Hurontario-Main LRT

EMME Model Report Report

November 2013

Prepared for: Prepared by: City of Mississauga and City of Brampton Steer Davies Gleave 1500-330 Bay St Toronto, ON, M5H 2S8 Canada

+1 (647) 260 4861 www.steerdaviesgleave.com

EMME Model Report

CONTENTS

1 INTRODUCTION ...... 1 Background ...... 1 2 OVERALL MODELLING APPROACH ...... 3 Traffic Modelling Suite ...... 3 Provision of inputs for Business Case Appraisal and Ridership Forecasting ...... 3 Overview of existing HOT model ...... 3 3 AM MODEL REFINEMENTS ...... 5 Overview...... 5 Traffic Flows on Hurontario ...... 5 Auto vehicle speeds and journey times ...... 9 GO Rail Network ...... 9 4 AM 2031 BAU HOT MODEL DEVELOPMENT ...... 14 Overview...... 14 Transit Network ...... 14 Auto Network ...... 23 Planning Data ...... 28 Economic Parameters ...... 31 5 AM 2031 LRT HOT MODEL DEVELOPMENT ...... 32 Overview...... 32 LRT Alignment ...... 32 LRT Operations ...... 32 Bus Integration with LRT Operation ...... 35 Changes to the Auto Network ...... 37 6 PM HOT MODEL DEVELOPMENT ...... 40 Overview...... 40 7 HOT MODEL FORECASTS FOR THE CORE SCENARIO ...... 41 Overview...... 41 Core Assumptions ...... 41 Preliminary Results ...... 42 AM Base / Business As Usual Modelling results ...... 43

Contents EMME Model Report

AM LRT Modelling results ...... 47 Benchmarking ...... 59 Annual Ridership ...... Error! Bookmark not defined. Additional Findings ...... Error! Bookmark not defined. Impact of LRT on Highway Flows ...... 63 8 HOT MODEL OUTPUTS FOR USE IN VISSIM AND BCA WORK ...... 66 Overview ...... 66 Interaction with VISSIM ...... 66 Benefits Case Appraisal ...... 67 SYNCHRO Modelling ...... 67 9 CONCLUSIONS ...... 68

FIGURES

Figure 1.1 Figure showing the context of the current work ...... 1 Figure 1.2 Overview of Route and Stops for Hurontario Main LRT ...... 2 Figure 3.1 Hurontario Corridor Auto flows Southbound ...... 5 Figure 3.2 Hurontario Corridor Auto flows northbound ...... 6 Figure 3.3 Forecast Population and Employment growth 2006 to 2031 ...... 7 Figure 3.4 Changes in Travel Demand 2006 to 2031 ...... 7 Figure 3.5 Percentage growth in Highway Capacity and Traffic Demand ...... 8 Figure 3.6 GO Rail Boardings – 2006 Observed & Modelled Values ...... 10 Figure 3.7 GO Rail Vehicle Access – 2006 Observed & Modelled Values ...... 10 Figure 3.8 GO Rail Vehicle Access – 2031 Observed & Modelled Values ...... 11 Figure 3.9 Diagram Illustrating the 2006 Model Adjustment Process ...... 12 Figure 3.10 GO Rail Boardings – 2006 Observed & Revised Model Values ...... 13 Figure 3.11 GO Rail Vehicle Access – 2006 Observed & Revised Model Values ...... 13 Figure 4.1 LOCAL TRANSIT SERVICES ADDED TO REFLECT 2012 NETWORKS ...... 15 Figure 4.2 GO Bus Services added to reflect 2012 Networks ...... 16 Figure 4.3 Service Changes to serve new Development Areas ...... 18 Figure 4.4 updated go bus services ...... 19 Figure 4.5 Diagram showing the Transitway Services ...... 21

Contents EMME Model Report

Figure 4.6 ‘Big Move’ Projects included in the Model ...... 22 Figure 4.7 Differences between the 2006 Base and 2031 BAU networks ...... 24 Figure 4.8 Parking Charges applied within the 2006 Model ...... 26 Figure 4.9 Parking Charges applied within the 2031 Model ...... 27 Figure 4.10 Change in Modeled Employment Density from 2006 to 2031 ...... 29 Figure 4.11 Change in Modeled Population Density from 2006 to 2031 ...... 30 Figure 5.1 Illustration of the Option 4 Service Pattern from the PSOP ...... 32 Figure 5.2 TraveL Time Illustration for MiWay Services 19 and 103 ...... 33 Figure 5.3 Figure Ilustrating Headway Reliability in the Corridor ...... 34 Figure 5.4 EMME Lanes difference Plot for BAU & LRT Auto Networks ...... 38 Figure 5.5 EMME Capacity difference Plot for BAU & LRT Auto Network ...... 39 Figure 7.1 AM BAU Transit Flow on Hurontario (Southbound) ...... 45 Figure 7.2 AM BAU Transit Flow on Hurontario (Northbound) ...... 45 Figure 7.3 AM BAU Auto Flow on Hurontario (Southbound) ...... 46 Figure 7.4 AM BAU Auto Flow on Hurontario (Northbound) ...... 46 Figure 7.5 Brampton GO – Downtown Mississauga – Brampton GO Load Profile ... 49 Figure 7.6 Port Credit – Downtown Mississauga – Port Credit Load Profile ...... 50 Figure 7.7 AM Peak Hour SOuthbound Load Profile ...... 51 Figure 7.8 AM Peak Hour Northbound Load Profile ...... 52 Figure 7.9 Brampton GO – Downtown Mississauga – Stop to Stop matrix...... 56 Figure 7.10 Port Credit – Downtown Mississauga – Stop to Stop Matrix ...... 57 Figure 7.11 Average Weekday Ridership ...... Error! Bookmark not defined. Figure 7.12 Boardings per Km (Daily) ...... Error! Bookmark not defined. Figure 7.14 Annual Ridership ...... Error! Bookmark not defined. Figure 7.15 Comparison of Daily to Annual Ridership FactorError! Bookmark not defined. Figure 7.16 Boardings per Km (Annual) ...... Error! Bookmark not defined. Figure 7.17 Population to Average Weekday Ridership ...... 62 Figure 7.18 Average Ridership per Station...... Error! Bookmark not defined. Figure 7.18 AM Peak Hour Auto Flow on Hurontario (Southbound) ...... 64 Figure 7.19 AM Peak Hour Auto Flow on Hurontario (Northbound) ...... 64 Figure 8.1 SUMMARY OF process to generate 2031 VISSIM Matrices ...... 66

Contents EMME Model Report

TABLES

Table 4.1 Service Changes to Regional Transit using Transitway ...... 20 Table 4.2 Summary of Planning Data information in Model ...... 28 Table 4.3 Table showing Economic Parameters Used within the model ...... 31 Table 5.1 Derivation of IVT Equivalence ...... 34 Table 5.2 Derivation of Headway Reliability Factor ...... 34 Table 5.3 Summary of IVT Calculation ...... 35 Table 5.4 Bus Network Changes to Integrate with LRT ...... 35 Table 7.1 Table showing Evolution of Modelling Results...... 42 Table 7.2 AM Peak (6-9am) Transit Boardings (Demand) ...... 43 Table 7.3 AM Peak (6-9am) Hurontario Routes Transit Boardings ...... 44 Table 7.4 Example Travel Times including Generalized Travel TimesError! Bookmark not defined. Table 7.5 AM Peak Transit Boardings on Hurontario ROutes ...... 47 Table 7.6 AM Peak Period LRT Demand and Boardings ...... 47 Table 7.7 Maximum Peak Hour Auto Flow ...... 63 Table 8.1 EMME Model Outputs used in the BCA ...... 67 Table 9.1 AM Peak Period LRT Demand and Boardings ...... 68

APPENDICES

A GO RAIL DATA

B MODELLING RELIABILITY AND QUALITY TECHNICAL NOTE C PM UPLIFT TECHNICAL NOTE D ANNUALISATION FACTOR TECHNICAL NOTE

Contents EMME Model Report

1 Introduction

Background

1.1 Steer Davies Gleave, as part of a consortium led by SNC Lavalin, has been appointed by the City of Mississauga and City of Brampton to undertake the Preliminary Design and Transit Project Assessment Process for Hurontario Street/Main Street Light Rail Transit. This follows the Hurontario/Main Street study that developed a Corridor Master Plan and recommended Light Rail Transit (LRT) technology for the corridor. This study, overall, will complete the preliminary design and progress the project through the TPAP process and position the project for implementation. Figure 1.1 shows the overall timeline for project development and implementation, while Figure 1.2 shows the general project alignment and stop locations.

FIGURE 1.1 FIGURE SHOWING THE CONTEXT OF THE CURRENT WORK

1.2 One of the components of the work involves the development of traffic and transit ridership forecasting tools to determine the ridership impacts and traffic effects of the LRT; this report sets out that work.

1.3 This report provides information on the EMME modelling work, including the ridership forecasting, inputs into the Business Case Appraisal (BCA) work and the provision of traffic flows for further detailed study in the traffic micro-simulation VISSIM model.

1.4 Following this introductory chapter, the remainder of this report contains eight further chapters:

I Chapter 2: Overall Modelling Approach I Chapter 3: AM Model refinement; I Chapter 4: AM 2031 BAU HOT Model development; I Chapter 5: AM 2031 LRT HOT Model development; I Chapter 6: PM HOT Model Development; I Chapter 7: HOT Model Forecasts for the core scenario; I Chapter 8: HOT model outputs for use in BCA and VISSIM work; I Chapter 9: Conclusions.

