network is generally formed by a strict grid network of arterial roads and irregular collectors and local streets. An exception to the grid pattern is Dundas Street, which runs in multiple different diagonals through the area. Towards the lakeshore, there are many employment lands and the local streets are more grid-like.

High Park forms a bottleneck in the road network. In the narrow (approximately 300 metres) space between the park and Lake Ontario, there are three major roads – the Queensway, the Gardiner Expressway and Lake Shore Boulevard – as well as GO Transit’s Lakeshore West corridor. Immediately north of the park is another major thoroughfare, Bloor Street. There is only one more major street north of that within Planning District 2, Dundas Street, which runs parallel to the CP North Toronto Subdivision that marks the northern boundary of the Planning District.

4.2.2 Transit Network

The main focus of the transit network in the study area is the Bloor-Danforth Line subway (Line 2) of the TTC. There are four subway stations in Etobicoke (from west to east: Kipling, Islington, Royal York and Old Mill) and a number in Planning District 2. As with elsewhere in the city, TTC routes in the study area generally follow simple routes along arterials. The majority of these routes in the study area feed into the Bloor-Danforth line. Some lines in the extreme north and south run eastward to connect with the Yonge-University-Spadina line (Line 1) instead; examples include the 501 QUEEN, 508 LAKE SHORE and 32 EGLINTON WEST. The main TTC hub in the area is Kipling Station, which connects the subway to a number of bus routes including the express 191 HIGHWAY 27 and 192 AIRPORT ROCKET (Toronto Transit Commission, 2013b).

GO Transit also has a footprint in the study area. The Lakeshore West line – GO Transit’s busiest – calls at two stations in Etobicoke, Long Branch and Mimico. Further north, the Milton line interchanges with the Bloor-Danforth line at Kipling and Bloor (Dundas West) Stations (Metrolinx, 2014).

Finally, MiWay (the transit agency of the City of Mississauga) feeds a large number of routes into its terminal at Islington Station (MiWay, 2015).

Gardiner Expressway

As mentioned previously, the Gardiner Expressway features prominently in the study area’s road network. It is an important roadway for commuting and freight and carries, as of 2010, 5650 vehicles per hour eastbound (towards Downtown Toronto) at Dufferin in the AM peak (Waterfront Toronto, 2014).

The expressway has become increasingly in need of major construction work; concrete pieces have regularly fallen off of the elevated portions. In 2013, the City of Toronto approved funding for a long-term repair program, which will stretch late beyond 2025 (City of Toronto, 2015a). The first stages of this work have been ongoing and the city has allocated additional funds to accelerate the work on the order of months. Lane closures have been causing additional congestion valued at $1 million per day (Peat, 2015).

Opportunities

The City of Toronto, in its TOcore project, refers to a desire to “reinforce Downtown’s role within the regional economy and as a generator of jobs for city residents” (City of Toronto, 2015c). It is important, then, that the major access infrastructure for the Downtown area remains robust and capable of handling current and future demand.

There is much debate over the eastern approach to Downtown Toronto. Recently, when given the choice between reconfiguring eastern sections of the Gardiner Expressway (the “hybrid” option) or removing those sections and replacing them with an at-grade boulevard, City Council voted to reconfigure the expressway (Pagliaro, 2015). Debate is also heated with regards to transit. There are many supporters of a “Downtown Relief Line,” which would connect the Bloor-Danforth line east of Downtown directly with stations on the Yonge-University-Spadina line in the Financial

District, bypassing the crowded transfer at Bloor-Yonge Station. Many suggest that this should be the City’s top priority in relation to other transit capital projects (Kalinowski, 2015b).

The western section of the Gardiner Expressway features less prominently in public discussion. Likely, this stems from the higher peak period utilization of that part of the highway (5650 vehicles per hour across three lanes), as compared to the eastern end (4500 vehicles per hour across four lanes) (Waterfront Toronto, 2014). It may be seen as a more “necessary” and therefore less controversial piece of infrastructure.

This western section will see extensive rehabilitation over the coming years. Lane closures have already been regularly in effect, starting in 2014 with the stretch of highway near Strachan Avenue (City of Toronto, 2015a). This lengthy, regular effect on commutes should presumably have a non-negligible impact on commuter behaviour. Although negatively impactful in the short term, some viewed this as an opportunity to improve the local transportation network and enhance understanding of travel behaviour. Prior to the commencement of the rehabilitation, professors with the University of Toronto Transportation Research Institute (UTTRI) proposed a study of potential downtown-oriented transit services. These services would aim to help transition some burdened (or displaced) Gardiner users onto transit while the utility of doing so is greatest, in hopes of creating more habitual transit usage. Meanwhile, surveys conducted before and during construction would illuminate some of the potential mode- and time-shifting behaviours present among the Gardiner users (University of Toronto Transportation Research Institute, 2013).

Ultimately, the proposal was unsuccessful, but the concept of identifying transit services for improvement in the context of the Gardiner rehabilitation remained. The study area used for this investigation was chosen because it represents the general origin areas of Toronto commuters headed Downtown via the Gardiner Expressway. Due to the time sensitive nature of the construction, mode shifting should occur quickly in order to maximize habitual transit usage. Therefore, for this study, the focus is on rapidly implementable and relatively inexpensive transit network improvements. Expensive, slow and potentially contentious projects, such as subways, are avoided. Further expansion on the proposed network improvements follows in Chapter 5.

Chapter 5 Methodology and Workflow Methodology and Workflow

This chapter provides a detailed explanation of the overall thesis methodology. The summary in Section 5.1 gives an overview of the workflow and provides an explanation of the structure for the remainder of the chapter.

Summary

One aim of this thesis is to uncover affordable, effective solutions of improving transit in and between Etobicoke and Downtown Toronto (the study area) and, in doing so, evaluate the effectiveness of GTAModel V4.0 in relatively fine-grained transit scenarios.

Uncovering these solutions required two main processes. The first was the creation of a set of potential solutions; the second was the testing and evaluation of those potential methods.

The scope of possibilities for these methods is virtually unbounded. Therefore, the first step in this investigation (beyond the initial objective formulation) was the definition of a solution typology. This typology (described further in Section 5.2) provided a framework for a focused brainstorming of potential solutions and, eventually, scenarios to test. Iterative changes to the scenario set was performed alongside the preparation of the scenarios in Emme. Further additions to the scenario set were introduced in order to expand the evaluation of GTAModel V4.0. Descriptions of scenarios and their assumptions can be found throughout this chapter and in Appendix A.

Another aim is to examine and illustrate the effectiveness and ease-of-use of GTAModel V4.0 with respect to other, less fine-grained policy interventions throughout the City of Toronto. Many of these interventions were exposed during the scoping and development of scenarios for the Etobicoke case study. Others were added later to complement the scenarios already defined in order to capture a large range of potential use cases for the model. These include additional sweeping policy changes, as well as projects already proposed and funded by local governments.

Network coding of all elements required for these scenarios was done on top of an existing base Emme network, creating a full project base network which contains all potential physical modifications (e.g. new lines and route modifications). A full background description of the existing base network and the resultant project base network is provided in Section 5.4.

Following the construction of the project base network, input files were built to define scenarios and allow for their automated creation. Section 5.5 describes the tool – “Full Network Set Generator” – that performs the automated creation procedure, as well the necessary inputs. Section 5.6 discusses the organization of the input files and setup of model runs.

For the testing process, the previously presented GTAModel V4.0 was used. There are no practical limits to the extent of model outputs from GTAModel V4.0, as it is easy to rapidly develop modules to prepare data. From these outputs, solutions were evaluated. Full description of the output modules is given in Section 5.7 and the evaluation procedure is explained in Section 5.10. A summary flowchart of the entire process is provided in Figure 11.

Brainstorm Solutions

Choose Feasible Scenarios to Test

Project TMG Base Base Code Scenarios Network Network

Scenario Input Files

Network Preprocessing

Time Repeat for scenariosall Period Networks

Common Run Input Files GTAModelV4

Outputs

Evaluation

Figure 11: Overall process flow chart

Scenario Typology

Initial typology development aimed to uncover feasible classes of transit network improvements. These classes were created to fit the “affordable” criteria for potential solutions for the Etobicoke-Downtown corridor. Three classes of improvements were carried: route modification, operational changes and fare structures. These are introduced in the subsections below. Additional interventions crafted for completing the full GTAModel V4.0 evaluation process can also be classified according to these categories.

Descriptions of the process and design of physical network changes and additions implemented for those scenarios requiring them is provided in Section 5.4.2. General descriptions of scenario groups requiring headway speed changes, as well the processes and assumptions for creating those scenarios is provided in Section 5.7. Assumptions and procedures for scenarios involving fare schema modification is given in Section 5.8. A full list of scenarios, as well as descriptions and assumptions, is appended to this thesis in Appendix A.

5.2.1 Route Modification

Route modification includes changes to routing, additional branches and new routes.

5.2.2 Operational Changes

For the purpose of this thesis, operational changes refers to modifications of headway or speed of transit lines. Headway modification models changes to service frequency along a route, usually involving the addition of vehicles to the route. The base network used for this investigation does not contain enough data to explicitly model infrastructure-related changes such as transit signal priority (TSP) and stop removal. As such, speed modification is used as a proxy.

5.2.3 Fare Structures

The TTC currently uses a single-fare system network-wide, with the exception of the premium Downtown Express lines. There are a variety of different possible fare schema, including distance-based alternatives, inclusion of co-fares with GO Transit and reductions to the Downtown Express price.

Proposals and Precedents

Many of the specific methods used in the thesis work are either additions to a long line of precedents or are directly inspired by previous proposals. While bringing new ideas into the process is beneficial, it is important to ground any changes within historical context and existing procedures. This thesis is meant to provide an external view, but is not done blindly. The following subsections describe existing practices and recent proposals from the TTC and other commentators. The ideas presented in the subsections are primarily intended for the case study area scenarios, though many – particularly the alternative fare implementation proposals – are more appropriate for the full network interventions.

5.3.1 Regular Operation Reviews

The TTC’s Service Standards Program (Toronto Transit Commission, 1984) provides insight into route review. It describes three main processes: monitoring and adjustment of existing routes; annual economic evaluations for all routes; and evaluation of routes introduced or modified in the previous year. Route headways are based on two requirements, minimum headway and crowding standards. If crowding standards are reached, an additional vehicle is added. The crowding standards themselves, though, are subject to budget constraints and have fluctuated in the past. Annual reviews are more thorough for those routes ranked in the bottom quartile of revenue/cost ratio or average occupancy. In regards to route additions or modifications, the Program prioritizes minimal cost solutions. These solutions do not require additional vehicles or substantial additional vehicle-hours. As long as standards are met, these solutions can usually proceed. Higher cost solutions are scrutinized more heavily, going through a “Comparative Evaluation” against poor-performing routes. Recommendations for new or modified routes come from various sources, such as staff, councillors and public meetings. In the early 2000s, summary documents were published explaining rationales for decisions regarding each reviewed route (Toronto Transit Commission, 2005). These annual documents are no longer disseminated publicly. Routes are recommended to follow the standard route philosophy. The TTC route philosophy is to create a grid network of routes, of which virtually all connect to subway or RT stations. This strategy takes advantage of the heavily gridded nature of the Toronto street network. It allows for efficient service due to minimal turning requirements at the expense of increased walking times in some neighbourhoods. The Standards Program is a

41 helpful document that guides a logical review process. However, new routes and route modifications are done on an ad-hoc basis. Additionally, no regard is given to examining increased headways for the sake of improving network connectivity.

5.3.2 Additional Service Reviews

Beyond the Service Standards Program, there have also been a variety of larger-scoped plans put forward to the TTC Board. Some of these have recommended broad improvements to route frequencies. The Ridership Growth Strategy (Toronto Transit Commission, 2003b) called for an up-to-10% increase in peak period frequency on busy routes, as well as off-peak increases for major routes, full extended service (6:00am–1:00am) on all routes and a maximum headway of 20 minutes on all routes. The Strategy was approved, although not completely implemented (Toronto Transit Commission, 2003a). A major follow-up to the Ridership Growth Strategy was the Transit City plan for LRT construction. A supporting component of the plan was the “Transit City Bus Plan” (Toronto Transit Commission, 2009). The Bus Plan recommended the full implementation of the 20 minute maximum headway recommendation from the Ridership Growth Strategy. In addition, it recommended the creation of a 21 route Transit City Bus Network that would feature 10 minute-or-better all-day frequency. The Transit City project was cancelled upon Mayor Rob Ford’s election in 2010. By 2014, the 20 minute maximum headway remained absent. However, the TTC’s desire for a dedicated “Ten Minute or Better Route Network” was reiterated in a report entitled “Opportunities to Improve Transit Service in Toronto” (Toronto Transit Commission, 2014d). This document also recommended modification of the crowding standards for both peak and off-peak periods. Such a modification would trigger service increases on many routes and would require a number of additional buses to be purchased, along with the construction of a new garage.

5.3.3 Express Routes

The TTC maintains three types of express bus routes. First is the Downtown Express network, a collection of five peak period routes (numbered 141-145) that run lengthy express portions before making local stops in the Downtown core along the Richmond Street and Adelaide Street one-way loop. Next is the Rocket network of full-day express routes. Finally, there is a similar collection of express branches of standard routes.

This network of express routes has evolved and expanded over the past few decades. By 2001, along with some Downtown Express routes, there were three Rocket routes. At that time, TTC staff recommended expanding (and improving) the network, identifying a number of potential technologies and corridors (Toronto Transit Commission, 2001). Of these corridors, only Albion Road/Weston Road remains without an express service. In 2009, the “Transit City Bus Plan” report (Toronto Transit Commission, 2009) was introduced. It made a number of recommendations towards a bus network supporting the Transit City LRT system. Included in this was a set of 15 express bus routes. Seven of the 15 routes already had express branches; these were recommended to be increased in peak period frequency by 10%. The other eight routes are still without express branches. Of these, the 29 DUFFERIN, 52 LAWRENCE WEST/58 MALTON and 89 WESTON are of relevance to the study area. More recently, the TTC published a report entitled “Opportunities to Improve Transit Service in Toronto,” which called for a number of short-to-medium term improvements (Toronto Transit Commission, 2014d). This included renewed calls for an expanded express bus network. The proposed network included new or enhanced express service on 20 routes, many of which were part of the Transit City Bus Plan’s proposed network. Somewhat of note for this investigation is the proposal for enhanced express service on the 96 WILSON. The document also called for expanded service on the Downtown Express network.

5.3.4 Transit Priority

Transit priority in Toronto is a relatively recent development. Early research included a questionnaire sent to other municipalities on their experience with reserved bus lanes (Toronto Transit Commission, 1973). More extensive study was performed in 1988, identifying priority routes for transit signal priority (TSP) implementation. The study also examined other potential priority measures, including physical separation (Toronto Transit Commission, 1988). This work came to fruition over the following few years. In 1991, the City’s consultant reported on the initial pilot study of six intersections, finding significant reductions in travel times for streetcars and a good cost/benefit ratio (Cansult Engineering Limited, 1991). As of 2013, 350 intersections in Toronto provide TSP for streetcars or buses (City of Toronto, 2013). While successful and cost-effective, these TSP installations use outdated methods. Currie and Shalaby (2008) suggest improving the algorithms used in order to reduce bunching of vehicles and improve travel times.