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FIGURE 1.2 OVERVIEW OF ROUTE AND STOPS FOR HURONTARIO MAIN LRT

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2 Overall Modelling Approach

Traffic Modelling Suite

2.1 The modelling element of the Hurontario/Main LRT project is undertaken using a suite of three transport modelling and traffic engineering software, plus a number of spreadsheets, macros and other processes to import and export the relevant data between each software package.

2.2 The principal model platforms are:

I EMME Higher Order Transit (HOT) Strategic Model – used to undertake confirmatory modelling for both transit ridership and traffic flows on the Hurontario-Main corridor and on corridors parallel to the LRT. It provides base and future demand matrices for use in VISSIM models. I VISSIM Micro-Simulation Model – used to assess the direct traffic impacts arising from the LRT including auto journey times and delays, changes in signal operation, through and turning lane requirements, reliability in LRT run times, and output for SYNCHRO analysis and 3D models. I SYNCHRO Intersection Analysis – used to assess individual offline intersection operation.

2.3 These models operate with different levels of detail, but interact to provide an overall consistent approach to modelling the impacts of the LRT project on transit and traffic demand within the transportation network. Throughout the modelling work, the emerging outcomes were fed into the design process to further refine the final design. This document is limited to the work undertaken in EMME, and the outputs from it.

2.4 Further detail of the process utilized to transfer information between the HOT model and the VISSIM models in provided in Chapter 8 of this document.

Provision of inputs for Business Case Appraisal and Ridership Forecasting

2.5 In addition to utilizing the EMME model to provide traffic flows for use in VISSIM and Synchro, outputs from the EMME model have also been utilized within the development of the Benefits Case Analysis (BCA) work and to provide information on ridership to inform the LRT system design process.

2.6 These workstreams are reported separately, however some further information on the data that was passed from EMME has been provided in Chapter 8 of this document.

Overview of existing HOT model

2.7 The existing Higher Order Transit (HOT) Model was developed by MMM and Professor Eric Miller for use in the Hurontario Main Street MasterPlan Study. Further detail on model operation can be found in the documentation listed below:

I HOT model review report prepared by SDG – May

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I Hurontario Main Street Study . Model Architecture and Validation - Draft Nov 6, 2009 . App X Travel Demand Forecasting Report - April 2010

I Transportation Trends and Demographics Hurontario Main Street Study Jan 09 I The following GTA Model Memorandums have also been reviewed:- . Memo-1_Definitions_Jun-28-08 . Memo-2_Socio-Economics_Jun-29-08 . Memo-3_Trip-Generation_Jun-30-08 . Memo-4_Base-Network_Jun-30-08 . Memo-6_Mode-Choice-Trip-Files_Aug-15-08 . Memo-7_TTS-vs-Census_Nov-02-08 . Memo-8_Trip-Distribution-Models_Feb-18-09 2.8 The HOT model uses a combination of the Greater Toronto Area (GTA) model versions 2 and 3, which have been developed over a number of years, primarily by Professor Eric Miller and his team at the University of Toronto. The model currently resides on the Data Management Group (DMG) servers at the University of Toronto and can be accessed remotely by authorized users.

2.9 The model consists of a four stage transportation model, in which the trip generation, distribution and mode split stages are coded and run through the bespoke xtmf software developed by Professor Miller. The xtmf front end software allows users to input data, call procedures and implement model runs. The assignment models are run in EMME and stored in an EMME databank which can be called through the xtmf front end.

2.10 The model is fully synthetic i.e. demand matrices are created based on the use of key assumptions and parameters within the modelling process, rather than being based on observed data. Observed data was used in the original calibration and validation process, with all demand matrices and assigned flows being based on the forecast synthetic matrices.

2.11 It represents the AM peak period, with zoning and networks representative of the GTA area. The model is of a typical structure and functionality for considering strategic changes to a transportation network.

2.12 A number of refinements were made to the model before utilizing it to produced updated ridership and traffic forecasts. The work that was undertaken is detailed in the chapters which follow.

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3 AM Model refinements

Overview

3.1 The original intent for the demand forecasting did not include the need for any changes to the 2006 AM Base Model developed by MMM as part of the original Hurontario Main Street Study.

3.2 During the production of the initial forecasts, it became apparent that there were a small number of key issues that would need further investigation before the model could be used with confidence for this project. These were:

I Relatively static and even small falls in the levels of traffic on Hurontario between 2006 and 2031; I Auto vehicle speeds and journey times; and I Forecast demand on GO Rail, in particular at key stations within the study corridor. 3.3 Each of these issues are set out in more detail below, along with the action taken (if any) to mitigate the impacts.

Traffic Flows on Hurontario

3.4 When the modelled auto volumes along Hurontario were compared between the 2006 models and the 2031 Business as Usual (BAU) models, the volumes were very similar. This was unexpected given the forecast growth along the Hurontario corridor and so further analysis was undertaken.

FIGURE 3.1 HURONTARIO CORRIDOR AUTO FLOWS SOUTHBOUND

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FIGURE 3.2 HURONTARIO CORRIDOR AUTO FLOWS NORTHBOUND

3.5 To better understand the pattern of traffic flows in the area, auto data was also compiled for the corridors adjacent and parallel to Hurontario, as well as the network characteristics for each of the modelled years (e.g. number of lanes etc.)

Land Use Changes 3.6 Between 2006 and 2031, significant overall growth is forecast within the Mississauga and Brampton areas, however this growth is not equally distributed throughout. The figure below sets out the forecast changes in population and employment.

3.7 Mississauga is largely built out already, and so the future development will largely be focussed on infill and targeted densification. It therefore equates that the greatest growth will be seen in Brampton.

3.8 Brampton still has significant areas of undeveloped land and so is able to grow strongly, particularly in the west. Going forward, land use policies designed to better balance employment with population will reduce the need for people residing in Brampton to travel beyond Brampton for employment.

3.9 The Hurontario corridor is already significantly developed, so is limited in the amount it can grow in terms of population and employment. As is the case for Mississauga as a whole, this will be focussed on infill and targeted densification.

3.10 Land use densification also encourages transit use, and transit expansion plans combined with limited planned highway improvements lead to a forecast increase in mode share for transit. With transit starting from a low base share, this results in a

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large percentage increase. Auto travel share gradually reduces as transit options become more competitive and the need to travel for employment is reduced.

FIGURE 3.3 FORECAST POPULATION AND EMPLOYMENT GROWTH 2006 TO 2031

FIGURE 3.4 CHANGES IN TRAVEL DEMAND 2006 TO 2031

3.11 Alongside land use growth, changes to the transport network will determine travel demand across Peel and how this is split between highway and transit, and between the various highway corridors and transit services. Even without any land use changes,

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such transport network changes will affect existing travel patterns and demands along various corridors and transit services.

3.12 Changes to the highway network by 2031 are limited to selected capacity improvements on the existing network, alongside new highway capacity in support of land development, notably in western Brampton.

3.13 Transit networks will develop in line with land use changes and growth in demand, and in support of policy objectives to encourage transfer from auto to transit. In Peel, this includes Mississauga BRT, enhanced MiWay and Brampton Transit services, development of the Zum network and GO Rail enhancements.

Planned Changes to Auto Capacity 3.14 Hurontario is paralleled by competing corridors (Mavis, McLaughlin and Kennedy) and these provide alternatives routes for many north-south auto trips. At present, Hurontario is the busiest of the four corridors.

3.15 Looking forward to 2031, these competing corridors are planned for capacity expansion, with the exception of Hurontario itself. This allows general north-south traffic growth to be absorbed by these parallel roads and divert through traffic away from Hurontario, with the result that Hurontario sees only modest traffic growth, but still remains the busiest corridor.

FIGURE 3.5 PERCENTAGE GROWTH IN HIGHWAY CAPACITY AND TRAFFIC DEMAND

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Auto vehicle speeds and journey times

3.16 As was the case with the traffic flows on Hurontario set out above, very little change was seen in auto speeds and journey times along the corridor (normally a factor providing a ‘push’ for people to switch from auto to LRT.)

3.17 This issue is partly explained by the fact that the auto volumes do not increase significantly between 2006 and 2031, and so a significant change in journey time is not anticipated. However, the more material issue remains over the absolute journey times in the corridor being considered as excessively fast.

3.18 Having discussed the issue with Professor Eric Miller, it was acknowledged that this is a common issue with GTHA models which can only be rectified by the application of a revised set of volume-delay functions and re-calibration of the demand model.

3.19 Practically, the demand model functions were calculated with these higher speeds in place and so while the absolute values are considered high, this has been taken account of in the calibration process, and so the relationships between the modes should be operating correctly. Based on this conclusion, no further work was undertaken on this issue.

GO Rail Network

3.20 The forecast demand on GO Rail, in particular at key stations within the study corridor, were not producing credible values, and it was agreed with the client that some work was required to resolve, or at least mitigate the issue.

3.21 Discussions were undertaken with members of the original project team, including Professor Eric Miller and a pragmatic approach was developed in order to produce more credible forecasts for the GO Rail network.

3.22 Adjustments were also made to the 2031 future year networks and information on this work is set out in chapter 4 of this document.

3.23 The issue of ‘unrealistic’ boardings at Brampton GO Rail station was first identified when the first full set of ridership and traffic forecasts were produced in September 2012. Upon further investigation, it was discovered that the issue was not limited only to Brampton, but that the correlation between observed and modelled boardings was poor across Peel and Halton. Issues with Auto access at stations was also identified as being a potential issue.

3.24 The graphs below which were presented at a modelling meeting in December 2012 illustrate the lack of correlation between modelled and observed trips for the original 2006 model.

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FIGURE 3.6 GO RAIL BOARDINGS – 2006 OBSERVED & MODELLED VALUES

FIGURE 3.7 GO RAIL VEHICLE ACCESS – 2006 OBSERVED & MODELLED VALUES

3.25 The tables in Appendix A1-A3 set out the full values produced by the original 2006 AM Base HOT model and compares the values output by the model with observed data based on boarding and car park occupancy counts undertaken between 2006 and 2008.

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3.26 It can be seen that in general, at the stations furthest away from Toronto boardings, auto access and transit access are generally underestimated, while closer to Toronto, they are generally overestimated.

3.27 As part of the investigation into the issue, data was also examined from the 2031 AM MMM BAU model. The forecast auto access appeared to have a better correlation with observed data (existing parking spaces), other than at Brampton which appeared to be somewhat of an outlier.

FIGURE 3.8 GO RAIL VEHICLE ACCESS – 2031 OBSERVED & MODELLED VALUES

3.28 Two different methodologies were adopted for the 2006 and 2031 models in order to try and mitigate the issue. In 2006, a post model adjustment process was developed for GO Rail trips, which involved the following steps:-

. Modify forecast matrices by replicating 2031 trip patterns in 2006 and scaling the 2006/2031 trips to ‘sensible’ values . Assign locally in EMME (away from HOT model on DMG server) . Use the new EMME assignment as a basis for both auto and transit flows and as part of the flow transfer to VISSIM

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FIGURE 3.9 DIAGRAM ILLUSTRATING THE 2006 MODEL ADJUSTMENT PROCESS

2006 2031 Geographic zones GO Rail zones Geographic zones GO Rail zones

Copy 2031 matrix columns for car/transit access to GO stations/zones to respective 2006 matrix

columns and scale to observed demand.

Geographic zones Geographic zones Geographic

Copy 2031 matrix rows for demand on GO Rail to

2006 matrix and scale to observed boardings.

GO GO Rail zones GO Rail zones

Car/Transit access to GO zones GO Rail trips to final destination

3.29 Based on the better correlation between the observed and modelled values in the 2031 model, and the desire to avoid the need to undertake a post-model adjustment after every run, a different approach was adopted for the future year models.

3.30 It was decided to revise the network specification to reproduce observed GO demand, concentrating on mitigating the attractiveness of each of the stations using parameter values within the model - @freq and @pkcst. Ordinarily these would directly relate to the hourly frequency of the train service at the station (@freq) and the parking charges at each of the stations (@pkcst).

3.31 Following a discussion with Professor Eric Miller and after undertaking a significant number of tests to determine the best values to use, new values were adopted to produce results that were closer to the observed values than previously, focussing on GO stations within the HMLRT corridor. In order to do this, it was required to determine ‘target’ 2031 values, and for this we utilized outputs from the Greater Golden Horseshoe Model (GGHM) and GO Rail Parking Study analysis.

3.32 Target GO boardings were developed based on a review of the predicted GGHM forecasts and comparing these with the original 2031 HOT model forecasts. Where there were significant discrepancies in the numbers, professional judgement was used to determine the ‘correct’ target value. Target ranges for car access were developed based on the availability and current utilization of parking at the GO stations and planned capacity expansions.

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FIGURE 3.10 GO RAIL BOARDINGS – 2006 OBSERVED & REVISED MODEL VALUES

FIGURE 3.11 GO RAIL VEHICLE ACCESS – 2006 OBSERVED & REVISED MODEL VALUES

3.33 When figures 3.11 and 3.12 are compared with figures 3.7 and 3.8, it can be seen that the correlation between modelled and observed values has improved considerably. Checks were made that these changes resulted in limited impacts elsewhere on the network before they were adopted. The methodology was discussed and agreed with Metrolinx prior to implementation.

3.34 The revised GO rail boardings and values for auto and transit access at each of the GO rail stations are provided in Appendix A4 – A6.

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4 AM 2031 BAU HOT Model development

Overview

4.1 Input for developing the revised AM 2031 networks was received from the City of Brampton, the City of Mississauga, local and regional plans and from discussions that were held with Metrolinx. The future year transit network was also informed by work carried out as part of the Ultimate Transit Network Plan (UTNP) workstream.

Transit Network

4.2 The initial changes to the transit networks were made to reflect changes to transit service that had been implemented between when the Base model had been built in 2006 and the present day (2012) transit services.

4.3 Depending on the type of service (local, regional or planned), the main data sources varied, and further detail on each of these is provided below.

Local Bus Services 4.4 The 2031 local bus networks were initially updated to reflect the latest 2012 local transit networks in the Cities of Mississauga and Brampton. Outside of the Brampton and Mississauga areas, the transit network is consistent with the 2006 networks, other than for the services outlined below. This proved to involve a significant coding effort as the services have changed significantly since they were coded for the 2006 model by MMM.

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FIGURE 4.1 LOCAL TRANSIT SERVICES ADDED TO REFLECT 2012 NETWORKS

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FIGURE 4.2 GO BUS SERVICES ADDED TO REFLECT 2012 NETWORKS

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4.5 Additional changes to CoM/CoB networks were undertaken to reflect the anticipated increases in network demand between the present day and 2031. To this end, City of Mississauga transit frequencies were uplifted by 24%, and those in Brampton were uplifted by 11% to reflect demand growth. For services in the Hurontario / Main Street corridor, an uplift of 40% was utilized. These figures were based on the change in network boardings between 2006 and 2031 model runs in the initial scenarios that were undertaken.

4.6 It was considered that in the future, bus travel times will be slower than those in the current timetable, and so the VISSIM auto journey times were compared for 2006 and 2031. These showed an average journey time increase of between 3% and 5%. A journey time increase of 5% was applied to the 103 and 502 express routes operating in the corridor, while a uniform increase of 3% was applied to all other Mississauga and Brampton local services.

4.7 The coding was supplemented in the Brampton area, as between the existing year and the 2031 forecast year, a number of new areas are planned to be developed and it was considered important to provide adequate transit provision for these areas. These services were developed adopting a pragmatic approach and using professional judgement and assumed to develop consistently with the 2009 Transportation and Transit MasterPlan (TTMP).

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FIGURE 4.3 SERVICE CHANGES TO SERVE NEW DEVELOPMENT AREAS

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FIGURE 4.4 UPDATED GO BUS SERVICES

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Regional Bus Services 4.8 The initial model runs were undertaken with limited updates to the regional bus network, although it later became apparent following discussions with Metrolinx that the planned Mississauga transit way would have a significant impact on GO regional services. The BRT services 407 and 427 that were included in the initial runs were removed and full service on the Mississauga BRT was included.

4.9 The table and figure below set out the services that were diverted to utilize the new transitway, with material revisions required to network coding.

TABLE 4.1 SERVICE CHANGES TO REGIONAL TRANSIT USING TRANSITWAY

Minimum Travel AM Peak Headway Time Route Description Direction (min) (min) Length (km)

BRT Mainline (local) Winston 100 Churchill Blvd to Kipling Both 10 34 25.8

Meadowvale Town Centre to Kipling 109 via Erin Mills Parkway/Busway (local) Both 10 55 32.3

Clarkson GO to Malton Westwood via 110 UTM/Busway (local)/Airport Both 7-8 66 37.4

Shoppers' World to Kipling via 202 Hurontario/CCTT/Busway (limited) Both 7-8 45 29.4

Kipling to Drew St via Busway 205 (limited)/Dixie Road NB/WB 10 39 21.6

Meadowvale Town Centre to Kipling 209 via Creditview Road/Busway (limited) SB/EB 7-8 56 31.2

Meadowvale Town Centre to Kipling 219 via W Churchill Blvd/Busway (limited) SB/EB 7-8 45 32.2

Kipling to Meadowvale Town Centre via Busway (limited)/Mavirs/Financial 261 Ctr NB/WB 15 57 36.5

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FIGURE 4.5 DIAGRAM SHOWING THE TRANSITWAY SERVICES

GO Rail Services 4.10 The GO Rail network remains reasonably unchanged into the future, and a pragmatic approach was taken not to add new planned stations to the network that were well away from the study area. Service frequencies were updated to be consistent with the GO Rail Electrification Study Reference Case timetable.

‘Big Move’ Planned Projects 4.11 Metrolinx were consulted as to the projects that we should include within our modelling work and the list of projects is set out below. The assumption was that only projects that have already received full funding should be included.

I MiWay & Züm committed expansions as planned by CoM/CoB I Mississauga Transitway I GO Rail – Electrification reference case service plan I Eglinton Crosstown (McCowan to Black Creek) I Sheppard LRT I Finch West LRT I Spadina subway extension to VMC I ATO implementation on Yonge-University-Spadina subway line I York Region Viva BRT

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FIGURE 4.6 ‘BIG MOVE’ PROJECTS INCLUDED IN THE MODEL

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Auto Network

4.12 Only projects that have received full funding or been completed post-2006 are included in the 2031 network, and these include:

I 400 series intersection reconfigurations I 410 extension I 401 widening at Hurontario I Network additions around Mississauga downtown I Brampton network development consistent with TTMP I Miscellaneous GTHA highway projects away from corridor 4.13 It should be noted that for the purposes of this study, that the downtown Mississauga auto networks are coded as for the present day situation, and that the improvements coded as part of the Downtown21 plan have not been included. The majority of the changes highlighted in the diagram below involve capacity increases relating to an increase in the number of lanes, apart from in Brampton where there are a significant number of new roads planned.

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FIGURE 4.7 DIFFERENCES BETWEEN THE 2006 BASE AND 2031 BAU NETWORKS

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Parking Charges 4.14 The Base 2006 model has parking charges in the HMLRT corridor limited to just the Square One mall for Mississauga, with two zones in downtown Brampton having charges.