Physical priority for transit vehicles also exists in Toronto. The 501 QUEEN, 509 HARBOURFRONT, 510 SPADINA and 511 BATHURST streetcars all use dedicated, separated infrastructure for at least a portion of the routes. The other fully separated transit infrastructure in Toronto is the York University Busway, which is used by a number of routes. Partially separated infrastructure include eight sections of HOV lanes on city streets and two sections of peak-period reserved lanes for the 504 KING streetcar. A recent report notes some success with the King Street reserved lanes, but posits that increased enforcement would improve their efficacy (City of Toronto, 2014b). Poor enforcement has been an issue for other attempts at implementing prohibitions, such as left-turn restrictions, to improve travel time and reliability (Toronto Transit Commission, 2001).

5.3.5 Fares

The current TTC fare structure is a single-trip, flat-rate fare. Users must go from origin to destination as directly as possible; no walking is allowed unless required for a transfer. The exception is the 512 ST CLAIR streetcar route, which provides a two-hour time-based transfer. Single trip fares can be paid with cash ($3.00) or a token ($2.80 when purchased in quantities of three or more). The PRESTO electronic fare card is useable on portions of the system for single trip fares. Trips cost the equivalent of a token. A variety of unlimited use passes are available. These include a monthly pass (“Metropass”), a weekly pass, a day pass and the GTA Weekly Pass, which allows for usage of MiWay (Mississauga), Brampton Transit and York Region Transit in addition to the TTC. There are specialized arrangements with GO Transit (GO fare sticker, TTC Times Two), as well as fare supplements for routes operated in York Region and Mississauga. Additionally, special fares and passes are available for students and seniors. Children 12 and under ride free (Toronto Transit Commission, 2015a).

Fares on the TTC have evolved over time. In 1973, the system switched away from its zone system; in 1980, the Metropass was first introduced (Bow, 2015). Recent TTC reports have studied changes to the current fare structure. 2003’s Ridership Growth Strategy (Toronto Transit Commission, 2003b) gave a cursory overview of potential structural changes (namely, fare-by- distance and peak/off-peak fares), but did not recommend any. The “Opportunities to Improve Transit Service in Toronto” report (Toronto Transit Commission, 2014d) recommended implementation of the two-hour time-based transfer – currently used on the 512 ST CLAIR route

– across the entire network. As of the 2015 operating budget, this has yet to be implemented (Toronto Transit Commission, 2015b). Today, tokens and paper transfers are still the norm for single fares. In 2000, the TTC published a report on fare collection systems (Toronto Transit Commission, 2000). It concluded that the TTC was not in need of an automatic fare collection (AFC) system because the existing system performed well in its simplicity and was not in a state of disrepair. Starting in 2001, various GTA agencies began work on a joint farecard system (Greater Toronto Area Fare System - GTAFS, later PRESTO). Initially, the TTC was involved as an observer and only committed to allowing installation of a total of ten turnstiles at five subway stations. However, a funding agreement in 2004 earmarked $140 million for TTC involvement in an “integrated GTA ticketing system.” The TTC subsequently began full participation in the GTAFS process and conducted a business case review that was published in 2007. This review described necessary components of a TTC smartcard system and expressed uncertainty regarding the financial implications of the GTAFS (Toronto Transit Commission, 2007). Eventually it was determined that the TTC would utilize the ultimate GTAFS product, PRESTO. Rollout has been slow, but full transfer to the system will be complete by the end of 2016. Full PRESTO rollout allows the TTC a great deal of flexibility in fare structure (Moore, 2015).

Networks

5.4.1 Base Network

The base Emme network used for this investigation is the current TMG (Travel Modelling Group) 2012 Base Network. This network is maintained by TMG for use by funding agencies and University of Toronto researchers. 2012 is used because it corresponds most closely to the 2011 TTS survey, the majority of which was conducted during 2012.

The base network covers the entirety of the GTHA (Greater Toronto and Hamilton Area). GTHA in this case covers the following municipalities: City of Toronto, Regional Municipality of Durham (Durham Region), Regional Municipality of York (York Region), Regional Municipality of Peel (Peel Region), Regional Municipality of Halton (Halton Region), and City of Hamilton. These are Regions 1 through 6, respectively, under the TTS structuring. These Regions are also subdivided into Planning Districts (1 through 46 in the GTHA). Within the City of Toronto these are the former Planning Districts 1-16 and in the other Regions they represent individual lower-tier municipalities.

Traffic zones for all six GTHA regions are encoded as zone centroids. These are numbered between 1 and 5999. Areas outside the GTHA are agglomerated into a number of zones and are numbered between 6000 and 6999. Zone centroids are also included for transit stations to allow for assignment of Drive Access Transit (DAT) trips. These are numbered between 9700 and 9799 for GO Rail stations and 9800 and 9899 for TTC subway stations.

All elements in the base network conforms to the 2011 EMME Network Coding Standard (NCS11) developed by TMG. NCS11 coding practice ensures that networks will remain compatible with TMG-developed tools and models and that data is relatively easy to share between agencies (Travel Modelling Group, 2012).

The road network in the base network generally contains all arterial and collector roads in the GTHA. In areas where the zone system is denser, some local roads are also included. The road network is also partially defined by some constraints. Firstly, the base network must not be so dense as to exceed any of the Emme license element limits after all processing is complete. The current process used to model transit fare structures in GTAModel V4.0 involves the creation of a “transit hypernetwork.” This procedure generates large amounts of extra nodes beyond the base network and thus the base network is designed such that the node limit is not reached. TMG and most of its funding agencies use Size 13 Emme licenses, so the network is designed to its node limits. Secondly, all roads carrying transit routes must be coded. This is not an important issue in Toronto, where the transit network follows a grid pattern based on major roads, but does introduce some additional road coding requirements in other areas of the GTHA.

With the exception of Durham Region Transit and the agencies in Halton Region, all transit lines in the base network were generated automatically by TMG using General Transit Feed Specification (GTFS) data provided by the relevant transit agencies. The automatic procedure creates stops and lines in Emme. Additionally, trip start and end time data were stripped from the input GTFS data and stored in a file called a Service Table, which lists all trips by Emme line ID and time data.

NCS11 philosophy dictates that modelling assumptions should not be embedded in a base model. Headway and speed data vary over the course of a day and even over the course of a time period (the definitions of which can vary between agencies). Therefore, this data is not provided within

46 the base model. These are generated a later step and are calculated using the Service Table data, which serves as an extension of the base model. Typical practice in the GTHA is to use segment speed data for heavy rail services. These are defined manually. Other lines use line speeds generated from the Service Table (Travel Modelling Group, 2012).

5.4.2 Additions to the Network

As mentioned previously, all physical network changes, including any infrastructure additions, route changes or new lines, were coded in a single project base network. From this base network, it is possible to use any combination of lines, headways and speeds in a model run, as long as those are provided as input to the tool “Full Network Set Generator”, which is described in more detail in the next section. The subsections below describe all of the routes requiring physical network changes and therefore additions to the base network. They are grouped by general themes, typically by the route being modified. Again, a full list of scenarios, including those not described in this section, is provided in Appendix A. This section describes only physical changes; assumptions regarding headways and speeds for scenarios in this section are given in Section 5.7 and again in Appendix A.

5.4.2.1 80 QUEENSWAY

There are two scenarios that introduce changes to the 80 QUEENSWAY route, which normally runs from Keele Station to Sherway Gardens along the Queensway. Scenario 25 shortens the 80 QUEENSWAY to run from Humber Loop to Sherway Gardens. The service along the Queensway and Parkside Dr. is transferred to an extended version of the 77 SWANSEA, running between Runnymede Station and Keele Station around three sides of High Park. Note that this scenario does not conform to typical TTC route planning, as the revised 80 QUEENSWAY does not connect to any subway stations. Scenario 26 combines those same two routes into a single route running from Runnymede Station to Sherway Gardens; service along Parkside Dr. is removed.

5.4.2.2 Express Services

A number of scenarios are included that provide express service along existing routes. This is the same concept used for routes such as the ‘E’ branch of 41 KEELE. Scenarios 28 and 29 apply this concept to the 58 MALTON route, with Scenario 29 extending the terminal from Lawrence

West Station to Lawrence Station. These added routes are entirely express; they only stop at major intersections for their entire length. Similarly, Scenario 30 applies a major intersection- only stopping pattern to a new branch of the 32 EGLINTON WEST bus. Scenarios 31 and 32 provide combined express and local service on the 37 ISLINGTON and 29 DUFFERIN routes, respectively. Local service on 37 ISLINGTON is north of Highway 401 and north of Glencairn Avenue for 29 DUFFERIN.

These scenarios are included to investigate whether there is a market for faster service along these corridors (at the expense of some local service). Note that we must make speed assumptions in these scenarios, as the network does not use actual dwell times at stops.

5.4.2.3 89 WESTON

There are two scenarios, 33 and 45, that combine the 89 WESTON bus route with other routes. In Scenario 33, the 89 WESTON continues north on Weston as far as Steeles Ave., duplicating portions of the 165 WESTON RD NORTH. In Scenario 45, service along Albion Rd. normally provided by the ‘C’ branch of the 73 ROYAL YORK is transferred to a new branch of the 89 WESTON.

In both these cases, the new versions of the 89 WESTON provide direct service closer to Downtown than the options they replace.

5.4.2.4 48 RATHBURN

There are three scenarios, 36, 38 and 39, that modify the 48 RATHBURN route in Etobicoke. These are all very minor changes that are designed to demonstrate the effectiveness of the model with respect to fine-grained physical transit adjustments.

Scenario 36 turns the route southward at Islington Ave., terminating at Islington Station instead of Royal York Station. Scenario 38 extends the route along Dundas Ave., Prince Edward Dr. and Bloor St., terminating at Royal York Station. Scenario 39 uses the same extension as Scenario 38, but moves the terminus to Old Mill Station.

5.4.2.5 15 EVANS

Scenario 41 shifts service from the 15 EVANS route along Evans Ave. to a new branch of the 110 ISLINGTON SOUTH, running from Islington Station to Long Branch GO Station. This consolidates much of the service to the South Etobicoke employment lands to a single numbered route. It does this at the expense of direct service to Sherway Gardens for residential areas along Royal York Rd.

5.4.2.6 161 ROGERS RD

Scenario 42 shifts service on the 161 ROGERS RD route from Oakwood Ave. onto Dufferin St. Oakwood Ave. is already well served by the 63 OSSINGTON bus, which shares a subway connection at Ossington Station, and the extra service along Dufferin St. may help alleviate some congestion on the crowded 29 DUFFERIN route.

5.4.2.7 66 PRINCE EDWARD

Currently, the 66 PRINCE EDWARD is the only route serving Old Mills Station, an isolated subway station on the west bank of the Humber River and one of the least busy stations in the network. Scenario 43 tests shortening the terminus of the route to Royal York Station. Scenario 44 does the same, but also removes Old Mill Station from the Bloor-Danforth Line. The hypothesis in this case is that most of the ridership at Old Mill Station comes from passengers transferring to the subway from the bus3. Without the bus route, the station could then be closed, potentially saving money for the TTC.

5.4.2.8 30 LAMBTON

There is a single scenario, Scenario 27, which affects the 30 LAMBTON bus, a route that runs from Kipling Station to High Park Station via Dundas St. Another route, 55 WARREN PARK, connects the small Warren Park neighbourhood to Jane Station. This neighbourhood is served at its north end by the 30 LAMBTON bus, only the 55 WARREN PARK enters the neighbourhood itself. Scenario 27 combines the two routes. This lengthens travel time on the 30 LAMBTON

3 An Emme path analysis run on assigned TTS demand matrices suggests that approximately 75% of AM boardings at Old Mill Station arrive via the 66 PRINCE EDWARD.

49 and increases travel time to the subway for those normally boarding the 55 WARREN PARK. However, it also provides direct access to the popular Junction neighbourhood for those living in Warren Park and eliminates a very minor route from the network.

5.4.2.9 Ferries

With the challenging geography between Etobicoke and Downtown Toronto, there a few options for surface connections other than on Bloor St. or in the Lake Shore Blvd. and Queensway corridor. One idea that could bypass the bottleneck is to run ferries across Humber Bay. There are obvious challenges to this plan, such as winter ice buildup and congestion at the existing Jack Layton Ferry Terminal. However, a quick test of prospective ridership is worth completing. There are two scenarios, 47 and 48, which examine, respectively, the 900 HUMBER FERRY and the 901 KIPLING FERRY. Both routes terminate downtown at the Jack Layton Ferry Terminal. At the Etobicoke end, the 900 HUMBER FERRY terminates at Humber Bay Park, where it connects with the 501 QUEEN and 508 LAKE SHORE, as well as the 66 PRINCE EDWARD and the 145 DOWNTOWN/HUMBER BAY EXPRESS. The 901 KIPLING FERRY terminates at Samuel Smith Park at the foot of Kipling Ave. near Humber College’s Lakeshore Campus. Direct connection is available to the 44 KIPLING SOUTH bus. Neither route makes any intermediate stops. Vehicle and service characteristics were modeled to be similar to Vancouver’s SeaBus (TransLink, 2009), assuming a passenger capacity of 385, a headway of 15 minutes and a speed of 21 km/h. Mode ‘m’ was assigned to the lines, as the long stop spacing and relatively high capacity vehicle makes the ferry more alike with subway vehicles than buses or streetcars. This mode designation affects the boarding penalty and congestion function used during transit assignment.

5.4.2.10 13 NORSEMAN

Scenario 49 introduces a new route, 13 NORSEMAN, which provides service from Royal York Station along Norseman St. and N Queen St. to Sherway Gardens. This route provides a fourth TTC route to Sherway Gardens (in addition to 80 QUEENSWAY, 123 SHORNCLIFFE and 15 EVANS) and improves access to employment lands around the former CP Rail Obico yard.

5.4.2.11 Funded LRT Projects

There are three LRT projects currently funded in the City of Toronto. One, the Eglinton Crosstown is under construction. Another, the Finch West LRT, is currently in the Request for Qualification (RFQ) stage. The third, Sheppard East LRT, will not begin the construction phase until after the Finch West project is operational (Kalinowski, 2015a).

All three are investigated here. Please note that although these are future projects, they are examined using 2011 population and employment data, as well as a 2011 transit network. All are coded on the network as mode ‘s’ (streetcar), as this is the most appropriate mode already in use. A transit time function (ttf) corresponding to exclusive right-of-way streetcar is used throughout – again, it is the most appropriate one already in use. This ttf value affects the congestion function. The vehicle used for the Eglinton Crosstown is a 260-passenger capacity 2-car train, as per available documentation (Toronto Transit Commission, 2010). It is assumed that the other projects will be using the same vehicles.