4.15 The initial 2031 LRT forecasts adopted the parking charge assumptions utilized for the Hurontario Main Street MasterPlan Study. This assumed that in 2031 a $10 parking charges would be introduced to multiple zones along the corridor as well as to additional zones within downtown Toronto.

4.16 It was felt that as parking charges had not also been applied to zones in corridors adjacent to the study area, that this could unduly influence demand along the corridor and potentially force trips away from destinations in the corridor. It was also considered that a number of these parking lots would be on private land and linked with businesses and so blanket charging would be unlikely. Such a high blanket charge in the corridor also led to unrealistic HMLRT demand. Further testing therefore removed charges from the HMLRT corridor.

4.17 As part of the project development process, the issue of parking charges was revisited and it was decided to apply charges to the downtown areas of Mississauga and Brampton only.

4.18 The definition of an explicit charge for testing purposes is affected by a range of considerations:

I Who is actually paying (employers may pay for employee parking); I Parking duration; I Frequency of parking and hence payment method (a permit providing a far lower average daily cost than paying daily); and I Real terms growth in charges (typically 1-2% p.a.). 4.19 In addition, for model testing, parking charges are being used as a proxy for wider TDM measures, only part of which will be restrictions on parking availability and cost. On that basis, it was decided to apply a common charge across the respective downtown areas of $5.

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FIGURE 4.8 PARKING CHARGES APPLIED WITHIN THE 2006 MODEL

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FIGURE 4.9 PARKING CHARGES APPLIED WITHIN THE 2031 MODEL

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Planning Data

4.20 For the majority of the forecasting work undertaken, the planning data assumptions remained unchanged for those used in the MasterPlan study and are consistent with the “Places to Grow’ official forecasts. Category totals for the planning data are presented below, followed by maps illustrating the changes in the density of population and employment in the study area.

TABLE 4.2 SUMMARY OF PLANNING DATA INFORMATION IN MODEL

Population (000s) Employment (000s)

2006 2031 % Change 2006 2031 % Change

Brampton 453 738 63% 156 319 104%

Mississauga 698 812 16% 431 519 20%

GTHA 6,250 8,318 33% 3,227 4,574 42%

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FIGURE 4.10 CHANGE IN MODELED EMPLOYMENT DENSITY FROM 2006 TO 2031

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FIGURE 4.11 CHANGE IN MODELED POPULATION DENSITY FROM 2006 TO 2031

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Economic Parameters

4.21 There are a number of economic parameters used within the model, and these are set out below. These sit outside the EMME model, but instead are altered within the xtmf parameters set before undertaking a model run on the DMG server. As value of time is not used directly to convert monetary values into time equivalents within the utility functions, the values have been uprated in lines with assumed changes in real value of time growth and cost. This is set out in the following table.

TABLE 4.3 TABLE SHOWING ECONOMIC PARAMETERS USED WITHIN THE MODEL

2006 VoT Cost 2031 Parameter value growth change value

Transit time value of money (min/$) VoT 8 1.64% 12.01

GO incr. fare x dist ($/km) Cost 0.084 1.64% 1% 0.07

Base Auto Drive Cost ($/km) Cost 0.138 1.64% 2% 0.15

Auto Drive Cost ($/km) Cost 0.138 1.64% 2% 0.15

Auto value of time ($/hr) VoT 30 1.64% 45.05

Unit Road Toll ($/km) Cost 0.11 1% 0.14

Toronto base fare ($) Cost 1.83 1.64% 1% 1.56

GO Transit base fare ($) Cost 3.55 1.64% 1% 3.03

York base fare ($) Cost 2.28 1.64% 1% 1.95

Peel base fare ($) Cost 1.95 1.64% 1% 1.67

Halton base fare ($) Cost 2.15 1.64% 1% 1.84

Hamilton base fare ($) Cost 1.41 1.64% 1% 1.20

Durham base fare ($) Cost 2.33 1.64% 1% 1.99

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5 AM 2031 LRT HOT Model development

Overview

5.1 The 2031 LRT HOT model was based on the 2031 BAU HOT model, with changes made to the auto network as per the latest set of design drawings. The LRT specification was based on the Preliminary System Operations Plan (PSOP).

LRT Alignment

5.2 The latest available information on the LRT alignment was based on the most recent post-DW2.0 set of design drawings. This provides information on the number of traffic lanes along the corridor, and the permitted turns at each intersection along the route. The drawings also provide information on the stop locations along the LRT route.

LRT Operations

5.3 The primary information required to model the operation of the LRT was the service pattern and runtime information, and this was taken from the Preliminary System Operations Plan (PSOP).

LRT Service Pattern and Runtime 5.4 The preferred option – option 4 was modelled and is illustrated in the diagram below. This includes services that run from Brampton to Square One and return, and from Port Credit to Square One and return. This does require an interchange for trips travelling from the north of Square One to the south of Square one and the reverse.

5.5 The run times used in the modelling were as per Medium-High priority (44.9mins Brampton – Port Credit).

FIGURE 5.1 ILLUSTRATION OF THE OPTION 4 SERVICE PATTERN FROM THE PSOP

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Capturing the Impact of LRT ‘Quality and Reliability’ within the Modelling 5.6 It is well established that LRT systems can attract more ridership than bus services with a similar travel time, based on their perceived ‘quality and reliability’. Often an ‘in-vehicle time’ (IVT) factor is used to represent this impact and it reduces the perceived runtime.

5.7 For previous work we have undertaken with Metrolinx on Hamilton, we used an IVT factor of 0.72, which we used for our initial tests. Since then project specific factors have been developed, based on detailed analysis of the reliability of the comparable bus services – the MiWay 19 and 103. Further detail on the work undertaken to derive the revised IVT factor can be found in the ‘Modelling Reliability and Quality Technical note’ which can be found in Appendix B.

5.8 In summary, analysis of the bus travel times along the corridor indicates a significant variation in and uncertainty around the observed journey time.

FIGURE 5.2 TRAVEL TIME ILLUSTRATION FOR MIWAY SERVICES 19 AND 103

5.9 It would be expected that when a segregated LRT was introduced that the journey time uncertainty would be reduced significantly. An estimate for the LRT was derived using a factor of 30% of the value of the bus variability. The variability forecast in the two modes was then used to derive an IVT equivalence as set out in the table below.

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TABLE 5.1 DERIVATION OF IVT EQUIVALENCE

Measure Value

Route 19 and 103 average in-vehicle time variability (% of in-vehicle time) 6.9%

LRT in-vehicle time variability (% of in-vehicle time) 2.1%

Change in in-vehicle time variability with LRT (% of in-vehicle time) -4.8%

Unreliability weighting factor (generalised time / actual time) 2.5

Perceived in-vehicle time variability (% of in-vehicle time) -12%

IVT equivalence 0.88

FIGURE 5.3 FIGURE ILUSTRATING HEADWAY RELIABILITY IN THE CORRIDOR

TABLE 5.2 DERIVATION OF HEADWAY RELIABILITY FACTOR

Measure Value

Perceived BAU headway variability (generalised minutes) 2.4

Perceived LRT headway variability (generalised minutes per passenger) 1.9

Perceived headway variability benefit of LRT over BAU (generalised minutes per passenger) 0.5

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5.10 In addition to IVT and headway reliability, there is also the quality component to be considered. There are intrinsic and intangible benefits perceived by passengers between LRT and conventional bus. These benefits are categorized as "quality benefits" and reflect a key component of mode choice. Quality benefits arise from parameters associated with modelling "quality" aspects of the transit system, namely those related to the ambience, ride quality or security of transit stops or vehicles.

5.11 An international literature review was undertaken and the proposed values adjusted for local conditions. Journeys in the Hurontario corridor are forecast to be shorter than those considered in the literature at around 10 minutes rather than 15 minutes, and the difference in quality between the buses currently operating along Hurontario and the proposed LRT vehicles was deemed to be less than in the case studies cited.

5.12 Combining all of the above factors, an IVT weighting of 0.85 was derived as below.

TABLE 5.3 SUMMARY OF IVT CALCULATION

In-Vehicle Average Boarding Proportion Component Weighting Benefit Per Penalty of Total Benefit Passenger*

In vehicle Time Reliability 0.88 1.2 30%

Headway Reliability 0.5 0.5 13%

Quality 0.97 2.0 2.3 57%

Total 0.85 2.5 4.0 100%

Bus Integration with LRT Operation

5.13 Working in conjunction with the team that developed the Ultimate Transit Network Plan (UTNP), a number of changes were made to the existing transit routes in order to better integrate with the LRT. This included the removal of bus routes that are currently operating along the LRT alignment, and also diversions to better connect existing local transit with the LRT service.

TABLE 5.4 BUS NETWORK CHANGES TO INTEGRATE WITH LRT

Route Proposed Change Notes

MiWay 19/19A/19B Shorten to run Port Credit - Square Frequency reduced – initial assumption of 31 Hurontario One only as a single route 19. buses per hour, to be reviewed after initial Replace 19A/19B branches with new modelling. local distributor route feeding LRT at Possibly integrate with a revised 25 Traders Britannia stop. Loop - details to be finalized

1 Frequency suggested by MiWay transit planners

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Route Proposed Change Notes

MiWay 103 Remove. Replaced by LRT. Hurontario Express

Brampton 2 Shorten to run between Brampton Local service south of Brampton replaced by Main GO and Heart Lake Terminal. extension of 53

Brampton 53 Extend from Shoppers World to Replaces local route 2 in South Brampton with Oaklea Brampton GO via Bartley Bull new ‘back street’ service. Parkway, Peel Village Parkway, Extension could be provided by either 53 or 54 Hurontario Street, Elgin Drive, Mill (or both) according to detailed scheduling. Street, Harold Street, Hurontario As an alternative, section between Shoppers Street, Clarence Street, Centre Street and Queen Street. World and Brampton could be considered as a stand-alone route.

Brampton 502 Shorten to run between Brampton Northern terminus as in BAU. Züm Main GO, Sandalwood Parkway and a new northern terminus (to be defined).

MiWay 3 Divert via Central Parkway instead of Route 8 to remain on Elm Drive. Bloor Elm Drive to provide for transfers at Central Parkway LRT stop.

MiWay 4 Divert via Queensway, Hurontario Camilla Road (north of Queensway) and Paisley Sherway Gardens Street and Paisley Boulevard West to Boulevard East would not be served by route 4 - provide for transfers at Queensway low density residential area. If this is LRT stop. unacceptable, a diversion of the 28 or 38 could be considered as a replacement.

MiWay 25 Divert via Hurontario/Matheson Replaces branch sections of routes 19A/B Traders Loop Hurontario, Milverton, Avebury, Matheson, Falbourne, Cantay, Britannia, Whittle, Brunel, Kennedy, Watline, McAdam, Matheson, terminating back at Hurontario/Matheson

MiWay 28 Divert via Paisley Boulevard West, Very little effect on accessibility as bypassed Confederation Hurontario Street and Queensway in stops are all close to others. both directions to provide for transfers at Queensway LRT stop.

MiWay 38 Divert via Queensway, Hurontario As for route 28. Creditview Street and Paisley Boulevard West northbound (already runs this way southbound) to provide for transfers at Queensway LRT stop.

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Changes to the Auto Network

5.14 It is important to be aware that the HOT model is not designed to model detailed intersection movements, and so the model is limited in how much of the detailed design is reflected within the auto coding in the model.

5.15 Wherever traffic lanes are removed as part of the design, they are removed in the EMME model and, where appropriate, the capacity reduced to reflect reduced green time at intersections once the LRT has been introduced. Along the HMLRT corridor, the key change has been a lane capacity reduction of 900 to 700 vphpl.

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FIGURE 5.4 EMME LANES DIFFERENCE PLOT FOR BAU & LRT AUTO NETWORKS

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FIGURE 5.5 EMME CAPACITY DIFFERENCE PLOT FOR BAU & LRT AUTO NETWORK

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6 PM HOT Model Development

Overview

6.1 The HOT model represents the AM Peak only. However, following discussions with the client, the PM peak was deemed to be the most critical time period for traffic assessment, as it has higher overall traffic volumes than the AM peak. In order to ensure a robust assessment of the traffic impacts of the LRT, a proposal to develop a PM auto assignment only model was put forward to the client as a way to provide more robust inputs to the VISSIM modelling of the PM peak. This approach to developing a PM model is set out below.

6.2 The PM networks were based on the AM networks and the only changes implemented were to include zone centroids in the reverse direction to the AM in the instances that they had been coded as single direction connectors. This was principally an issue with the Park and Ride zone connectors.

6.3 The PM demand matrix was approximated from the AM matrix by a 3-step process:

I Transpose the AM matrix to reflect the broadly, reverse trip patterns typical of the PM peak; I Factor this PM matrix to reflect the generally higher level of demand in the PM peak (PM uplift); and I Apply a separate process for the Square One shopping centre zones to reflect their relative lack of demand in the AM peak matrix.

. factor the 2031 BAU trip ends for the zone containing Square One to match the observed PM peak traffic count data; . calculate a growth factor based on the modelled increase between 2006 Base model and 2031 BAU model for the Square One zone and apply this factor to the traffic count to reflect 2031 demand.

6.4 The 2031 PM uplift factor was derived by reviewing the uplift present in the VISSIM Base 2011 models (16%) and considering how this may change into the future. It is considered that the networks would be more congested into the future and that there would not be the network capacity for the PM to have the same relative difference in demand than the AM as is currently present.

6.5 A method was therefore derived (set out in the ‘PM Uplift note’ in Appendix C) that considered the narrowing gap between AM and PM demand. Using an exponential delay function an uplift factor of 6% for 2031 was derived (compared to 16% used in 2006). Results produced using this method were passed for inclusion in VISSIM.

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7 HOT Model Forecasts for the core scenario

Overview

7.1 As part of the HOT modelling process, there were several iterations of modelling and design to ensure an integrated approach. This included some iteration of review and analysis between the HOT and VISSIM modelling to inform the capacities used within the EMME modelling. VISSIM analysis and testing was also used to inform the physical design.

7.2 As well as design iterations, tests were also undertaken to understand the impacts of using in-vehicle time (IVT) factors, changes to economic parameters and parking costs, as well as changes to complimentary bus services. This Chapter sets out the final modelling work after such investigative modelling has been completed and incorporated into the overall modelling process.

Core Assumptions

7.3 The HOT model covers the 3 hour morning peak period from 06:00 – 09:00, with the transit assignment being for the 3 hour peak, and the highway assignment being for a 1- hour peak. The global factor applied to convert the (3hr) peak period to a (1hr) peak hour for transit is 0.45, and for highways is 0.43.

7.4 It should be noted that for consistency, all results are shown as (1hr) peak hour, unless otherwise stated.

Annualisation Factor 7.5 The annualisation factor used has a material bearing on the forecast annual ridership. The factor has been derived from analysis of the current profile of demand on the bus network, notably the core routes that operate on the HMLRT corridor (MiWay 19/103 and Brampton Transit 2/502). The memo containing the full details of the analysis undertaken can be found in Appendix D, but is summarised briefly below.

7.6 Detailed boardings data for bus routes that currently run on the Hurontario corridor, as well as data for the surrounding network was analysed across the day and a peak to daily factor calculated separately for the corridor and remaining network.

7.7 A hybrid of the corridor and network factors was then used as it was considered that as the corridor develops and the importance of the Square One shopping centre becomes less dominant the corridor factor could decline and become closer to the network average.

7.8 The approach adopted assumes the current demand has the higher factor, reflecting established demand and trip patterns, but that the demand increment arising from land use and network development will tend to reflect the network wide factor.

7.9 Using the HOT model forecasts for 2006 and 2031 BAU, as follows:

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I AM peak base demand on corridor 9,613 at 1,574 I BAU demand on corridor 19,599 and hence increment of 9,986 at 1,279 I Weighted annualisation factor 1,424

Preliminary Results

7.10 Prior to arriving at the core scenario results, a significant amount of work was undertaken to develop and refine the process. These iterations, which involved the transfer of information between the VISSIM and design work streams, informed the refinement of the design and operation of both the transit and auto networks.

7.11 Also during this period refinements were also ongoing on the HOT model itself including minor changes to the coding of the auto and transit networks, parking and economic parameters.

7.12 The table below provides an overview of how the results have evolved over the course of the project, before setting out in more detail the final core scenario outputs on the pages which follow.

TABLE 7.1 TABLE SHOWING EVOLUTION OF MODELLING RESULTS

Date Details AM LRT Annualisation Annual LRT Boardings Factor Ridership

10th December Initial forecasts 38,300 1,586 60.7M

11th January IVT reset to 1 from 0.72 27,900 1,365 38.0M Corridor parking charges removed Revised Annualisation

17th January Minor updates to auto and transit network 22,600 1,365 30.8M assumptions Refinement of Centroid connectors Changes to economic assumptions Refined highway capacity assumptions with LRT

25th March Highway network refinements arising from 26,100 1,418 37.0M VISSIM interface IVT revised to 0.85 Revised Annualisation

21st May Revised Downtown Mississauga 25,500 1,418 36.2M alignment/stops Revised BAU bus speeds and frequencies

23rd Sept Implementation of a $5 Downtown 24,900 1,424 35.5M Brampton/Mississauga parking charge

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Date Details AM LRT Annualisation Annual LRT Boardings Factor Ridership Revised LRT and traffic lanes in Brampton Heritage area and Downtown Mississauga

AM Base / Business As Usual Modelling results

7.13 To understand the progression of the demand from 2006 to 2031, forecast demand has been compiled for MiWay and Brampton Transit. Boardings refer to individual legs of a trip, while the demand refers to whole trips. The table shows that both MiWay and Brampton transit are anticipated to see a material increase in demand from 2006 to 2031. For both transit operators, the 2006 modelled boardings are materially higher than observed, but demand is lower than current boardings. Overall, it is considered that the HOT model is obtaining the correct level of transit demand, but overestimating interchange between routes. This is often prevalent in strategic models such as the HOT model, where the granularity of the model does not reflect the more detailed routing options available in practice, forcing additional interchange to complete transit trips.

TABLE 7.2 AM PEAK (6-9AM) TRANSIT BOARDINGS (DEMAND)

MiWay Brampton Transit

2006 59,000 (30,600) 39,700 (21,400)

2031 100,300 (67,000) 69,000 (37,800)

% change 70% (119%) 74% (77%)

2010/11 observed boardings 35,700 26,900

7.14 Of specific interest is the demand in the Hurontario corridor, which shows a particularly high increase between 2006 and 2031 in the Brampton Transit area. It should be noted there are significant new development areas in Brampton, which are being serviced by new transit routes in those areas. It should also be considered that looking at demand on individual routes in a regional model is not ideal and that it is considered that the corridor totals are more reflective of the overall demand pattern.