Scenario 81 tests the 601 EGLINTON CROSSTOWN. From the aforementioned documentation, an assumption of a 3.5 minute all-day headway is drawn. Similarly, an average speed of 28 km/h is assumed based on the speeds provided for various portions of the route. It is assumed that all bus service along the route is removed and truncated at an appropriate connection point. This affect various branches of the 32 EGLINTON WEST and the 34 EGLINTON EAST. Existing headways on those branches are maintained. The Mt. Dennis Station (the western terminus) is encoded slightly to the west of the proposed location in order to simplify the network. All other stops are coded as per maps available on the project website (Metrolinx, n.d.).

Scenario 82 tests the 602 FINCH WEST LRT. Stops are coded as per Metrolinx documentation (Metrolinx, 2015). Headways are assumed to be the same as the 601 EGLINTON CROSSTOWN, as existing ridership is lower over a shorter corridor (Collins, 2012). An average speed of 22 km/h is assumed as per the Metrolinx business case analysis (Metrolinx, 2009). All 36 FINCH WEST branches are assumed to be truncated and combined into one branch at Keele St. and run east from there at the existing combined headway and average speed.

Scenario 83 tests the 603 SHEPPARD EAST LRT. Stop locations are as per the Metrolinx fact sheet (Metrolinx, n.d.). Frequency is expected to be lower than the other two lines given the

51 existing ridership level (Collins, 2012). For the purpose of this investigation, all-day headways of 5 minutes are assumed. As with the 602 FINCH WEST LRT, an average speed of 22 km/h is used (Metrolinx, 2009). Branches of the 85 SHEPPARD EAST are truncated either at Don Mills or at Morningside; existing headways on those sections are maintained.

5.4.2.12 Transit Congestion Effects

This particular class of scenarios does not require network additions, per se. Instead, duplicates of the original network are made with some modifications. For Scenario 84, vehicle capacities for subway trains are increased such that they are effectively unlimited, thus removing congestion effects from affected lines. Similarly, GO Train vehicle capacities are increased in Scenario 85. Note that it would be possible to maintain the single base scenario policy to run these tests. However, that approach would require duplicate vehicles, as well as duplicate lines. It is much preferred to simply use different base networks in this particular case.

Full Network Set Generator

Early in the development of this thesis, each intervention was encoded as two Emme scenarios – one for AM Peak and one for PM Peak. Each of these Emme scenarios required multiple “child” scenarios to prepare for a model run. All interventions used a base set of Emme scenarios for the remaining three time periods, but the 100 scenario limit for an Emme database quickly filled up. In order to better organize the interventions, the fully pre-processed scenarios were exported using a TMGToolbox tool called “Export Network Package”. Then, at run-time, the accompanying “Import Network Package” tool loaded the scenarios into a different database used solely for model runs (hereafter referred to as the “run database”).

Many inadequacies were discovered with this approach. Firstly, the amount of time required to create off-peak scenarios was deemed to be too great, so base Midday and Evening networks were used for all interventions (transit is not modelled overnight, so the Overnight network remains constant regardless). This meant that all modelled changes were peak-period only. Secondly, the pre-processing required to create a valid input scenario was substantial and mistakes during the process had the potential to yield failed runs later on, thus slowing the entire modelling timeframe. Lastly, the amount of scenarios – and the time required to create them – became vast. A method of simplification was strongly desired.

Shortly after coming to these conclusions, work began on a different project. The structure of that project was similar to this thesis in that it involved comparisons of a large number of scenarios developed from a common base network. Applying what had been discovered from the thesis work, an Emme tool called “Full Network Set Generator” was developed alongside upgrades to a number of other tools in order to completely overhaul the process of constructing scenarios and preparing those scenarios for model runs.

Quite simply, “Full Network Set Generator” takes a full base network (and a number of input files) and converts it into the five time period networks required for compatibility with GTAModel V4.0. It reduces the number of required tool runs needed to produce a full set of time period networks from at least 15 down to one. A summary diagram of the tool is provided below (Figure 12).

Full Base Network

Delete Old Scenarios

Create Transit Time Periods

AM MD PM EV ON

Apply Network Update

Apply Batch Line Edits

Prorate Transit Speeds

Remove Extra Nodes

AM Cleaned MD Cleaned PM Cleaned EV Cleaned ON Cleaned

Publish

Figure 12: Flow chart of Full Network Set Generator

The first step of the tool is to optionally delete scenarios in the locations specified for the new scenarios. For this investigation, a run database is utilized, overwriting old runs when moving to a new intervention, so this option is set to true.

The next is a vital step. At this point, a TMGToolbox tool called “Create Transit Time Period” is called multiple times. This tool is what defines headways and speeds for transit lines for all time periods. The basic concept is that, for a given start and end time, the tool will read in the Service Table and create a new scenario wherein the transit lines are assigned headways and speeds based on the trips in the service table starting within the time period. There are two options for headway calculations: naïve and average. Naïve headway aggregation divides the time period length by the sum of all trips for a given line during that time period. Average headway aggregation reads the difference in start times between all adjacent trips for a given line and averages them. Average headway aggregation tends to overestimate combined frequencies along corridors, whereas naïve aggregation can yield very long headways on individual lines. In order to target unique service characteristics, “Create Transit Time Period” was recently upgraded to allow for line-by-line selection of aggregation type. TMG currently uses naïve aggregation for all agencies with the exception of GO Transit, which tends to have much longer headways on its services. This work uses the same approach.

Another newly implemented feature of “Create Transit Time Period” is the ability to manually set headways and speeds using an input .csv file, termed an “Alt File.” This is particularly useful for assigning values to lines without Service Table values, as well as for future year scenarios. The Alt File is one of the main ways one can define a scenario.

After running “Create Transit Time Period” five times, there is a full set of time periods with fully defined transit line characteristics. Next, the option to apply a .nup file using the TMGToolbox tool “Import Network Update” is given. The .nup file is a .zip file containing macros and scripts to run on a scenario. TMG has historically used this tool to push network changes out to users of the TMG Base Network. Within “Full Network Set Generator”, the typical use case is to apply time period-specific road network changes. For example, some turning movements may only be available during certain times of the day. The network update

55 option allows the user to retain time period restrictions while working off of a single base network. This option is not used in this thesis.

The next called tool is “Apply Batch Line Edits”. This is a new tool written in tandem with “Full Network Set Generator”. It allows for ratio changes to be applied for headways and speeds. Unlike the Alt File component of “Create Transit Time Period”, “Apply Batch Line Edits” applies changes to network expressions, not individual line IDs. The advantage of this approach is that it allows for broad changes to be applied in a single line of the .csv input file (“Batch Edit File”).

Next, the tool calls “Prorate Transit Speed”. This tool assigns segment speeds across a transit line by prorating the line speed according to link free-flow speeds. The standard setting is to apply the calculation to all lines with the exception of those whose segment speeds are already set manually (i.e. heavy rail lines).

Finally, “Full Network Set Generator” calls “Remove Extra Nodes” for each scenario. “Remove Extra Nodes” allows for the removal of superfluous nodes. Superfluous nodes in this context refers to all nodes that connect to exactly two links, neither of which is a centroid connector. This definition ensures that the nodes removed are ones that do not affect either traffic or transit assignment, as all movements remain available. By default, nodes with transit stops are not removed. A hypothetical transit line with only mid-block, non-centroid connected stops would lose all access if this were not the case. However, transit lines are not designed like this in the network. In practice (and in this thesis), transit stop nodes meeting the superfluous criteria are removed. Given the size of the base network, if such nodes were not removed, the eventual “hypernetwork” scenario would exceed the Size 13 node limit (Size 13 is the largest license in use at the University of Toronto).

Once “Remove Extra Nodes” is complete, we have ten total scenarios created from the initial full base network. The first five – created by “Create Transit Time Period” – are termed “uncleaned” networks and the five created by “Remove Extra Nodes” are termed “cleaned.” The cleaned networks are saved using a standard set of scenario numbers, allowing them to be accessed by the remainder of the GTAModel V4.0 modules.

Input and Run Organization

The majority of input files required for a model run are common between all runs for a given project. For this project, this includes data such as zonal population and employment, parking costs and availability, school enrollment data and parameters for many of the component models.

The input files that do change between runs can be considered as defining a scenario. For this investigation, this is a set of three files: the Alt File, a Batch Line Edit file and a Fares schema file. Each broad scenario is assigned a folder with the same name as the scenario. Inside this folder are the three files, as well as text file called Notes, which describes the scenario, and the other input files for “Create Transit Time Period” – namely, an Aggregation file and a Service Table. The latter two do not change between scenarios, but are included for convenience. Together, the Alt File, Batch Line Edit file and Service Table define the service characteristics of the transit network for a given scenario. By defining transit line groups, the Fares file provides instruction for the creation of the transit hypernetwork, as well as the fares within and between those line groups.

Other types of investigations may require different files to change. For example, projects involving horizon year forecasting would not have static zonal population and employment data. Those projects might also use multiple station capacity files for use by the Drive-Access Transit mode in different horizon years.

In order to maximize the efficiency of the model runs, the XTMF multi-run framework is used to run the entire set of model runs in sequence. The multi-run .xml instruction file is capable of issuing a number of commands. For this project, the ability to copy files and folders is often used. Each run is entered as a block of code in the .xml file and is given a run name. Within that code block, instructions are given to copy data from the scenario input folder into the newly created run output folder, to copy the base network to the output folder and to copy the Fares file into a common location. Using relative paths and consistent scenario naming, the model system can access all the input files without any other manipulation, allowing for an entire set of model runs to be completed with no further interaction. This greatly reduces the amount of downtime between model runs; one can queue multiple model runs to execute overnight. The basic portion of a sample code block for an individual run in a multi-run .xml file is provided below.

At the start of each run, “Full Network Set Generator” copies over all scenarios from the previous run. Any post-assignment analyses not performed during the run should therefore be unavailable. To partially avoid this, scenarios containing both traffic and transit assignment results are copied from the “run” database into a “storage” database. Assignment results, specifically strategy files, are extremely large – on the order of 3GB per scenario – and can quickly fill up a hard drive. Due to the size constraints, and due to the maximum of 100 scenarios per database, only AM peak scenarios were copied to the storage database. This allows for any follow-up Emme analysis of trip patterns during the peakiest time of day. Note that any post-run analysis requiring strategy- or path-based analysis needs the same matrix as used in the assignment. For this, the analyst must simply import the matrix from the output folder into the specified location in Emme using the TMGToolbox tool, “Import Binary Matrix”. The following line of code is added to each run entry in the multi-run .xml file to define where to save the copied scenarios. The parameter value corresponds to the value assigned to the given intervention in an Excel spreadsheet used to track all interventions.

The large number of interventions in this investigation causes the 100 scenario limit to be exceeded. As such, an extra line is added for each run entry specifying which of the two storage databases to store the scenario data:

Headway and Speed Calculations and Assumptions

As described in the previous section, headway and speed modifications are performed using the Alt File for many scenarios. For some others, the Batch Edit File is used instead. Generally, scenarios with titles containing “Headway Reduction,” “Improvement4” or “Speed Increase” use the Batch Edit File. Such scenarios include the extent of change in their titles, except for “Headway Reduction” scenarios affecting only a single route. For these particular scenarios, the term “Headway Reduction” specifically means a 20% increase in frequency (i.e. the factor applied to headway in the Batch Edit File is 0.8). Scenarios using the Alt File are typically those that were described earlier in Section 5.4.2 – the scenarios requiring physical network modifications. With the exception of scenarios only adding new routes (i.e. the funded LRTs and the ferries), total vehicle requirements are maintained as closely as possible and rounded vehicle requirements are never exceeded. Line vehicle requirements are calculated using the following equation:

퐿𝑖푛푒 퐿푒푛푔푡ℎ 60 푉푒ℎ𝑖푐푙푒푠 = ( ) ∗ ( ) 푆푝푒푒푑 퐻푒푎푑푤푎푦

For new routes or branches using the same streets as existing routes, speeds are assumed to be a rough average, weighted by distance, of speeds on those existing routes. New express branches use assumed speeds 10% higher than the routes they overlap in order to account for stop removal. This is approximately the average of speed increases seen on existing TTC express branches (Figure 13).

4 Improvement in this context means both a headway reduction and speed increase.

40%

35%

30%

25%

20%

15%

10% Speed Speed Increase 5%

-5%

-10%

Figure 13: Speed increases in TMG base network for AM peak period TTC express branches relative to the local branch. Average and median = 12%.

Fare Modification Scenarios and Processes

As discussed in Section 3.4, fare structures are defined by an input fare schema .xml file and incorporated into the fare hypernetwork. This fare schema file has great flexibility. For this investigation, three main classes of fare changes are incorporated. First, there are a number of interventions related to GO-TTC co-fares – a discount applied when transferring between GO Transit and TTC, meant to encourage the use of both systems for a single trip. Shown below is an example of a bidirectional co-fare. Effectively, this particular example reduces the cost of boarding the TTC from $1.98 to $0.75. If the bidirectional tag were set to “false,” then the fare reduction would apply only when transferring from TTC to GO Transit. TTC Regular GO Transit True

The second group of interventions relates to the Downtown Express buses. In the standard fare schema, riders pay $4.68 to board the Downtown Express (a premium of $2.70 above TTC fare), but receive a discount when transferring to or from the regular TTC routes, which are coded as a separate line group. Together, this is coded as follows.

TTC Regular TTC DT Express TTC Regular TTC DT Express True

Because the two are treated separately, when transferring from the Downtown Express one must initially pay the standard TTC boarding fare. However, this is immediately negated by the bidirectional discount given. There are three scenarios that test the sensitivity of the Downtown Express surcharge, setting it to $0.00, $1.00, and $1.50.

The final set of fare scenarios tests the oft-discussed potential distance-based fare structure for the TTC. There are two scenarios; one sets the base fare to $1.00 and the distance-based portion to $0.075/km and the other sets the base fare to $1.50 and the distance-based portion to $0.050/km. Downtown Express prices are set at double these amounts. The maximum straight- line distance within the borders of the City of Toronto is roughly 40km. This would yield a total fare of $4 and $3.50, respectively, roughly twice the standard fare used. An example of how this is coded in the fare schema is provided below. TTC Regular TTC Regular TTC DT Express TTC DT Express Outputs

There are a large number of output modules in XTMF available for use with GTAModel V4.0. Output modules can be found throughout the module structure. Some output data is used later in the run, either in a subsequent step or in an earlier step in a later iteration. Other data is output solely for evaluation purposes. The following subsections describe a selection of output modules, organized by the step of the model during which they are run.

5.9.1 Post Household Iteration

Household Iteration refers to the process in the Mode Choice model during which we iterate through individual households and assign modes based on maximum household utility. The Post Household Iteration step runs between household iterations5. During this process, three sets of modules are run. Firstly, mode split data is updated. These files contain total trips by mode for each zone pair. Next, the auto demand matrices are updated. Finally, a module called Total Utility Calc is used. This output is purely for evaluation purposes and was developed for this thesis. The following subsection describes it in detail.