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TABLE 7.3 AM PEAK (6-9AM) HURONTARIO ROUTES TRANSIT BOARDINGS

MiWay Brampton Transit Total (19/103) (2/502)

2006 7,300 2,300 9,600

2031 11,300 8,300 19,600

% change 55% 261% 104%

2006 observed boardings 5,000

2010/11 observed boardings 4,700 2,300 7,000

7.15 Figure 7.1 and Figure 7.2 below show the northbound and south bound transit demand profile along the route, with the largest increase in demand being seen in the southbound demand at the Brampton end, being driven by the planning data changes discussed above. Increases in demand in the northbound direction are more uniform along the length of the route.

7.16 Figure 7.3 and Figure 7.4 showing the auto demand along the corridor for 2006 and 2031 show that a similar pattern of demand is retained between 2006 and 2031. It is noticeable that the increase in demand is slight, but this is due to capacity improvements made to parallel routes absorbing the increase in N-S demand (this is set out in Chapter 4).

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FIGURE 7.1 AM BAU TRANSIT FLOW ON HURONTARIO (SOUTHBOUND)

FIGURE 7.2 AM BAU TRANSIT FLOW ON HURONTARIO (NORTHBOUND)

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FIGURE 7.3 AM BAU AUTO FLOW ON HURONTARIO (SOUTHBOUND)

FIGURE 7.4 AM BAU AUTO FLOW ON HURONTARIO (NORTHBOUND)

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AM LRT Modelling results

Assumptions 7.17 The LRT coding in the model is based on DW#3 and the runtimes are based on a medium – high priority with an end to end runtime of 44.9 minutes between Brampton and Port Credit. An in-vehicle time (IVT) factor of 0.85 was applied, derived as set out in chapter 5.

7.18 The option 4 operating pattern from the Preliminary System Operations Plan (PSOP) report has been assumed:

I Brampton to Square One and return I Port Credit to Square One and return This necessitates interchange if travelling beyond Downtown Mississauga in either direction.

7.19 The bus network has been modified based on the Ultimate Transit Network Plan (UTNP), which primarily involves the removal of the Hurontario bus routes 19/103 and the 2/502 south of Brampton downtown.

7.20 The tables below show that transit demand within the corridor is increased with the introduction of the LRT, though a combination of mode shift from auto and transfer from other adjacent transit routes. The service pattern proposed requires an interchange for trips that travel beyond Downtown Mississauga – the table below shows around 1,300 trips make this interchange in the AM peak period.

TABLE 7.4 AM PEAK TRANSIT BOARDINGS ON HURONTARIO ROUTES

BAU LRT with BAU Demand LRT Routes (19/103/2/502) (19/2/502/LRT) (19/2/502/LRT)

Peak period (6am – 9am) 19,600 23,900 (+22%) 27,800 (+16%)

Peak Hour (45%) 8,800 10,800 12,500

TABLE 7.5 AM PEAK PERIOD LRT DEMAND AND BOARDINGS

Demand Boardings Brampton GO – Mississauga Downtown 11,100 12,400

Port Credit - Mississauga Downtown 11,200 12,500

Both lines 1,300

Total 23,600 24,900

Weekday (4.78) 112,800 119,000

Annual (1,424) 33.6M 35.5M

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7.21 The figures below show the loading profile, both by the service pattern and then by direction to give the overall picture for the LRT demand, which is forecast to remain within the planning capacity of the system. They also highlight the importance of Downtown Mississauga as a trip destination

7.22 Also, where LRT trips starts from and ends at are depicted in LRT trip origin and destination maps below. This re-confirms downtown Mississauga as a major trip destination and the high volume of GO-rail transfers in Cooksville and Port Credit.

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FIGURE 7.5 BRAMPTON GO – DOWNTOWN MISSISSAUGA – BRAMPTON GO LOAD PROFILE

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FIGURE 7.6 PORT CREDIT – DOWNTOWN MISSISSAUGA – PORT CREDIT LOAD PROFILE

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FIGURE 7.7 AM PEAK HOUR SOUTHBOUND LOAD PROFILE

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FIGURE 7.8 AM PEAK HOUR NORTHBOUND LOAD PROFILE

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FIGURE 7.9 AM PEAK LRT TRIP ORIGINS

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FIGURE 7.10 AM PEAK LRT TRIP DESTNATIONS

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7.23 The figure below shows transfers between the Hurontario-Main LRT users and other transit routes at major interchanges in the corridor. It highlights the large volume of transfers to the Mississauga BRT during the AM Peak Period.

FIGURE 7.11 INTERCHANGE TRANSIT VOLUMES OF LRT USERS

7.24 The stop to stop matrices are also shown in the figures below as to depict the interaction and volume between origin and destination stops during AM Peak hour. It is noted that many riders are destined at the Rathburn or Robert Speck as a transfer point to the North and South leg of the line.

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FIGURE 7.12 BRAMPTON GO – DOWNTOWN MISSISSAUGA – STOP TO STOP MATRIX

AM Peak Hour Brampton GO-

Mississauga BramptonQueen GO Nanwood CharolaisGateway Sir Lou Ray LawsonHighway 407Derry CourtneyparkBritannia MathesonBristol Eglinton Robert SpeckMain Duke of YorkRathburn Total Brampton GO 0 20 3 2 157 59 0 0 100 24 91 30 0 48 257 88 22 115 1,016 Queen 5 0 2 2 117 34 1 0 100 24 78 30 0 54 126 91 23 110 797 Nanwood 3 4 0 0 3 0 0 0 10 2 5 2 0 3 10 2 1 7 53 Charolais 32 8 0 0 33 6 0 0 21 3 7 5 0 11 8 7 3 20 165 Gateway Terminal 34 31 2 1 0 40 0 0 58 28 57 21 0 42 82 41 17 79 533 Sir Lou 16 10 0 1 135 0 0 0 28 3 6 6 0 11 14 5 2 19 257 Ray Lawson 20 12 0 0 41 2 0 0 14 3 4 3 0 6 9 3 1 8 127 Highway 407 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Derry 13 8 0 1 31 9 0 0 0 51 16 6 2 18 13 17 4 32 221 Courtneypark 0 0 0 0 2 0 0 0 14 0 11 6 0 11 4 10 4 23 87 Britannia 3 2 0 0 10 2 0 0 10 17 0 2 0 2 9 7 1 8 74 Matheson 1 1 0 0 3 1 0 0 5 18 0 0 0 2 4 8 1 3 47 Bristol 7 5 0 0 15 5 0 0 25 15 23 3 0 26 15 6 7 40 193 Eglinton 15 7 0 0 51 6 0 0 53 38 38 0 15 0 65 106 16 44 454 Robert Speck Parkway 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 38 7 9 56 Main 2 0 0 0 7 0 0 0 9 3 10 1 2 2 0 0 4 88 127 Duke of York Blvd 10 3 0 0 41 1 0 0 29 9 32 2 7 20 0 0 0 204 358 Rathburn 80 11 0 0 241 12 0 0 200 126 287 4 43 15 0 0 0 0 1,020 TOTAL 242 122 8 6 887 179 2 0 677 365 666 123 69 270 616 430 113 811 5,586

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FIGURE 7.13 PORT CREDIT – DOWNTOWN MISSISSAUGA – STOP TO STOP MATRIX

AM Peak Hour Port Credit-Mississauga Port Credit GOMineola North ServiceQueenswayDundas CooksvilleCentral GO PkwyMatthews GateRobert SpeckRathburn RoadDuke of YorkMain Total Port Credit GO 0 2 12 124 99 15 9 58 99 216 30 13 677 Mineola 95 0 1 6 37 5 1 1 4 36 2 0 189 North Service 60 18 0 8 27 7 1 1 3 41 3 0 167 Queensway 268 33 3 0 79 47 4 4 16 104 9 2 568 Dundas 169 18 8 66 0 68 16 24 62 222 10 7 670 Cooksville GO 142 13 8 124 139 0 57 117 214 188 29 51 1,082 Central Pkwy 163 1 3 38 123 162 0 2 19 87 22 1 622 Matthews Gate 78 5 1 14 59 73 4 9 16 163 7 2 428 Robert Speck Parkway 14 1 0 2 7 14 0 0 0 12 7 4 61 Rathburn Road 66 4 5 65 74 11 6 1 0 0 31 50 315 Duke of York Blvd 63 0 2 26 71 51 20 15 6 0 0 13 267 Main 167 2 1 52 196 81 43 35 3 0 0 0 580 Total 1,285 97 44 524 912 534 160 267 441 1,069 150 143 5,627

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EMME Model Report

7.25 Table 7.6 on the page that follows sets out how the journey times change between the BAU and LRT for a number of representative journeys on the HMLRT corridor. It can be seen that the in-vehicle time (IVT) for the LRT scenario is perceived as much lower than in the BAU scenario, but that in a number of cases this is partially offset by increases in walk and wait times.

7.26 The LRT has a higher stop spacing compared to the bus service, potentially requiring passengers to walk slightly further to access the LRT service, depending on the exact origin point. Local bus services have also been altered and removed following the introduction of LRT, potentially increasing the wait time at a stop before a service arrives, given the differences in frequency.

7.27 Overall however, it can be seen that in the majority of cases, the perceived journey time between these origins and destinations has improved overall between the BAU and LRT scenarios.

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TABLE 7.6 EXAMPLE TRAVEL TIMES INCLUDING GENERALIZED TRAVEL TIMES

Travel Time (minutes) Journey Element of Journey BAU LRT Difference

Walking 10.7 12.9 Waiting 1.8 2.5 In-vehicle 22.1 15.4 Total Actual Time 34.6 30.8 3.8 Mineola - Square One Weighted Walking & Waiting time 12.5 15.4 Boarding penalty 2.5 0 IVT value 0 -2.3 Total Perceived Time 49.6 43.9 5.7 Walking 8.6 8.6 Waiting 1.8 2.5 In-vehicle 13.0 8.4 Total Actual Time 23.4 19.5 3.9 Dundas - Port Credit GO Weighted Walking & Waiting time 10.4 11.1

Boarding penalty 2.5 0

IVT value 0 -1.3 Total Perceived Time 36.3 29.3 7.0 Walking 11.1 13.4 Waiting 1.8 2.5 In-vehicle 9.9 7.5 Cooksville GO – Total Actual Time 22.8 23.4 -0.6 Square One Weighted Walking & Waiting time 12.9 15.9

Boarding penalty 2.5 0

IVT value 0 -1.1 Total Perceived Time 38.2 38.2 0 Walking 6.8 7.7 Waiting 3.3 2.5 In-vehicle 11.2 7.9 Steeles – Downtown Total Actual Time 21.3 18.1 3.2 Brampton Weighted Walking & Waiting time 10.1 10.2

Boarding penalty 2.5 0

IVT value 0 -1 Total Perceived Time 33.9 27.3 6.6

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Benchmarking

7.28 To ensure robust and credible ridership forecasts have been obtained, a benchmarking exercise has been undertaken to compare the HMLRT ridership with ridership on around 30 existing systems in North America and Europe.

7.29 HMLRT is forecast to have around 118,000 passengers per average weekday, and this ranks in the top third when compared to the other LRT systems (Figure 7.11).

FIGURE 7.14 AVERAGE WEEKDAY RIDERSHIP

7.30 This metric does not obviously take account of the length of system being assessed and these vary significantly in the systems set out, from 7km for the Toronto St Clair line to over 120km for the Dallas Dart line.

7.31 Considering the boardings per kilometre in Figure 7.12, it can be seen that Hurontario is in the top third, with very similar numbers to the Boston Green Line and Montpelier Tramway. However, these two systems have much higher daily ridership reflecting the fact that these are much longer systems. Boston weekday ridership averages 236,000 and Montpelier Tramway has an average weekday ridership of 180,000.

7.32 Comparing population to average weekday ridership shows that forecasted ridership (i.e., service population) is quite modest compared to the population of Brampton and Mississauga. (Figure 7.13).

7.33 While in the top third of systems for both the Average Weekday Ridership and Daily Boardings per kilometer, they sit within the established range for similar systems, and validate that the forecasts produced are reasonable.

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FIGURE 7.15 BOARDINGS PER KM (DAILY)

FIGURE 7.16 POPULATION TO AVERAGE WEEKDAY RIDERSHIP

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Impact of LRT on Highway Flows

7.34 With the introduction of the LRT, auto capacity along Hurontario is reduced and it would be expected that there would be a reduction in traffic levels on Hurontario- Main, as well as wider traffic diversion onto parallel and/or competing routes.

7.35 The table below shows the impact on Hurontario – Main traffic levels, which reduces following introduction of the LRT. The change in flows have been employed in the VISSIM analysis to understand the impact on individual intersection movements and queuing. It can be seen that the pattern of demand does not change significantly between BAU and LRT scenarios, the main impact being a general reduction in auto flow levels along the corridor.

TABLE 7.7 MAXIMUM PEAK HOUR AUTO FLOW

Southbound Northbound

BAU LRT BAU LRT

Church St – Brampton GO 700 500 400 200

Brampton GO – Nanwood 800 600 700 600

Nanwood - Steeles 1,500 1,000 700 700

Steeles – 407 2,300 1,600 1,200 1,000

407 – 401 2,600 1,900 1,800 1,400

401 – 403 2,400 1,800 2,700 2,100

403 – Burnhamthorpe 2,400 2,000 2,400 1,900

Burnhamthorpe – Dundas 1,600 1,200 2,000 1,200

Dundas – QEW 1,600 1,000 2,400 1,700

QEW - Lakeshore 1,700 1,200 1,300 1,200

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FIGURE 7.17 AM PEAK HOUR AUTO FLOW ON HURONTARIO (SOUTHBOUND)

FIGURE 7.18 AM PEAK HOUR AUTO FLOW ON HURONTARIO (NORTHBOUND)

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7.36 Within the wider network, the changes are generally marginal (under 100vph), although there are locations where flow changes exceed this level:

I Edwards Blvd southbound at Derry Rd: +200 I Kennedy Rd southbound at Derry Rd: +100 I McLaughlin Rd southbound at Derry Rd: +120 I Mavis Rd northbound at Derry Rd: +100 I Courtney Park eastbound at Mavis: +100 I Mavis Rd northbound around Hwy401: +160 I McLaughlin Rd northbound at Bristol Rd: +100 I Central Parkway eastbound at Bloor St: +110 I Confederation Parkway northbound from Queensway to Dundas: +190 I Camilla Road northbound from North Service Rd to Queensway: +100

7.37 Information on changes to flows in the wider network has been employed in SYNCHRO to determine the specific impacts to turning movements and queuing.

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8 HOT model outputs for use in VISSIM and BCA work

Overview

8.1 The HOT model provides information for downstream work on both the detailed traffic and design assessment undertaken in VISSIM, and to provide data for input into the Benefits Case Analysis (BCA) appraisal process. An overview of the data used in each of the workstreams is set out below.

Interaction with VISSIM

8.2 A standalone VISSIM model based on observed data was generated for a 2011 base year, and data from the HOT model is used to produce forecasts for the 2031 BAU and LRT scenarios. Link flow data is extracted from the HOT for the 2006 and 2031 models and the incremental change in auto flow between these two models is applied to the VISSIM base model. A correction is made to reflect the fact that the VISSIM has a 2011 base year, while the HOT model base year is 2006.

8.3 Further information on how the data extracted from the HOT model is used in VISSIM can be found in the ‘Future Scenarios VISSIM Model Report’.

FIGURE 8.1 SUMMARY OF PROCESS TO GENERATE 2031 VISSIM MATRICES

66 EMME Model Report

Benefits Case Appraisal

8.4 The HOT model provides a number of key values to inform the BCA and these are listed below and are provided for both the BAU model and the LRT scenarios. These feed into the appraisal model to produce the information that is reported within the Business Case.

TABLE 8.1 EMME MODEL OUTPUTS USED IN THE BCA

Description Unit

Transit demand trips

Transit travel time User min

Highway demand trips

Highway travel time User min

Highway distance Savings Veh km

Non-motorized trips

Revenue from Transit fares $

Revenue from GO Rail fares $

8.5 The information in the table above is used to calculate total transit and auto time savings, as well as the auto distance savings.

8.6 Further details on how this data feeds through to a final Benefit : Cost Ratio (BCR) can be found in the Business Case report.

SYNCHRO Modelling

8.7 Information on changes in traffic flows within the wider network and away from the corridor is also passed from the HOT model to be used in offline SYNCHRO models. These models will be used to identify any intersections issues within the wider network caused by the introduction of the LRT and the rerouting of traffic. This will include reviewing queuing and delay statistics.

67 EMME Model Report

9 Conclusions

9.1 Steer Davies Gleave has utilised the existing HOT model developed by MMM and refined it to produce updated forecasts for the Hurontario LRT project. During the development process, the model outputs, through VISSIM, were used to inform and develop the design for the system. Final model outputs have been used to inform the Benefits Case Analysis.

9.2 This project has considered the detailed highway capacity restraints that will be caused by the introduction of the LRT, which were not considered in such a detailed manner previously.

9.3 Consideration has also been given to the benefits that an LRT can offer through the use of an In vehicle time weighting to reflect the associated quality and reliability attributes that a fixed, segregated LRT system can offer.

9.4 Based on the analysis to date, the table below sets out the forecast ridership on both the northern and southern loops, combining to give an annualised ridership of 35.3M.

TABLE 9.1 AM PEAK PERIOD LRT DEMAND AND BOARDINGS

Demand Boardings

Brampton GO – Mississauga Downtown 11,100 12,400

Port Credit - Mississauga Downtown 11,200 12,500

Both lines 1,300

Total 23,600 24,900

Weekday (4.78) 112,800 119,000

Annual (1,424) 33.6M 35.5M

9.5 Work following on from this will utilise the HOT model to develop a robust business case and set of sensitivity tests to determine the case for the LRT project.