5.9.1.1 Total Utility Calc

Total Utility Calc is a module that, for each outer loop iteration, outputs total network utility. For each household, the tool cycles through all persons in the household and calculates utilities for all trip chains for a given person. All trip chain utilities are summed to calculate the total household utility, which is later added to the total network utility. Note that, as this tool is run in Post Household Iteration, resultant values must be divided by the number of specified household iterations to give a single value.

Using a conversion value (the beta parameter for cost), the resultant utility value can be converted from utils to dollars in order to compare it directly to other monetary values. This is explained further in Section 5.10.

5.9.2 Post Household

Post Household occurs after a household has been fully processed by both Scheduler and Mode Choice during a given outer loop iteration. At this time, transit demand matrices are updated, start time and trip length distributions are exported and PoRPoW assignments are extracted. The distributions and assignments are invaluable for model validation.

5 For this thesis 10 household iterations are used, but this is not a researched value. Further work should be done to understand the relationship between model accuracy and runtime for different household iteration amounts.

5.9.3 Post Iteration

Post Iteration occurs after the completion of an outer loop iteration. It is important to note that Emme network assignments are performed at this point. A number of level of service matrices are extracted from Emme after assignment. During Post Iteration we also export station demands for Drive-Access Transit. These values are used in two ways: firstly for validation of station usage and secondly for calculation of station capacity factors, which are used as a scale factor in the Access Station Choice model. Finally, the zonal mode split file is converted into a planning district mode split file. The planning district data is small enough that it can be easily processed in Excel and used to output helpful aggregate mode choice data.

5.9.4 Post Run

For this exercise, the bulk of output for evaluation comes from modules run during Post Run. Post Run occurs after the final outer loop iteration is complete and most modules run at this time interface with Emme. There are a number of modules used in Post Run for this investigation; they are described in the subsections that follow.

5.9.4.1 Accessibility Calculations

In the context of the Accessibility Calculations tool, accessibility refers to the percentage of person-job pairs in a given set of population and employment zones accessible within a given timeframe. This is calculated using the following equation:

∑ 푍표푛푎푙 퐸푚푝푙표푦푚푒푛푡 ∗ 푍표푛푎푙 푃표푝푢푙푎푡𝑖표푛 %퐴푐푐푒푠푠𝑖푏𝑖푙𝑖푡푦 = ∈푆 ∑ 푍표푛푎푙 퐸푚푝푙표푦푚푒푛푡 ∑ 푍표푛푎푙 푃표푝푢푙푎푡𝑖표푛 where S refers to the set of zone pairs accessible within the given time limit. The tool takes in a list of timeframes to analyze, as well as ranges from employment zones and population zones. Accessibility percentages are listed for each specified timeframe and are calculated for auto times, total door-to-door transit times and for transit in-vehicle travel times. For this project, the regularly extracted accessibility values are for Planning Districts 7 and 8 to Planning District 1 and for Planning Districts 7 and 8 to all of Toronto.

5.9.4.2 Export Transit Boardings

The Export Transit Boardings module outputs a .csv file containing boardings for a number of line groups. These groups are specified in an input file that has two columns: one that lists Emme line IDs and one that specifies the name of the desired line group for each line. This is useful tool for both validation and evaluation.

5.9.4.3 Ridership Counts

Ridership is defined here as the number of trips that use a given line group at least once. For example, if one passenger were to board the 22 COXWELL bus, transfer to the Bloor-Danforth subway and then transfer again to the 40 JUNCTION bus, the trip would add three to the TTC boarding count, but only one to the TTC ridership count. The tool takes in a list of scenarios to examine, as well as a set of line filters defining the desired line groups, and outputs a .csv file listing ridership counts for all line groups for each scenario. The base network used for GTAModel V4.0 has a number of short lines that are artifacts of the importing of GTFS data, resulting in slightly inflated boarding counts. Ridership Counts is therefore useful for validation. Additionally, ridership is a useful evaluation metric because we can compare actual trips on a system across different interventions.

5.9.4.4 Line Data

The Line Data module is currently used solely for evaluation purposes. It outputs two .csv files, one of which provides data for a full set of line IDs and the other uses an input aggregation file to summarize the data according to a number of specified line groups, similar to the Export Transit Boarding module. In addition to headway and speed data, the Line Data module outputs an estimate of the number of vehicles required in each of the AM and PM peaks, total distance travelled during the day and an estimated daily operation cost. These metrics are calculated using the following three equations, respectively.

퐿𝑖푛푒 퐿푒푛푔푡ℎ 60 푉푒ℎ𝑖푐푙푒푠 = ( ) ∗ ( ) 푆푝푒푒푑 퐻푒푎푑푤푎푦

퐷푎𝑖푙푦 퐷𝑖푠푡푎푛푐푒 = ∑ 푇𝑖푚푒 푃푒푟𝑖표푑 퐿푒푛푔푡ℎ ∗ 푆푝푒푒푑 ∗ 푉푒ℎ𝑖푐푙푒푠 퐴푙푙 푇푖푚푒 푃푒푟푖표푑푠

푂푝푒푟푎푡𝑖표푛 퐶표푠푡 = 훽0 + 훽1 ∗ 푉푒ℎ𝑖푐푙푒푠퐴푀 + 훽2 ∗ 푉푒ℎ𝑖푐푙푒푠푃푀 + 훽3 ∗ 퐷푎𝑖푙푦 퐷𝑖푠푡푎푛푐푒

Parameter values for the operation cost equation were developed using the 2012 Ridership Summary data available from the TTC. (Toronto Transit Commission, 2012) A number of other linear regression model formulations were attempted using data available in both Emme and the Ridership Summary. This particular model, which proved to be the most robust, is a modified version of a model seen in the literature, using daily distance and AM and PM peak vehicle requirements (Caudill, Kaplan, & Taylor-Harris, 1983). The Adjusted R Square value was found to be 0.995 and all variable coefficients were significant at the 99% confidence level (Table 3).

Table 3: Regression results for daily bus operating costs model Coefficients Standard Error t Stat P-value Intercept 526.79 189.32 2.78 6.25E-03 AM Peak Vehicles 457.69 52.31 8.75 1.43E-14 PM Peak Vehicles 913.42 66.32 13.77 1.30E-26 Km/Day 2.55 0.22 11.72 1.02E-21

The Line Data module is a new tool created for this thesis.

5.9.4.5 Line Loads

The Line Loads module outputs maximum and average loads for each line and time period. This is a useful tool for identifying areas for potential service improvements. Line Loads was created for this investigation.

5.9.4.6 Extract Transit OD Vectors

Extract Transit OD Vectors creates two Emme matrices: one origin matrix and one destination matrix. These matrices represent the ultimate origins and destinations for trips using a line or set of lines specified using a network filter expression. In more simplistic Emme networks, the process would be straightforward. It would consist simply of aggregating the resulting demand matrix of a Select Line Analysis performed on the desired set of lines into both an origin and a demand matrix. However, the inclusion of the Drive-Access Transit (DAT) mode complicates the procedure. DAT trips are observed in the transit demand matrix as originating from or destined for a station parking lot centroid, which are numbered between 9700 and 9899. The Extract Transit OD Vectors tool cross-references such trip totals with the corresponding trips in

65 the auto demand matrix and includes them in the resulting origin and demand matrices. Therefore, the final output matrices include ultimate origins and destinations for a combination of walk-access transit (WAT) and DAT.

Using the node polygon layer in Emme Desktop, combined with a zonal boundary file, it is possible to create a worksheet map for each matrix that displays the data spatially. If the worksheet file is created in advance, the Export Emme Worksheet module in XTMF can be used to automate the process of saving the resulting maps to file. The maps are useful for identifying catchment areas of lines and for examining how network changes affect those catchment areas. For this investigation, maps were created for the Bloor-Danforth Line because it acts as the main transit spine between Etobicoke and Downtown Toronto.

5.9.4.7 Export Station Boardings Alightings

The Export Station Boardings Alightings module takes in a .csv file containing a list of node IDs, along with labels for those nodes, and outputs a file listing total boardings, transfer boardings, total alightings and transfer alightings. Typically, heavy rail station nodes are specified, though any node could be used. The output is useful for validation and for distinguishing between transfer stations and local-access stations.

5.9.4.8 Revenue Calculations

For transit agencies that are modeled purely using a flat fare, revenue can be calculated by multiplying the transit ridership by the boarding fare. The TTC is an example of this, though we must also add additional revenue from Downtown Express boardings. In the GTHA, the only example of pseudo distance-based fare is GO Transit. In GTAModel V4.0, the complicated zonal structure of GO Transit is modeled as an initial boarding base fare plus an accumulated distance- based fare. The base fare can be handled using the same ridership multiplication as explained earlier, but the distance-based portion must be calculated at the transit segment level in the Emme network. The Revenue Calculation module accesses transit volumes across transit segments for a given line group, multiplies them by the distance-based fare on those segments and sums the results to yield a total line group revenue for the selected scenario.

5.9.4.9 Global Weighted Average

The final set of outputs comes from the Global Weighted Average module. This module takes two matrices as input, aggregating one matrix to a single scalar value using the other to define the weights for individual elements. In practice, the weight matrix is usually a demand matrix. Numerous results are possible. Useful validation results include average transit in-vehicle times, transit walk times, transit times, transit fares and vehicle-kilometers travelled (VKTs). For this project, VKTs are a particularly useful metric for measuring network performance.

Evaluation

A number of different metrics are available from the outputs for evaluation of individual interventions. These are compared against data from a base run in order to evaluate the impact of the intervention.

5.10.1 Summary Sheets

Run outputs are organized in a standardized way from run to run and each set of outputs is contained within a folder named using the run name provided. An XTMF model system by TMG is then used to convert the .csv output files into .xlsx format. This can be done with any level of folder nesting, so it is possible to batch this file conversion for an entire set of runs done using the multi-run framework. With the files in .xlsx format, it is easier to extract the data using Excel. TMG recently modified a VBA module for pulling in data from unopen workbooks (by default, Excel can only pull data from explicitly opened workbooks). Using this tool, the large amount of output data can be summarized in what is referred to herein as a Performance Sheet. This workbook has a number of useful summary worksheets for individual runs. To use the Performance Sheet, one must simply provide the path to the run folder containing the outputs in .xlsx format and click a button to run the module.

The Performance Sheet also has a summary worksheet that contains most of the important information a given run. This worksheet is usually enough to make informed comparisons between different interventions. To view these summaries quickly, another workbook called the Consolidated Runs Sheet uses a similar VBA module to pull in the relevant data from any number of Performance Sheets stored in the same folder. All the values summarized in the Consolidated Runs Sheet are listed in Table 4.

Table 4: Consolidated Runs Sheet summary values

Primary Grouping Secondary Grouping Tertiary Grouping Units

Scenario # Number TTC All TTC Bus AM Ridership System All GO All Passengers TTC All TTC Bus Daily Ridership System All GO All Auto Accessibility 10min Auto Accessibility 15min Auto Accessibility 30min Auto Accessibility 45min Auto Accessibility 60min Auto Accessibility 90min In-vehicle Transit 10min Accessibility In-vehicle Transit 15min Accessibility In-vehicle Transit 30min Etobicoke Pop/PD1 Accessibility Emp In-vehicle Transit 45min Accessibility In-vehicle Transit 60min Accessibility Percent (%) Accessible In-vehicle Transit 90min Accessibility Transit Accessibility 10min Transit Accessibility 15min Transit Accessibility 30min Transit Accessibility 45min Transit Accessibility 60min Transit Accessibility 90min Auto Accessibility 10min Auto Accessibility 15min Etobicoke Auto Accessibility 30min Pop/Toronto Emp Auto Accessibility 45min Auto Accessibility 60min Auto Accessibility 90min

Primary Grouping Secondary Grouping Tertiary Grouping Units

In-vehicle Transit 10min Accessibility In-vehicle Transit 15min Accessibility In-vehicle Transit 30min Accessibility In-vehicle Transit 45min Accessibility In-vehicle Transit 60min Accessibility In-vehicle Transit 90min Accessibility Transit Accessibility 10min Transit Accessibility 15min Transit Accessibility 30min Transit Accessibility 45min Transit Accessibility 60min Transit Accessibility 90min Utility Utils Utility Monetary Equivalent Daily Revenue GO Daily Revenue TTC Dollars ($) Daily Bus Operating Revenue/Cost TTC Costs Profit TTC Profit per rider TTC Dollars ($)/rider Profit, net of utility TTC Dollars ($) AM Auto AM Transit AM Active AM Other Mode Splits Percent (%) PM Auto PM Transit PM Active PM Other

A modified Consolidated Runs Sheet was also used during scenario development to compare at- a-glance boardings by transit line, allowing for a quick, yet spatial analysis of line usage by intervention.

5.10.2 Quick Evaluation Tools

There are a number of ways with which we can draw conclusions about the viability of a given intervention. Firstly, if a change has a non-negative cost, it would be expected that the intervention causes an increase in system ridership. As such, ridership is a useful at-a-glance metric. Similarly, improvements to the network should cause increases in accessibility. For this investigation, two accessibility calculations are performed. The first measures how accessible Downtown Toronto (Planning District 1) is to people residing in Etobicoke; the second measures how accessible the entire City of Toronto is for people in Etobicoke. Mode split calculations also provide useful quick comparisons.

5.10.3 Cost and Revenue Data

Perhaps the most interesting and important comparisons are drawn from the revenue and cost data. It is, of course, useful to know the ridership increase for an intervention, but without knowing the cost implications, it is not a powerful decision-making metric. The first calculation to be done is to find the change in profit, where profit is defined as follows:

푃푟표푓𝑖푡 = 퐷푎𝑖푙푦 푅푒푣푒푛푢푒 − 퐷푎𝑖푙푦 푂푝푒푟푎푡𝑖푛푔 퐶표푠푡푠

For this investigation, the TTC is the focus, so profit is only calculated for that agency. Note that operating costs are only available for buses. Most interventions are bus-centric, so looking solely at changes in bus costs is valid in most cases. Because the evaluated value is the difference from base (or another intervention), it is not necessary for the absolute profit value to be calculated, so long as the unknown portions of the profit value (streetcar and subway operating costs, etc.) do not change between interventions.

Difference in profit can be extended further to a more meaningful measure – difference in profit per rider. The TTC is interested in improving ridership only insofar as the marginal cost per rider is maintained below a given threshold6. Profit (or loss) per rider can be calculated as follows:

6 As of 2008, the threshold was 0.23 new riders per dollar spent (Toronto Transit Commission, 2008).

퐷푎𝑖푙푦 푅푒푣푒푛푢푒 − 퐷푎𝑖푙푦 푂푝푒푟푎푡𝑖푛푔 퐶표푠푡푠 푃푟표푓𝑖푡 푝푒푟 푅𝑖푑푒푟 = 푅𝑖푑푒푟푠ℎ𝑖푝

Again, the actual compared value is the difference in profit per rider from base or other intervention.

5.10.4 Consumer Welfare

These profit calculations can be manipulated further to allow for comparisons from a welfare perspective. The premise here is that although an intervention may – from the agency’s perspective – reduce profits (or increase losses) per rider, it may also provide a benefit to society as a whole. If these benefits outweigh the costs, then there is a stronger case for implementation.

The first step in calculating the societal half of the equation is to extract utility data from the mode choice sub-model. As mentioned in Section 5.9.1.1, a module called Total Utility Calc sums up the total utility value across all chosen trips. The value from the final outer loop iteration is then carried forward and divided by the number of household iterations (in this case, 10) to give a representative total. This resultant value is the total network-wide (dis)utility from trips for an entire day.

After running this tool, it is necessary to use a factor to convert the total utility (in utils) to an equivalent monetary value (in dollars) in order to directly compare consumer welfare with the transit costs. The most appropriate way to do this is to divide the total utility by the coefficient for cost in the mode choice utility formulation. Cost parameters are consistent across modes, but they are not consistent across occupation categories. The most accurate method of applying these coefficients would be at the source. When each individual trip utility is added to the total, the utility divided by the trip-maker’s relevant coefficient would be added to another variable storing the total monetary value. At present, this method is not used. Instead, an averaged coefficient is used to factor the entire utility total. This average coefficient is weighted by trips made by each occupation category in the TTS data. The data was accessed using the Internet Data Retrieval System (iDRS) maintained by the Data Management Group (2015). All external trips were not considered, nor were those made by persons with occupation “Unknown” and nor were trips made by mode “Unknown” or “Other.” Non-external trips excluded from the calculation

71 represent less than 0.3% of the total and are not expected to have made a large impact on the final value of 3.541 util/$.

It should be noted that in GTAModel V4.0, (dis)utility is based solely on trip characteristics. There is no utility derived from completing an activity, nor is there any disutility for shifting activity start times. Grether, Kickhöfer & Nagel (2010) show that such extensions of the utility calculation are feasible in multi-agent travel demand models (in their case, MATSim).

5.10.5 Intervention-Specific Tests

In addition to the generic evaluation tools listed above, there are a number of further tests and analyses that can be applied to specific interventions or groups of interventions.

Explanatory power can be added to many intervention results by running path analyses on a line or set of lines. If only conducting this analysis for the AM peak, it is a very simple procedure, as AM assignment results and transit strategies are saved for each intervention in the storage databases. First, the AM transit demand used for that intervention must be imported into Emme using the TMGToolbox tool “Import Binary Matrix.” This matrix is the final demand matrix from the model run and therefore corresponds to the assignment results stored in the storage database. Typically, a transit line extra attribute is created as a marker and set equal to one for the lines of interest. Then, the standard INRO tool “Path-based analysis” can be run, setting the initial and transfer boardings inputs to the marker extra attribute (in order to capture all passengers using the line at some point in the trip) and saving results in an already-created matrix or on an extra attribute, depending on the desired analysis. On occasion, Emme will throw an error that transit strategies were not retained. This error is seemingly unwarranted and has no known solution. In the event that this error occurs, an XTMF model system can be used to quickly load in the transit demand and rerun the transit assignment before proceeding as above.

Building on the path analysis, it is also beneficial to create origin and destination maps for particular lines. While these maps are created in all runs for the Bloor-Danforth Line, other scenarios may require additional maps for other lines. Additionally, it may be desirable to run the “Extract Transit OD Vector” tool twice, including once for a base run, in order to, for example, show the change in catchment area for a proposed line.

Known Bugs

There are two bugs in the model code that are known to have existed during the model runs, but were fixed later. As the model runs take weeks to complete and as there likely wasn’t a large direct impact to the study area or to Toronto as a whole, these bugs were ignored for the purposes of this investigation and are listed below.

External Auto Trips. These were factored down by the number of household iterations. In this case, a factor of 10. The number of missing trips is on the order of tens of thousands. Note that external trips are direct from TTS. They are not run through mode choice and are unaffected, apart from route choice, by changes to the transit network.

External transit trips. These were not included. This includes both WAT and DAT trips.

Computer Performance

With all outputs mentioned in Section 5.9 included, along with using 10 household iterations and four outer loop iterations, model runs take approximately 220 minutes. This run time is observed on a desktop computer equipped with a quad-core Intel i7-4790 (3.60GHz) CPU, 32GB of RAM and Windows 10. The majority of the run time occurs during assignments in Emme, particularly transit assignments.

Chapter 6 Results and Discussion Results and Discussion

The purpose of this chapter is to present the results of all model runs performed, as well as any additional follow-up data extraction not present in the main model data. It provides an extensive discussion of the numerical results of the full set of model runs. The section is broken down by groups of interventions, as the diversity of scenarios is too great to compare them all at once. The majority of analysis is drawn from data found on the Consolidated Runs Sheet. Appendix B provides the completed Consolidated Runs Sheet for the entire set of model runs and Appendix C provides boardings broken down by line group for the entire set of runs. Additional analysis is also included where necessary for comparison and for illustration of some of the capabilities of the model.

Repeatability Testing

GTAModel V4.0 is an extremely complex collection of interconnected and complicated sub- models. To avoid lengthy run times, much of the code is written in parallel. Through the process, it is possible for small errors (floating point rounding, etc.) to be introduced. The data observed shows some unexpected patterns and results, which are seen throughout this chapter. Some minor variation is expected, as it is a complex system. However, the variation is concerning and previous builds of the model system had shown no issues of base run variability, so repeatability testing was performed on the model as part of this investigation.

A total of nine runs were completed, including the base run used throughout the investigation. Inputs for all nine were identical; no difference other than inherent model variations were expected. Selected statistics summarizing the results of those nine runs are provided in Table 5.

Table 5: Summary statistics for selected variables resulting from repeatability testing Min Max Mean Std Dev TTC All 435,312 435,661 435,494 100 AM TTC Bus 256,627 256,819 256,690 61 Ridership System All 571,768 572,011 571,890 83 GO All 98,614 98,792 98,728 64 TTC All 1,559,599 1,560,290 1,559,883 222 Daily TTC Bus 961,415 961,948 961,627 152 Ridership System All 2,065,756 2,066,317 2,065,984 183 GO All 242,099 242,471 242,354 121 Utility -115,954,300 -115,900,400 -115,916,100 17,999 Utility Monetary $ (3,274,620) $ (3,273,098) $ (3,273,541) $ 508 Equivalent Daily GO $ 1,703,266 $ 1,705,963 $ 1,705,083 $ 852 Revenue Daily TTC $ 3,088,247 $ 3,089,625 $ 3,088,785 $ 423 Revenue Daily Bus Operating TTC $ (2,951,169) $ (2,951,169) $ (2,951,169) $ 0 Revenue Costs Profit TTC $ 137,077 $ 138,456 $ 137,615 $ 423 Profit per TTC $ 0.088 $ 0.089 $ 0.088 $ 0.000 rider Profit, net TTC $ (3,137,543) $ (3,134,704) $ (3,135,926) $ 783 of utility

Changes between base runs are minute; the maximum value for daily ridership value represents only a 0.044% increase over the minimum and the coefficient of variation is 0.014%. Regardless, the fact that values are not exactly repeatable between model runs is an important caveat for all results herein.

Similar summary statistics were calculated for AM boardings on all exported line groups. Coefficient of variation exceeds 1% in only 11 TTC routes. Of these, only three have average AM boardings above 100 passengers: 50 BURNHAMTHORPE (1.26%), 81 THORNCLIFFE PARK (1.25%) and 167 PHARMACY NORTH (1.55%). Again, the differences are small, but depending on the context may be potentially impactful.

Large-scale Headway Reductions and Speed Increases

A number of scenarios were created to test wide-reaching changes to headways and speeds. This includes headway reductions on all TTC routes (Scenarios 1-4), headway reductions to streetcars

(Scenarios 5 and 6) and TTC buses (Scenarios 7 and 8), speed increases on all TTC routes (Scenarios 9-11), speed increases on streetcar routes (Scenarios 79 and 80), improvements to headways and speeds on all TTC routes (Scenarios 13-15), changes to the maximum allowable TTC headway (Scenarios 76-78) and headway reductions in the South Etobicoke study area (Scenario 51). Some of these are intended as interesting policy investigations, while others are intended mainly to illustrate general patterns in how GTAModel V4.0 responds to such changes. In some cases, results were also used to identify routes that potentially benefit from targeted improvements, which are covered in Section 6.3.

Table 6 provides selected data for Scenarios 1 through 8, which reduce headways on large segments of the TTC network. Note that profit data is not provided for scenarios with changes to routes other than bus routes because operation costs are not available. TTC ridership appears to increase linearly with indiscriminate headway reductions. At least in the range of 5-20% reduction, there does not seem to be a point at which headway reduction has a marked diminishment in effect. In fact, in all three groups the ridership increase from base to 10% reduction is smaller than the ridership increase from 10% to 20% reduction.

Table 6: Selected data for all major headway reduction scenarios (Scenarios 1-8) Difference from Base Headway Routes Daily TTC TTC Profit, net of TTC Profit Reduction Affected Ridership utility 5% All TTC 15,237 N/A N/A 10% All TTC 31,949 N/A N/A 15% All TTC 48,010 N/A N/A 20% All TTC 65,682 N/A N/A 10% Streetcars 3,321 N/A N/A 20% Streetcars 7,718 N/A N/A 10% TTC Buses 17,254 $ (245,031) $ (230,216) 20% TTC Buses 36,203 $ (557,047) $ (527,208)

This type of scenario is an extremely blunt test, so it is unclear what is generally causing such patterns to emerge (if they are, in fact, patterns). A supplemental analysis is the comparison of boardings on individual routes. This analysis exposes a large amount of interesting information including that are a number of routes steadily losing ridership as headway is decreased. Potentially these routes compete with other, less frequent routes that become more competitive as each gains frequency. There are also other routes that experience extreme increases in

76 ridership – far outpacing the increase in frequency. For example, the 64 MAIN bus experiences a 60% increase over base AM boardings of 544 passengers when all TTC headways are decreased by 10%; this value is 75% when headways are decreased by 20%. Routes in the study area exhibiting such behaviour were added as targeted routes for further model runs. It is also interesting to note the patterns in the aggregate results (Table 7). These results show how some of the modes interact and compete with each other. For example, streetcars headway reductions have an efficiency of approximately 90% in terms of streetcar boardings. That is, for every 10% increase in streetcar frequency, there is an approximate 9% increase in boardings. While overall TTC boardings do increase in these scenarios (as should happen with an increase in service), both bus and subway networks lose riders in the aggregate. This suggests that streetcar routes compete with both bus and subway routes. This is supported by the other scenario results. In the bus headway reduction scenarios, streetcar boardings decrease; in the TTC headway reduction scenarios, streetcar boardings increase, but not to the same extent as in the streetcar-only reduction cases. This behaviour is not seen to the same extent with the bus network. The bus- only scenarios suggest that while buses compete with streetcars for ridership, they drive additional ridership on the subway network. This in unsurprising, given that the modus operandi of the TTC bus network is to be a subway feeder network, in addition to being a well-connected grid. It should also be noted that the decrease in TTC bus ridership between the TTC headway reduction and bus-only headway reduction scenarios is very small, suggesting that bus ridership is mostly driven by the characteristics of the bus network itself.

Table 7: Aggregate AM boarding changes relative to base for headway reduction scenarios (Scenarios 1-8) Percent Change from Base Headway Reduction Routes Affected TTC Bus TTC Streetcar TTC Subway TTC Total 0% Base 337,819 63,430 403,869 805,117 5% All TTC 2.05% 2.66% 1.86% 2.00% 10% All TTC 4.81% 5.85% 3.79% 4.38% 15% All TTC 7.38% 9.56% 5.62% 6.67% 20% All TTC 10.25% 12.93% 7.57% 9.12% 10% Streetcars -0.30% 8.45% -0.22% 0.43% 20% Streetcars -0.49% 18.00% -0.46% 0.98% 10% TTC Buses 4.54% -0.65% 0.25% 1.98% 20% TTC Buses 9.97% -2.35% 0.61% 4.31%

Applying bus headway changes to a particular portion of the network, we observe a similar level of effect. Specifically, bus routes at least partially in South Etobicoke were given headways reduced by 10% (Scenario 51). On those routes, total AM boardings increased by 4.86%, similar to the 4.51% seen in the case of all TTC buses.

One realistic policy test performed varied the maximum all-day headway for TTC routes. As discussed in Section 5.3.2, a proposed 20 minute maximum policy has not been implemented. Data in Table 8 shows that ridership increases are modest at the 20 minute maximum. The increase of 1361 riders represents a value-for-money of 0.06 riders per dollar spent, well below the TTC standard. Moving to shorter maximum headways does not increase this value-for- money.

Table 8: Selected data for maximum all-day headway improvement scenarios (Scenarios 76-78) Difference from Base Maximum All-Day Headway Daily TTC Ridership TTC Profit TTC Profit, net of utility 20 Minutes 1,361 $ (24,518) $ (23,922) 15 Minutes 3,352 $ (67,087) $ (64,710) 10 Minutes 13,480 $ (226,568) $ (216,184)

Model runs were also performed for speed increases. These increases are a proxy for various improvements such as stop consolidation/removal and transit signal priority (TSP). Table 9 provides aggregate AM boarding data for these scenarios. Streetcars perform very well in these

78 scenarios and show only small variation between the complete TTC effects versus the streetcar- only effects. At the route level, the 510 SPADINA and 511 BATHURST both exhibit growth outpacing speed increases for all five speed increase scenarios. A possible explanation is that these two routes compete directly with the University Line subway and increases in speed provide strong incentive for travel behaviour changes. Ridership growth on 15 bus routes also outpace all speed increases. A number of these routes similarly offer parallel competition to a subway line; these include 5 AVENUE RD, 62 MORTIMER, 94 WELLESLEY, 97 YONGE and 113 DANFORTH.

Table 9: Aggregate AM boarding changes relative to base for speed increase scenarios (Scenarios 9-11, 79-80) Percent Change from Base Speed Increase Routes Affected TTC Bus TTC Streetcar TTC Subway TTC Total 0% Base 337,819 63,430 403,869 805,117 5% All TTC 2.10% 3.44% -0.21% 1.05% 10% All TTC 4.24% 7.80% -0.32% 2.24% 15% All TTC 6.29% 10.98% -0.58% 3.21% 10% Streetcars -0.24% 8.05% -0.31% 0.38% 20% Streetcars -0.49% 15.60% -0.68% 0.68% Targeted Route Headway Reductions

Routes chosen for targeted headway reductions were identified through a combination of brainstorming and analysis of large-scale headway changes. These routes are relevant to the Etobicoke-Downtown corridor study area. All routes were given a 20% reduction in headway. Table 10 provides some selected outputs from the targeted headway reduction runs. In all cases, absolute changes are small. This is expected, as of these 12 routes, only five exceed 1000 AM boardings in the base case. Seven of the runs vary by more than one standard deviation of the base case daily ridership, with five of them being positive. However, of these five, only two (40 JUNCTION and 77 SWANSEA) exceed the 0.23 new riders per dollar spent standard. Additionally, when including utility in the calculation, the value for 40 JUNCTION becomes worse than the standard. Another interesting observation is that nine of the 12 routes modified increase AM boardings at a faster rate than headway is reduced (i.e. the nine routes have a greater-than-20% improvement in AM boardings compared to the same routes in the base case).

However, as inferred from Table 10, these boardings must be mainly drawn from other routes, as overall ridership does not keep pace with these individual route increases.

Table 10: Selected data for targeted headway reduction scenarios Difference from Base Route Daily TTC Ridership TTC Profit TTC Profit, net of utility 5 AVENUE RD - 320 $ (1,784) $ (1,601) 15 EVANS - 176 $ (2,041) $ (1,502) 26 DUPONT - 302 $ (2,839) $ (2,686) 37 ISLINGTON 134 $ (7,833) $ (7,714) 40 JUNCTION 238 $ (856) $ (1,285) 44 KIPLING SOUTH 541 $ (2,542) $ (2,864) 50 BURNHAMTHORPE - 192 $ (1,675) $ (1,423) 77 SWANSEA 326 $ (176) $ (241) 94 WELLESLEY 206 $ (2,111) $ (2,207) 110 ISLINGTON SOUTH 133 $ (3,441) $ (3,345) 111 EAST MALL 323 $ (1,869) $ (2,552) 161 ROGERS RD 282 $ (2,423) $ (2,544)

Headway Sensitivity

An important test was that of headway sensitivity. If the model is able to provide meaningful feedback to small changes in individual route headways, then an extension towards using the model for solving Transit Network Problems (TNP) becomes a worthwhile effort. For this test, two routes in Etobicoke were used. The first, 37 ISLINGTON, is a route that runs north from Islington Station along Islington Ave. One branch remains on Islington until turning back at Steeles, while the other diverts westward along Rexdale Blvd. to Humberwood Loop. The other, 50 BURNHAMTHORPE runs largely east-to-west along Burnhamthorpe Rd., also originating at Islington Station. According to TTC data from 2011, the 37 ISLINGTON is one of the busiest routes in Etobicoke, transporting 16,500 riders per weekday, while the 50 BURNHAMTHORPE is a minor route transporting 3,100 daily riders.

Daily TTC ridership generally trends upwards with an increase in frequency on both routes (Figure 14). However, there are some anomalies, with three different scenarios dropping below the base case. Comparing TTC profit (Figure 15) and TTC profit net of utility (Figure 16), there is only one case that could be considered for further analysis – 50 BURNHAMTHORPE with a 10% headway reduction (Scenario 69).

2,500

2,000

1,500

1,000

500

- Changein Daily TTC Ridership 0% 10% 20% 30% 40% 50% -500 Headway Reduction

37 ISLINGTON 50 BURNHAMTHORPE

Figure 14: Daily TTC ridership across different headway scenarios for two routes, relative to base case

$5,000

$- 0% 10% 20% 30% 40% 50% $(5,000)

$(10,000)

$(15,000)

$(20,000)

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$(35,000) Headway Reduction

37 ISLINGTON 50 BURNHAMTHORPE

Figure 15: Daily TTC profit across different headway scenarios for two routes, relative to base case

$- 0% 10% 20% 30% 40% 50% $(5,000)

$(10,000)

$(15,000)

$(20,000)

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$(30,000) Changein Daily TTC Profit,Net Utility of Headway Reduction

37 ISLINGTON 50 BURNHAMTHORPE

Figure 16: Daily TTC profit (net of utility) across different headway scenarios for two routes, relative to base case

Looking more closely at the 10% headway reduction scenario, both daily ridership and TTC profit increase. Clearly then, this exceeds the 0.23 riders per dollar spent that the TTC requires for special service changes. However, the shape of the curves above suggest that the point may be an anomaly and should be approached with a degree of caution.

Minor Route Modifications

A number of scenarios were run to test the effectiveness of GTAModel V4.0 in handling small route changes. A summary of the results of these scenarios is given in Table 11. Of the set of scenarios, only two see an increase in daily TTC ridership – Scenario 25 (80 QUEENSWAY split and reroute) and Scenario 26 (80 QUEENSWAY combine with 77 SWANSEA). Only Scenario 25 shows a ridership improvement larger than one standard deviation of the mean base case ridership. None of the scenarios produce a profit for the TTC, net of utility or otherwise. The ridership increase in Scenario 25 provides 0.144 riders per dollar spent – less than the TTC target of 0.23.

Table 11: Selected data for minor route modification scenarios Difference from Base (Scenario 0) Scenario Daily TTC Ridership TTC Profit TTC Profit, net of utility 25 444 $ (3,067) $ (1,661) 26 48 $ (2,213) $ (1,493) 27 - 505 $ (92) $ (341) 33 - 199 $ (930) $ (1,241) 36 - 213 $ (61) $ (256) 38 - 72 $ (145) $ (585) 39 - 328 $ (948) $ (1,643) 41 - 460 $ (640) $ (1,199) 42 - 356 $ (262) $ (367) 43 - 550 $ (534) $ (2,232) 44 - 1,562 $ (2,545) $ (3,491) 45 - 430 $ (77) $ (192) Express Services

A total of five scenarios (Scenarios 28-32) were used for testing express branches of TTC bus routes. Two of these involved the 58 MALTON and the rest modified the 29 DUFFERIN, 32 EGLINTON WEST and 37 ISLINGTON. As previously mentioned, express branch buses travel at 10% higher speeds; headways are split between existing local service and the express branch.

For all of these routes, except for the 29 DUFFERIN, total boardings on the route (all branches combined) increased in the express scenario (Table 12Table 13). The strongest increase was seen with the 58 MALTON from Lawrence Station. However, this route, as it extends beyond the reach of the existing service eastward, is arguably not comparable to the others.

Table 12: Combined AM boardings on various routes before and after implementation of express service Express Service AM Boardings Base Modified Change 29 DUFFERIN 10,896 10,399 -4.6% 32 EGLINTON WEST 8,849 8,999 1.7% 37 ISLINGTON 3,375 3,545 5.0% 58 MALTON 3,670 3,709 1.1% 58 MALTON from Lawrence Station 3,670 3,970 8.2%

The hypothesis that the 58 MALTON from Lawrence Station is not comparable to the other routes is supported by the total daily TTC ridership data in Table 13, which shows a decrease in

83 overall ridership for that scenario. That same scenario also produced a loss in TTC profit relative to the base, both in standard and net of utility terms. By the metrics in this table, the 37 ISLINGTON is the most promising, showing an improvement relative to base in daily TTC ridership and both profit metrics. This route also showed increases in AM boardings previously.

Table 13: Differences from the base case for a number of outputs from express service scenarios Difference from Base (Scenario 0) Daily TTC Ridership TTC Profit TTC Profit, net of utility 29 DUFFERIN - 196 $ 262 $ 178 32 EGLINTON WEST 134 $ (256) $ (107) 37 ISLINGTON 210 $ 68 $ 590 58 MALTON 319 $ 342 $ (217) 58 MALTON from Lawrence - 93 $ (626) $ (73)

In its entirety, the express service class of scenarios do not allow for any general conclusions regarding their implementation. There are no clear patterns and most results fall within one standard deviation of base case variability. Even considering this, though, the initial finding that the 37 ISLINGTON may be a good candidate is unsurprising, as, apart from the area directly around the terminal, denser areas of the route are generally found toward the end of the line.

Ferries

Scenario 47 shows 34 AM boardings and 85 all-day boardings on the 900 HUMBER FERRY route. Scenario 48 shows 6 AM boardings and 29 all-day boardings on the 901 KIPLING FERRY. Clearly these routes are not ideal for bypassing congestion on routes from Etobicoke into Downtown Toronto. A likely explanation is that none of the terminals serve much demand directly; most trips using the ferries would need to make transfers at either end, making the trip very onerous.

To illustrate this, a path analysis was run on the AM transit demands to show travel patterns of passengers using the routes. Figure 17 shows that the vast majority of 900 HUMBER FERRY users access and egress the route on foot, apart from a small number accessing the Humber Bay Park terminal via the 66 PRINCE EDWARD bus. Additionally, almost passengers start and end their trips south of the Gardiner Expressway, thus illustrating the very confined demand for the

84 service. Similar results for the 901 KIPLING FERRY are seen in Figure 18. Only a small portion of passengers use the 44 KIPLING SOUTH bus for access.

Current transit connections and land uses at the terminals of these proposed ferry routes are insufficient to warrant any consideration of construction. Crossing times are not a significant improvement and docking and boarding times could be lengthy, making this proposal untenable.

Funded LRT Projects

Scenarios 81-83 showcase the three LRT projects funded in the City of Toronto. None of these are simple projects and will likely require extensive redesigns of the connecting transit networks, as well as other associated planning work. The simple tests here do not use any large changes; only a few small assumptions related to redundant bus routing are used. Notably, no changes to population and employment data are used herein.

The model runs suggest that, before considering costs, the Eglinton Crosstown is a far more attractive project than the other two. This is likely somewhat buoyed by the faster, underground portion and the subsequent overall speed advantage that it has over the existing bus services, but the difference is still striking. While both the 602 FINCH WEST (Scenario 82) and 603 SHEPPARD EAST (Scenario 83) show small negative ridership changes from base, the 601 EGLINTON CROSSTOWN shows a marked increase in overall TTC ridership both in the AM peak and all-day (Figure 19). The 601 EGLINTON CROSSTOWN also shows a larger decrease in daily TTC bus ridership. This is to be expected, given that it replaces a more heavily used (in total) set of bus routes. Also, though, its crosstown nature could suggest that it also replaces more trips that were single-route in the past and remain so with the LRT in place. The other two LRT routes are shorter and may require more regular transfers to bus routes, thus maintaining relatively high daily bus ridership.

603 SHEPPARD EAST LRT

602 FINCH WEST LRT

601 EGLINTON CROSSTOWN

-35,000 -30,000 -25,000 -20,000 -15,000 -10,000 -5,000 - 5,000 10,000 Ridership

Daily TTC Daily TTC Bus AM TTC

Figure 19: Various ridership metrics for LRT routes in Scenarios 81-83, difference from base case

Similar results are found in the utility calculations. Table 14 shows the utility and monetary equivalent relative to the base run. Scenario 81 is the only intervention showing significant improvements; Scenario 82 shows a decrease in utility. Incorporating the monetary equivalent into the TTC profit data is infeasible for these scenarios, given that operating costs are not available.

Table 14: Utility data for Scenarios 81-83, difference from base case Scenario 81: 601 Scenario 82: 602 Scenario 83: EGLINTON FINCH WEST LRT SHEPPARD EAST CROSSTOWN LRT Utility 258,800 - 12,000 3,900 Monetary $ 7,308.67 $ (338.89) $ 110.14 Equivalent

Another set of enlightening metrics for Scenario 81 are the accessibility data. For South Etobicoke (Planning Districts 7 and 8) to Planning District 1, the percentage of employment accessible within a 45 minute in-transit-vehicle travel time increases approximately 4% to 46%. For total transit travel time within 60 minutes, the increase is 2.4% to 28.7%. Similar results are seen when considering all employment within Toronto. For the other two LRT scenarios, there are no major increases. This is unsurprising, given that neither directly serves South Etobicoke.

A final interesting set of analyses available from the model runs are the origin catchment areas for the LRT corridors before and after the introduction of the new services. Maps showing these areas can be generated using the “Extract Transit OD Vectors” tool in the TMGToolbox. Figure 20 shows origin catchments for the main bus routes along the Eglinton corridor in the base case (Scenario 0). Figure 21 shows the same map, but for Scenario 81, including the trips using the 601 EGLINTON CROSSTOWN. Looking at these maps, it is clear that the route builds ridership in multiple areas directly on the route, as well as in others that require transfers from local bus routes. Figure 22 clearly shows the impact of the LRT line by comparing origins in Scenario 81 against origins in Scenario 0. Figure 23 provides a similar comparison along the Finch corridor for Scenario 82. The changes there are much more modest and localized. Similarly, the results of the Sheppard corridor comparison for Scenario 83 (Figure 24) show very little growth outside of the Malvern neighbourhood near the eastern end of the 603 SHEPPARD EAST LRT.

Figure 20: Aggregated origins for trips using 32 EGLINTON WEST and 34 EGLINTON EAST, Scenario 0 (base case)

Figure 21: Aggregated origins for trips using 601 EGLINTON CROSSTOWN, 32 EGLINTON WEST and 34 EGLINTON EAST, Scenario 81

Figure 22: Aggregated origins for passengers using Eglinton corridor, difference between Scenario 81 and Scenario 0

Figure 23: Aggregated origins for passengers using Finch corridor, difference between Scenario 82 and Scenario 0

Figure 24: Aggregated origins for passengers using Sheppard corridor, difference between Scenario 83 and Scenario 0

An important consideration for the preceding analyses is that there is no indication if a single sub-model dominates the changes in origin patterns with the introduction of these new LRT services. Particularly, a dominant transit assignment would weaken the argument for using GTAModel V4.0 for this analysis. In order to isolate the remainder of the model, AM transit demand matrices for both the base case (Scenario 0) and the 601 EGLINTON CROSSTOWN case (Scenario 81) were assigned to the network used in Scenario 81. Any changes in origins for passengers using the Eglinton therefore originate in Mode Choice, PoRPoW, Location Choice, etc. The total difference in origins is 2110 passengers, an approximate increase of 8.5%. Figure 25 shows this change spatially.

Figure 25: Aggregated origins for passengers using Eglinton corridor, difference between AM transit demand matrices from Scenario 81 and Scenario 0 assigned to the Scenario 81 network

Fare Schema Modification

With the addition of fare-based transit assignment in GTAModel V4.0, it becomes possible to test a variety of fare schema. For this investigation, three groups of fare schema were tested: Downtown Express surcharge reductions, GO-TTC co-fares and TTC distance-based fares.

Scenarios 55-57 test changes in the Downtown Express surcharge. A clear pattern emerges in the data (Table 15). Daily TTC ridership, AM boardings on the Downtown Express lines and TTC profit (with and without utility included) all increase across the scenarios, though daily ridership

94 and profit net of utility decrease between the base and the first surcharge reduction. With a $0 surcharge on Downtown Express fares, ridership increases beyond one standard deviation of the base mean. With positive profits showing, as well, it may be that eliminating the Downtown Express premium is a good choice for the TTC.

Table 15: Selected data for Downtown Express surcharge changes (Scenarios 55-57) Difference from Base Surcharge Daily TTC Ridership AM Downtown TTC Profit TTC Profit, net of utility Express Boardings $2.70 (Scenario 0) - - $ - $ - $1.50 (Scenario 57) - 141 132 $ 85 $ (296) $1.00 (Scenario 56) 49 228 $ 461 $ 267 $0.00 (Scenario 55) 497 1,531 $ 984 $ 2,051

Scenarios 58-63 test the implementation of GO-TTC co-fares. Scenarios 58-60 test various discounts bidirectionally and Scenarios 61-63 test the same discounts unidirectionally from TTC to GO. Both GO and TTC daily ridership exhibit a strong positive linear correlation with fare discount (Figure 26). At present, transfer data from the model runs are unavailable, so accurate tests of revenue change are not possible. With full PRESTO implementation imminent on the TTC network, these tests are highly relevant.

Unidirectional, $1 TTC Fare

Unidirectional, $0.75 TTC Fare

Unidirectional, $0.50 TTC Fare

Fare Fare Type -

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- Bidirectional, $0.75 TTC Fare GO

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- 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 Daily Ridership (Difference from Base)

GO TTC

Figure 26: Daily GO and TTC ridership compared to Scenario 0 for GO-TTC co-fare tests, Scenarios 58-63

Finally, Scenarios 53 and 54 test two pricing schemes for a potential distance-based TTC fare structure. Scenario 53 uses a $1.00 base fare plus $0.075 per kilometer traveled. Scenario 54 uses a $1.50 base fare plus $0.050 per kilometer traveled. In order to compare revenue and profit data against the base case, “distance-based” fares must be calculated for the base, as they were not incorporated in the initial model run. “Distance-based” in this case refers to fares accumulated on transit segments. In the base case, segment-based fares are accumulated only on TTC routes on cross-boundary routes into York Region. These amount to approximately $29,200 over the entire day. In other scenario comparisons, this amount is assumed not to change. Table 16 provides a summary of output data from these scenarios with the base segment-based fares incorporated. In both distance-based fare scenarios, there are significant increases in daily TTC ridership. The changes to the fare scheme are not profit neutral, though the costs are in line with the TTC standard for service increases, with Scenario 53 matching the standard (0.23 passengers per dollar spent) and Scenario 54 exceeding it (0.59 passengers per dollar spent). For these two schemes, the breakeven distances (the trip length required to pay more than the base case flat fare) are 13km and 10km, respectively. Clearly, then, transit use becomes more advantageous on short distances. In both scenarios, a large proportion of increased transit mode share comes from active modes of transportation that are typically used for short trips. This proportion is higher (in fact exceeding the change in auto mode share) in Scenario 54 likely because the breakeven distance is shorter and therefore the set of trips with which transit becomes more competitive initially had a greater of proportion of active trips. This very small set of analyses suggests that longer breakeven distances (i.e. lower base fares) perform well from a ridership attraction and auto mode reduction perspective, but not from an agency revenue perspective. Equity and complication concerns with these fare schemes have also been raised (Kalinowski, 2014), but these are considered to be outside the scope of this investigation.

Table 16: Selected data for distance-based fare schemes (Scenarios 53 and 54) Difference from Base TTC Profit, AM Mode AM Mode AM Mode Daily TTC Fare Scheme TTC Profit net of Share, Auto Share, Share, Ridership utility Transit Active $1.00 + $ 105,712 $ (461,539) -0.32% 0.69% -0.26% $0.075/km (395,250) $1.50 + $ 35,351 $ (60,110) -0.07% 0.18% -0.12% $0.050/km (42,818) Congestion Testing

An important addition to GTAModel V4.0, relative to previous versions, is the introduction of congested transit assignment. With this introduction, transit headways now have an expanded effect on assignment. In an uncongested assignment, headway only has impact on wait times; in a congested one, headway also has an impact on route carrying capacity, which affects route congestion and subsequently in-vehicle travel times. As described earlier, Speiss’ conical functions are used for modelling the effect of transit congestion on travel times.

Figure 27 and Figure 28 show AM capacity utilization (i.e. v/c ratio) in the base run (Scenario 0) on subway and GO Train routes, respectively. Note that a capacity utilization of exactly 1 yields a transit travel time on that segment double the travel time of a capacity utilization of zero. Values above and below depend on the exponent used for that mode (or whatever other method of varying functions is used).

Figure 27: Capacity utilization on subways (aggregated on links), Scenario 0

Figure 28: Capacity utilization on GO trains (aggregated on links), Scenario 0

From these maps, it is clear there are some major bottlenecks in the heavy rail networks. In the base run, the westbound Scarborough RT, both directions of the Bloor-Danforth Line into Downtown, the southbound Yonge Line (both north and south of Bloor Station), the southbound University Line (south of St. George Station) and the GO Kitchener Line are all at or above capacity in the AM peak period. An interesting test of the model, then, is to examine the effect of relaxing some of these congestion effects. If these bottlenecks did not exist, how would travel in the region change?

Two scenarios were run to respond to this question. The first, Scenario 84, uses modified subway vehicles that allow for virtually unlimited throughput, therefore eliminating congestion effects on the main TTC spines. The second, Scenario 85, applies the same vehicle modification approach

99 to the GO Train lines. Note that capacity is gained solely through capacity changes; thus, only in- vehicle travel times are changed.

Results of these tests suggest that a sizeable amount of latent demand is deterred by heavy rail congestion (Table 17). Region-wide, transit usage increased by approximately 12,600 with uncongested subways and 9,500 with uncongested GO Trains. AM transit mode splits increased in absolute percentages by approximately 0.2% in both cases – a rise of about 1% over the base value. Transit accessibility data shows why such increases occur – passengers are able to access more jobs. In both cases at the 60 minute threshold, South Etobicoke populations are able to reach about 5% more Toronto jobs than in the base case.

Table 17: Selected outputs from Scenarios 84 and 85

Uncongested Uncongested Base Run Subways GO Trains (Scenario 0) (Scenario 84) (Scenario 85) TTC All 1,560,068 1,578,189 1,561,432 TTC Bus 961,948 971,759 960,225 Daily Ridership System All 2,066,184 2,078,837 2,075,689 GO All 242,383 236,990 255,404 Etobicoke Transit Accessibility 45min 7% 8% 8% Pop/Toronto Transit Accessibility 60min 24% 29% 29% Emp Transit Accessibility 90min 76% 79% 79% AM Auto 50.91% 50.76% 50.77% AM Transit 20.40% 20.62% 20.57% Mode Splits AM Active 9.75% 9.72% 9.73% AM Other 18.94% 18.90% 18.93%

To illustrate further the effect of congestion on the transit network, one can compare on a line- by-line basis. In Table 18 it is shown that all GO Train lines see an increase in boardings after removing congestion effects, but the largest increase (29%) is on the Kitchener Line, which was earlier shown to be over capacity in the base scenario.

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Table 18: Boardings by GO Train line, Scenario 85

Base Run (Scenario 0) Uncongested GO Trains (Scenario 85)

Lakeshore West 24,279 27,181 Milton 13,925 14,040 Kitchener 9,544 12,312 Barrie 6,238 6,684 Richmond Hill 3,471 4,034 Stouffville 6,188 6,879 Lakeshore East 19,406 20,745

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Chapter 7 Conclusions Conclusions

As described in Section 1.3, the research objectives for this investigation were twofold: a) the applicability and effectiveness for GTAModel V4.0 was to be examined over a wide range of policy scenarios and b) interventions were sought to provide cheap and effective improvements for the case study area of South Etobicoke and the corridor connecting it to Downtown Toronto. This chapter responds to these objectives and provides conclusions and some recommendations. Section 7.1 describes the policy scenarios tested – with a focus on those in the study area – and discusses which of these appear to be successful interventions. Section 7.2 discusses general findings about GTAModel V4.0 and suggests the range of analysis in which the model may be appropriate for future policy testing. Finally, Section 7.3 suggests future work, including extensions to this research and improvements to GTAModel V4.0.

Interventions

A number of different policy scenarios (or interventions) were tested for this investigation. Many of these were tested for effectiveness in addition to testing for the applicability of GTAModel V4.0. The majority of those tested for effectiveness were those interventions drafted for use in the Etobicoke-Downtown study area. However, some broader interventions are also included in this section.

From the variety of large-scale headway and speed changes, the most striking improvement came from speed increases on streetcars. There is a clear demand for faster local service throughout the downtown and shoulder areas. While the larger Bombardier vehicles being introduced into service may serve to decrease bunching and improve overall line speeds, other options should be examined seriously. Specifically, transit signal priority (TSP) improvements may be beneficial. Work from researchers such as Currie and Shalaby (2008) are a good starting point. It is recommended that the TTC examine potential TSP improvements in detail.

Of the individual targeted routes, only the headway reduction (by 20%) of 77 SWANSEA was successful. From an AM peak headway of 10 minutes in the 2011 base network, the TTC has already reduced the headway to 9 minutes as of 2016 (Toronto Transit Commission, 2016). As it

102 is unclear whether the 20% headway reduction was the ideal amount, it is recommended that some sensitivity analysis be performed in order to inform future scheduling on the route.

None of the minor route modifications performed well. One intervention showed ridership increase, but not in a cost effective manner. It is recommended that none of these modifications be implemented.

Of the express branches tested, the one used on the 37 ISLINGTON bus shows the greatest potential. The ridership and revenue results were positive, but not strong enough to warrant outright recommendation. The characteristics of the route suggest that an express service would be beneficial and it is recommended that additional fine-tuning and analysis of the route be performed using traditional tools.

The proposed ferry alignments across the Humber Bay showed little effect in attracting ridership – new or otherwise. That the terminals are so far removed from trip generating areas limits much of the potential ridership to those transferring on and off of the line. These additional transfers push perceived travel times beyond what is already available today. Neither alignment is recommended for further analysis.

The funded LRT project testing was not strictly intended for evaluation of the project. However, the results do show that the 601 EGLINTON CROSSTOWN is well positioned to perform well immediately. Using 2011 networks and population, the other two projects appear less successful, though specific conditions – such as the Spadina Subway Extension connecting the 602 FINCH WEST LRT directly to the subway not being present in the network – provide inherent disadvantages.

Results suggest that distance-based fares on the TTC would be a boon, at least from regular aggregate metrics. Ridership would increase strongly and the associated costs, while significant, are justifiable within the standard framework used at the TTC. With full PRESTO implementation set for the end of this year, implementation becomes much easier. However, distance-based fares would require a tap-on, tap-off procedure with PRESTO and this would be very difficult to implement on surface vehicles. Given the focus on tight integration between all modes on the TTC network, there likely is no political will to implement a separate fare structure between modes. Additionally, equity concerns have been consistently raised regarding the

103 potential for distance-based fares on the TTC. As poorer neighbourhoods in Toronto tend to be towards the edges of the city (Hulchanski, 2007), those more at-risk financially would be the ones seeing increases in travel costs. With these complications in mind, it is not recommended that the TTC pursue distance-based fares at present.

GO co-fares are used all over the region, with the TTC being the most notable exception. Analysis shows that GO-TTC co-fares would provide significant ridership boosts on both GO and TTC. As Metrolinx implements the Regional Express Rail (RER) program, connections at stations other than Union Station become more viable. A co-fare may then become even more significant. With full PRESTO implementation imminent, GO-TTC co-fares become relatively straightforward, requiring only electronic transfer rules to be coded. There would be some potential challenges regarding allowable transfer times, since neither system requires a universal tap-out7, but these could be overcome using a global allowable transfer window or an estimated travel time from previous tap location. It is recommended that this fare scheme be researched further, with focus on the revenue implications, which were not examined in this investigation.

The examination of the Downtown Express network strongly suggests that the surcharge on those routes be eliminated, as both ridership and profit are expected to increase. Given the limited, infrequent nature of the Downtown Express routes, it is recommended that the base network be reviewed for spatial, headway and speed accuracy on these routes and that the base run data be compared to internal TTC information on Downtown Express route boardings and other performance metrics prior to any decisions on surcharge elimination.

While the tests on congestion effects were intended to showcase the new feature of the model, they also exposed interesting characteristics. It is well-known that there are bottlenecks in the TTC network, particularly around Bloor-Yonge Station. However, this analysis underscores the large latent demand for subway (as well as GO train) travel into Downtown Toronto. It highlights the urgency for relief, either through a new service, improved signalling or something else entirely.

7 GO Transit allows for a “Default Trip” to be assigned to PRESTO cards for GO Train trips. Non-default trips and all GO Bus trips require tap-off (Metrolinx, 2013).

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Together, these analyses show that effective and affordable transit network improvements can take a variety of forms. It stresses the need for creative transit planning and regular experimentation.

Use of GTAModel V4.0

This investigation tested GTAModel V4.0 under a variety of conditions, with the aim being to determine its appropriateness for various classes of policy scenarios. This required full use of the model’s available capabilities, as well as capabilities not already available. Generally, the model was found to be highly customizable and most desired outputs were possible with modules coded for XTMF or tools coded for Emme. Some incomplete analyses, such as the revenue aspect of the GO-TTC co-fare, were left incomplete not because the analysis was not possible even with writing new tools, but rather due to time constraints.

GTAModel V4.0 is not a user-friendly piece of software, nor is the program that interfaces it, XTMF. Use of the model requires extensive knowledge of the software and it is simple to make potentially catastrophic and difficult-to-identify mistakes. The rapid-prototyping aspect of XTMF is both a major strength and a potential weakness. Identifying changes in a model system is only possible through direct examination of its definitional .xml file. A stable, complete model system, though, is relatively easy to work with and the majority of desired outputs come from one section of the model system – Post Run. Adding modules here is not trivial, but interfacing with XTMF, GTAModel V4.0 and Emme through code is well-documented, and a vast repository of sample code is available through the open-source TMG GitHub page. It is therefore recommended that policy analysis be performed with a stable, production version of the model system – isolated from any continued model development. Output modules can be changed, but modules internal to the run should not be modified.

For large-scale changes, GTAModel V4.0 behaves predominantly as expected. The large-scale headway and speed changes show interesting, yet predictable changes in aggregate values. Similarly, the 601 EGLINTON CROSSTOWN test showed a logical shift in route choice and in sub-models earlier in the algorithm. Additionally, the ability to craft virtually any fare schema is vital, and the model appears to respond appropriately to changes in fare structure. This capability is particularly important in this region, as the transition to a highly customizable electronic fare card system is almost complete. Finally, modelling congestion effects is also highly relevant in

105 this region, as much work has been done recently on examining potential relief for the highly congested Yonge-University-Spadina Line. And again, the model behaves appropriately when the effects of congestion on relevant lines are removed.

For smaller scale improvements, though, GTAModel V4.0 does not provide an improvement over existing available tools. For example, for minor route changes, much of the benefit would be anticipated to come from very minute shifts in walking patterns for local patrons. The broadness of individual zones and the fact that each centroid only connects to the network at a small number of nodes limits the applicability of the model in this case. The small travel time and convenience changes expected from this class of intervention is better measured by tools such as MADITUC that are designed for analysis of this sort. Similarly, single route headway changes, particularly to minor routes, are not captured well in the model. The complexity of the behavioural modelling and the small errors introduced throughout perhaps make it unsuitable at present to use the model for such analyses.

In summary, GTAModel V4.0 is a highly capable and customizable piece of software able to model any number of policy scenarios. It incorporates a number of important improvements throughout, including agent- and activity-based modelling, fare-based and congested transit assignment and extended scenario analysis organizational tools. However, there is no evidence to suggest that its use case should expand beyond the large-scale policy analysis typically given to similar models.

Future Work

There are a number of possible changes and expansions possible for both the analysis performed in this investigation and for GTAModel V4.0 itself.

Firstly, further runs could be done testing headways on targeted combinations of routes. There is potential that high quality transit paths could form through the network if two or more connected routes were to see increased service. To identify such potential routes combinations, a number of path analyses could be run on individual routes in the base case.

An expansion on this first set of tests is true optimization of headways and, potentially, route designs. Optimization of headways on individual routes, or even small combinations of routes, is

106 straightforward, and some attempt at this has been done in this investigation in the form of headway sensitivity testing. However, expanding further to the optimization of the whole network would be a type of solution to the TNP class of problems. Unpublished work has been attempted on headway optimization using GTAModel V4.0, but this was ultimately unsuccessful. However, recent updates to the model might make a reformulation of this research worthwhile. It is recommended that efforts be made to reduce run-to-run variation before embarking on any new optimization work.

One problem with the modelling of moderate-scale interventions such as express services on major bus routes is the lack of available dwell time data. This necessitates assumptions about line speeds when removing stops. Potentially, a stop dwell time model could be developed. This model would update after every outer loop iteration. Potential model inputs include stop boardings, stop alightings, physical stop characteristics and vehicle characteristics (Currie, Delbosc, & Reynolds, 2012). Similarly, surface transit speeds do not vary with road network congestion. This limits all but the most cursory attempts for modelling transit priority improvements such as queue jump lanes, though attempts at such analysis may prove difficult even with surface speed updating. Regardless, transit route performance is necessarily connected to road congestion and the model cannot currently handle their interaction.

A possible major change to the model would be the incorporation of a more sophisticated assignment procedure. This would preferably be a dynamic assignment tool such as Dynameq or Matsim. This would allow for better use of the full 24-hour nature of the model, would eliminate any edge effects at time period boundaries and could allow for a truer representation of the transit network, particularly services like the GO Train, which only exist for portions of the peak.

As shown throughout the investigation, GTAModel V4.0 has proven to be a useful tool – one that has certainly expanded its use case relative to its predecessor. However, it does have limitations. The proposed changes above could serve to broaden the potential uses of the model.

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Appendices

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Full Scenario List

ID Name Description Additional Assumptions 0 Base Run Base TMG Network 1 5% Global (TTC) All TTC route headways set to 0.95 Headway Reduction of base 2 10% Global (TTC) All TTC route headways set to 0.90 Headway Reduction of base 3 15% Global (TTC) All TTC route headways set to 0.85 Headway Reduction of base 4 20% Global (TTC) All TTC route headways set to 0.80 Headway Reduction of base 5 10% Streetcar Headway All TTC streetcar route headways set Reduction to 0.90 of base 6 20% Streetcar Headway All TTC streetcar route headways set Reduction to 0.80 of base 7 10% Bus (TTC) All TTC bus route headways set to Headway Reduction 0.90 of base 8 20% Bus (TTC) All TTC bus route headways set to Headway Reduction 0.80 of base 9 5% Global (TTC) All TTC route speeds set to 1.05 of Speed Increase base 10 10% Global (TTC) All TTC route speeds set to 1.10 of Speed Increase base 11 15% Global (TTC) All TTC route speeds set to 1.15 of Speed Increase base 13 5% Global (TTC) All TTC route headways set to 0.95 Headway/Speed of base and speeds set to 1.05 of base Improvement 14 10% Global (TTC) All TTC route headways set to 0.90 Headway/Speed of base and speeds set to 1.10 of base Improvement 15 15% Global (TTC) All TTC route headways set to 0.85 Headway/Speed of base and speeds set to 1.15 of base Improvement 17 94 WELLESLEY All 94 WELLESLEY route headways Headway Reduction set to 0.80 of base 18 26 DUPONT Headway All 26 DUPONT route headways set Reduction to 0.80 of base 19 5 AVENUE RD All 5 AVENUE RD route headways Headway Reduction set to 0.80 of base 20 50 All 50 BURNHAMTHORPE route BURNHAMTHORPE headways set to 0.80 of base Headway Reduction

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ID Name Description Additional Assumptions 21 110 ISLINGTON All 110 ISLINGTON SOUTH route SOUTH Headway headways set to 0.80 of base Reduction 22 15 EVANS Headway All 15 EVANS route headways set to Reduction 0.80 of base 23 44 KIPLING SOUTH All 44 KIPLING SOUTH route Headway Reduction headways set to 0.80 of base 24 111 EAST MALL All 111 EAST MALL route Headway Reduction headways set to 0.80 of base 25 80 QUEENSWAY split Split 80 QUEENSWAY at Humber Headways adjusted to and reroute loop. Extend 77 SWANSEA to take keep vehicle over service along the Queensway requirements stable. and Parkside Dr. 26 80 QUEENSWAY 80 QUEENSWAY terminus moved to Headways adjusted to combine with 77 Runnymede Station and 77 keep vehicle SWANSEA SWANSEA service absorbed. Service requirements stable. along the Queensway and Parkside Dr. removed. 27 30 LAMBTON 30 LAMBTON extended to run Headways adjusted to combine with 55 through Warren Park in both keep vehicle WARREN PARK directions, as per routing of existing requirements stable. 55 WARREN PARK. 55 WARREN PARK service eliminated. 28 58 MALTON express Express service from Lawrence West Stops at all service Station to Pearson Airport. intersections with other routes in the network. Splits main branch into two – one local, one express, allocating half frequency to each. Speed increased by 10% on express branch. 29 58 MALTON express Express service from Lawrence Stops at all service from Lawrence Station to Pearson Airport. intersections with other routes in the network. Splits main branch into two – one local, one express, allocating half frequency to each. Speed increased by 10% on express branch.

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ID Name Description Additional Assumptions 30 32 EGLINTON WEST Express service from Eglinton Station Stops at all express service to Skymark Hub in Mississauga. intersections with other routes in the network. Splits main branch into two – one local, one express, allocating half frequency to each. Speed increased by 10% on express branch. 31 37 ISLINGTON Express service from Islington Station Stops at all express service to Highway 401; local service from intersections with Highway 401 to Steeles Ave. other routes in the network. Splits main branch into two – one local, one express, allocating half frequency to each. Speed increased by 10% on express branch. 32 29 DUFFERIN express Express service from Exhibition Place Stops at all service to Glencairn Ave.; local service from intersections with Glencairn Ave. to Wilson Station. other routes in the network. Splits main branch into two – one local, one express, allocating half frequency to each. Speed increased by 10% on express branch. 33 89/165 combined Combines 89 WESTON with portion Some headway service of 165 WESTON RD NORTH on reduced on full 165 Weston Rd. WESTON RD NORTH branch to maintain vehicle requirements. 89 WESTON headways maintained. 36 48 RATHBURN 48 RATHBURN shortened to Headways adjusted to shortening terminate at Islington Station. fully utilize existing vehicle requirements.

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ID Name Description Additional Assumptions 38 48 RATHBURN 48 RATHBURN lengthened via Headways adjusted to lengthen via Dundas Dundas St., Prince Edward Dr. and keep vehicle Bloor St. requirements stable. 39 48 RATHBURN to Old 48 RATHBURN lengthened via Headways adjusted to Mill via Dundas Dundas St., Prince Edward Dr. and keep vehicle Bloor St. and terminus changed to requirements stable. Old Mill Station. 41 15 EVANS reroute 15 EVANS removed and new branch Headways adjusted to of 110 ISLINGTON SOUTH added keep vehicle running on Islington Ave., Evans requirements stable. Ave. and Browns Line to Long Branch GO Station. 42 161 ROGERS RD shift Move Oakwood Ave. section of 161 from Oakwood to ROGERS RD to Dufferin St. via Dufferin Davenport Rd. 43 66 PRINCE EDWARD 66 PRINCE EDWARD terminus Headways adjusted to to Royal York truncated to Royal York Station. fully utilize existing vehicle requirements. 44 66 PRINCE EDWARD 66 PRINCE EDWARD terminus Headways adjusted to to Royal York and truncated to Royal York Station. Old fully utilize existing shutter Old Mill Mill Station removed from the Bloor- vehicle requirements. Danforth Line. 45 73 ROYAL YORK C Albion Rd. portion of 73 ROYAL Existing headways branch shift to 89 YORK ‘C’ branch added to new maintained on all WESTON branch of 89 WESTON. 73 ROYAL sections. YORK ‘A’ branch extended to connect with 89 WESTON. 47 900 HUMBER FERRY Ferry connecting Jack Layton Ferry 385 total capacity Terminal on Queen’s Quay with vehicle, coded as ‘m’ Humber Bay Park. mode. 21km/h and 15 minute all-day headways. 48 901 KIPLING FERRY Ferry connecting Jack Layton Ferry 385 total capacity Terminal on Queen’s Quay with vehicle, coded as ‘m’ Samuel Smith Park. mode. 21km/h and 15 minute all-day headways. 49 13 NORSEMAN New bus route connecting Royal 21km/h and 15 minute York Station and Sherway Gardens all-day headways. via Norseman St. and N Queen St. 51 South Etobicoke 10% All bus route headways in South Headway Reduction Etobicoke (Planning Districts 7 and 8) set to 0.90 of base. This includes 15 EVANS, 30 LAMBTON, 32 EGLINTON WEST, 37

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ID Name Description Additional Assumptions ISLINGTON, 44 KIPLING SOUTH, 45 KIPLING, 46 MARTIN GROVE, 48 RATHBURN, 49 BLOOR WEST, 50 BURNHAMTHORPE, 52 LAWRENCE WEST, 58 MALTON, 66 PRINCE EDWARD, 73 ROYAL YORK, 76 ROYAL YORK SOUTH, 79 SCARLETT RD, 80 QUEENSWAY, 110 ISLINGTON SOUTH, 111 EAST MALL, 112 WEST MALL, 123 SHORNCLIFFE, 191 HIGHWAY 27 ROCKET, 192 AIRPORT ROCKET 53 TTC Distance Based Base TTC boarding fare set to $1.00 Downtown Express Fares (1 0.075) and distance-based fare applied of fares double the $0.075/km. regular fare. 54 TTC Distance Based Base TTC boarding fare set to $1.50 Downtown Express Fares (1.5 0.05) and distance-based fare applied of fares double the $0.050/km. regular fare. 55 Elimination of DT Downtown Express surcharge Express surcharge reduced from $2.70 to $0.00. 56 Reduction of DT Downtown Express surcharge Express surcharge (1) reduced from $2.70 to $1.00. 57 Reduction of DT Downtown Express surcharge Express surcharge (1.5) reduced from $2.70 to $1.50. 58 GO-TTC Co-fare Bidirectional co-fare between GO and (bidirectional 0.5) TTC set to $0.50 (i.e. savings of $1.48 in each direction). 59 GO-TTC Co-fare Bidirectional co-fare between GO and (bidirectional 0.75) TTC set to $0.50 (i.e. savings of $1.23 in each direction). 60 GO-TTC Co-fare Bidirectional co-fare between GO and (bidirectional 1) TTC set to $0.50 (i.e. savings of $0.98 in each direction). 61 GO-TTC Co-fare (one Bidirectional co-fare between GO and direction 0.5) TTC set to $0.50 (i.e. savings of $1.48 transferring from TTC to GO). 62 GO-TTC Co-fare (one Bidirectional co-fare between GO and direction 0.75) TTC set to $0.50 (i.e. savings of $1.23 transferring from TTC to GO). 63 GO-TTC Co-fare (one Bidirectional co-fare between GO and direction 1) TTC set to $0.50 (i.e. savings of $0.98 transferring from TTC to GO). 64 37 ISLINGTON All 37 ISLINGTON route headways Headway Reduction set to 0.80 of base

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ID Name Description Additional Assumptions 65 40 JUNCTION All 40 JUNCTION route headways Headway Reduction set to 0.80 of base 66 77 SWANSEA All 77 SWANSEA route headways Headway Reduction set to 0.80 of base 67 161 ROGERS RD All 161 ROGERS RD route Headway Reduction headways set to 0.80 of base 68 50 All 50 BURNHAMTHORPE route BURNHAMTHORPE headways set to 0.95 of base 5% Headway Sensitivity 69 50 All 50 BURNHAMTHORPE route BURNHAMTHORPE headways set to 0.90 of base 10% Headway Sensitivity 70 50 All 50 BURNHAMTHORPE route BURNHAMTHORPE headways set to 0.70 of base 30% Headway Sensitivity 71 50 All 50 BURNHAMTHORPE route BURNHAMTHORPE headways set to 0.50 of base 50% Headway Sensitivity 72 37 ISLINGTON 5% All 37 ISLINGTON route headways Headway Sensitivity set to 0.95 of base 73 37 ISLINGTON 10% All 37 ISLINGTON route headways Headway Sensitivity set to 0.90 of base 74 37 ISLINGTON 30% All 37 ISLINGTON route headways Headway Sensitivity set to 0.70 of base 75 37 ISLINGTON 50% All 37 ISLINGTON route headways Headway Sensitivity set to 0.50 of base 76 20 Minute Maximum Combined headway not to exceed 20 TTC service summary All Day Headway minutes on any route during any time compared to period. maximum headway and route headways factored as necessary. 77 15 Minute Maximum Combined headway not to exceed 15 TTC service summary All Day Headway minutes on any route during any time compared to period. maximum headway and route headways factored as necessary. 78 10 Minute Maximum Combined headway not to exceed 10 TTC service summary All Day Headway minutes on any route during any time compared to period. maximum headway and route headways factored as necessary.

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ID Name Description Additional Assumptions 79 10% Streetcar Speed All TTC streetcar route speeds set to Increase 1.10 of base 80 20% Streetcar Speed All TTC streetcar route speeds set to Increase 1.20 of base 81 601 EGLINTON 601 EGLINTON CROSSTOWN 3.5 minute all-day CROSSTOWN introduced. headways, 28 km/h. 260 total capacity vehicle, mode ‘s’. 32 EGLINTON WEST and 34 EGLINTON EAST routes truncated at intersection with LRT. Existing headways applied. 82 602 FINCH WEST 602 FINCH WEST LRT introduced. 3.5 minute all-day LRT headways, 22 km/h. 260 total capacity vehicle, mode ‘s’. 36 FINCH WEST branches consolidated into one branch running east of Keele at existing headway and speed. 83 603 SHEPPARD EAST 603 SHEPPARD EAST LRT 5 minute all-day LRT introduced. headways, 22 km/h. 260 total capacity vehicle, mode ‘s’. Branches of 85 SHEPPARD EAST truncated at intersection with LRT. Existing headways applied. 84 Uncongested Subways Congestion effects removed from Virtually unlimited subway lines. capacity provided on subway vehicles (not including Scarborough RT). 85 Uncongested GO Trains Congestion effects removed from GO Virtually unlimited Train lines. capacity provided on GO Train vehicles.

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Consolidated Runs Sheet (Standard)

Please refer to supplementary file.

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Consolidated Runs Sheet (AM Boardings)

Please refer to supplementary file.