68 EMME Model Report

APPENDIX

A

GO RAIL DATA

Appendix A

EMME Model ReportEMME Model Report

A1 GO RAIL DATA

APPENDIX TABLE A.1 OBSERVED & MODELLED GEORGETOWN LINE GO RAIL FLOWS PRIOR TO MODEL REFINEMENT

Georgetown Line Observed S1 2006 Base (peak period) Model Flows

2006 Rail % drove 2006 auto 2012 % parking Transit Boardings Veh trips Total Car access Transit Boardings A (= veh trips) access (veh) parking capacity access access Transit shortfall access B spaces Access shortfall

Georgetown 646 64.7% 418 614 68% 103 170 104 34 314 69

Mount Pleasant 765 70.3% 538 611 88% 66 0 0 0 538 66

Brampton 1,942 63.2% 1,227 962 128% 346 107 46 47 1,181 300

Bramalea 2,607 73.0% 1,903 1,991 96% 133 59 37 11 1,866 122

Malton 767 74.0% 568 525 108% 29 287 209 16 359 13

Etobicoke North 720 76.7% 552 530 104% 2 1,010 651 164 -99 -162

Weston 383 51.8% 198 110 180% 125 2,176 1,005 869 -807 -744

Bloor 24 0.0% 0 0 0 24 847 0 847 0 -823

TOTAL 7,854 4,656

Appendix A EMME Model ReportEMME Model Report

APPENDIX TABLE A.2 OBSERVED & MODELLED MILTON LINE GO RAIL FLOWS PRIOR TO MODEL REFINEMENT

Milton Line Observed S1 2006 Base (peak period) Model Flows

2006 Rail % drove 2006 auto 2012 % parking Transit Boardings Veh trips Total Car access Transit Boardings A (= veh trips) access (veh) parking capacity access access Transit shortfall access B spaces Access shortfall

Milton 1,237 69.7% 862 1,082 80% 116 62 28 26 834 90

Lisgar Open 2007 57.2% 0 788 0% 0 0 0 0 0 0

Meadowvale 2,058 58.9% 1,212 1,600 76% 482 212 135 36 1,077 446

Streetsville 2,225 68.0% 1,513 1,459 104% 258 224 157 20 1,356 238

Erindale 1,823 53.5% 975 770 127% 555 1,492 1,139 11 -164 544

Cooksville 2,713 54.5% 1,479 1,459 101% 791 4,119 2,641 686 -1,162 105

Dixie 646 92.9% 600 778 77% -134 1,293 987 10 -387 -144

Kipling 134 35.1% 47 0 0 73 742 0 742 47 -670

TOTAL 10,836 8,144

Appendix A EMME Model ReportEMME Model Report

APPENDIX TABLE A.3 OBSERVED & MODELLED LAKESHORE WEST LINE GO RAIL FLOWS PRIOR TO MODEL REFINEMENT

Lakeshore West Observed S1 2006 Base (peak period) Model Flows

2006 Rail % drove 2006 auto 2012 % parking Transit Boardings Veh trips Total Car access Transit Boardings A (= veh trips) access (veh) parking capacity access access Transit shortfall access B spaces Access shortfall

Hamilton 691 13.5% 93 0 0 570 28 0 28 93 542

Aldershot 465 93.3% 434 1,619 27% -99 121 89 5 345 -104

Burlington 2,219 75.2% 1,669 2,273 73% 50 329 189 84 1,480 -34

Appleby 2,376 73.9% 1,756 2,422 72% 93 170 77 70 1,679 23

Bronte 2,044 76.9% 1,572 2,424 65% 1 1,107 764 113 808 -113

Oakville 4,185 64.6% 2,704 2,724 99% 670 7,120 4,677 1,040 -1,973 -369

Clarkson 4,061 69.6% 2,826 2,878 98% 387 7,905 4,416 2,164 -1,589 -1,778

Port Credit 1,842 52.4% 965 922 105% 587 5,058 2,538 1,758 -1,573 -1,171

Long Branch 689 52.5% 362 281 129% 219 1,466 691 567 -330 -348

Mimico 422 44.0% 186 173 107% 181 2,277 1,249 653 -1,063 -473

Exhibition 1 0.0% 0 0 0 1 15 0 15 0 -14

TOTAL 18,995 25,594

Appendix A EMME Model ReportEMME Model Report

APPENDIX TABLE A.4 OBSERVED & REVISED MODELLED GEORGETOWN LINE GO RAIL FLOWS FOLLOWING MODEL REFINEMENT

Georgetown Line Observed Revised 2006 Base (peak period) Model Flows

2006 Rail % drove 2006 auto 2012 % parking Transit Boardings Veh trips Total Car access Transit Boardings A (= veh trips) access (veh) parking capacity access access Transit shortfall access B spaces Access shortfall

Georgetown 646 64.7% 418 614 68% 103 170 104 65 314 38

Mount Pleasant 765 70.3% 538 611 88% 66 0 0 0 538 66

Brampton 1,942 63.2% 1,227 962 128% 346 1,932 1,227 705 0 -359

Bramalea 2,607 73.0% 1,903 1,991 96% 133 2,502 1,903 599 0 -466

Malton 767 74.0% 568 525 108% 29 743 568 175 0 -146

Etobicoke North 720 76.7% 552 530 104% 2 1,010 651 359 -99 -357

Weston 383 51.8% 198 110 180% 125 2,176 1,005 1,171 -807 -1,046

Bloor 24 0.0% 0 0 0 24 847 0 847 0 -823

TOTAL 7,854 9,380

Appendix A EMME Model ReportEMME Model Report

APPENDIX TABLE A.5 OBSERVED & REVISED MODELLED MILTON LINE GO RAIL FLOWS FOLLOWING MODEL REFINEMENT

Milton Line Observed Revised 2006 Base (peak period) Model Flows

2006 Rail % drove 2006 auto 2012 % parking Transit Boardings Veh trips Total Car access Transit Boardings A (= veh trips) access (veh) parking capacity access access Transit shortfall access B spaces Access shortfall

Milton 1,237 69.7% 862 1,082 80% 116 1,259 862 397 0 -281

Lisgar Open 2007 57.2% 0 788 0% 0 0 0 0 0 0

Meadowvale 2,058 58.9% 1,212 1,600 76% 482 2,157 1,212 945 0 -463

Streetsville 2,225 68.0% 1,513 1,459 104% 258 2,293 1,513 780 0 -522

Erindale 1,823 53.5% 975 770 127% 555 1,898 975 923 0 -368

Cooksville 2,713 54.5% 1,479 1,459 101% 791 2,685 1,479 1,206 0 -415

Dixie 646 92.9% 600 778 77% -134 643 600 43 0 -177

Kipling 134 35.1% 47 0 0 73 742 0 742 47 -669

TOTAL 10,836 11,677

Appendix A EMME Model ReportEMME Model Report

APPENDIX TABLE A.6 OBSERVED & REVISED MODELLED LAKESHORE WEST LINE GO RAIL FLOWS FOLLOWING MODEL REFINEMENT

Lakeshore West Observed Revised 2006 Base (peak period) Model Flows

2006 Rail % drove 2006 auto 2012 % parking Transit Boardings Veh trips Total Car access Transit Boardings A (= veh trips) access (veh) parking capacity access access Transit shortfall access B spaces Access shortfall

Hamilton 691 13.5% 93 0 0 570 28 0 28 93 542

Aldershot 465 93.3% 434 1,619 27% -99 447 434 13 0 -112

Burlington 2,219 75.2% 1,669 2,273 73% 50 2,120 1,669 451 0 -401

Appleby 2,376 73.9% 1,756 2,422 72% 93 2,350 1,756 594 0 -501

Bronte 2,044 76.9% 1,572 2,424 65% 1 2,041 1,572 469 0 -468

Oakville 4,185 64.6% 2,704 2,724 99% 670 5,090 2,704 2,386 0 -1,716

Clarkson 4,061 69.6% 2,826 2,878 98% 387 4,059 2,827 1,232 -1 -845

Port Credit 1,842 52.4% 965 922 105% 587 1,842 965 877 0 -290

Long Branch 689 52.5% 362 281 129% 219 1,466 691 774 -329 -555

Mimico 422 44.0% 186 173 107% 181 2,302 1,249 1,053 -1,063 -872

Exhibition 1 0.0% 0 0 0 1 15 0 15 0 -14

TOTAL 18,995 21,760

Appendix A EMME Model Report

APPENDIX

B

MODELLING RELIABILITY AND QUALITY TECHNICAL NOTE

Appendix B

SNC-Lavalin Transportation Team 195 The West Mall Toronto, Ontario Canada M9C 5K1

Telephone: (416) 252-5311 Fax: (416) 231-5356

HMLRT Technical File Note

Date: 25th January 2013

Subject: HMLRT - Modelling Reliability and Quality

Background and Purpose

Through 2012 work has been progressing to develop the detail of the design for the HMLRT corridor, linking Port Credit, Downtown Mississauga and Brampton.

This note summarises the analysis undertaken to derive appropriate modelling parameters to capture reliability and quality benefits perceived by LRT passengers compared to bus passengers on the HMLRT corridor. These are real benefits that accrue to LRT passengers and explicit inclusion of them in the modelling process provides a direct way to capture the demand and benefit impacts.

Structure of this Note

This note is structured as follows:

 Introduction;  LRT Reliability Benefits;  LRT Quality Benefits; and  Modelling Reliability and Quality for LRT.

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1. LRT Reliability Benefits

Introduction 1.1 The implementation of LRT will typically improve both journey time reliability and headway reliability. A greater level of journey time reliability is driven by the following characteristics:

 Segregated running - reducing conflicts with general traffic, particularly during congested periods and when lane changes are required at bus stops;  Priority at intersections - where LRT has the potential to influence green phases;  Low floor/step free – where mobility impaired passengers can board/alight LRT more effectively;  Multiple doors – where LRT can handle large volumes of boardings and alightings quicker than conventional bus during peak periods; and  Off-vehicle or cashless ticketing – reducing the likelihood of delayed boarding times.

1.2 While journey time benefits are captured within conventional modelling and appraisal (which are based on ‘average’ journey times) reliability benefits are associated with the reduction in day-to- day journey time variability for similar times of travel. 1.3 Journey time variability is particularly important for transit riders who need to arrive at a given time (e.g. to get to work, to make on onward transport connection) and in these cases people often need to ‘factor in’ additional time to ensure they compensate for unreliability. 1.4 Traditionally, the impacts of transit unreliability have not been explicitly accounted for in transport models and the benefits from improved reliability did not have a formal role in the evaluation of transit projects. However, the fact that travellers do respond to the level of reliability (and the existence of economic benefits or costs associated with this response) have recently been acknowledged by transportation planning professionals and economists. 1.5 There has been significant research into reliability and in the UK this research has been used to develop an approach to value and monetize reliability benefits that forms part of UK Government Transport Appraisal Guidance. The approach used is to estimate the ‘average lateness’ based on the standard deviation of arrival times (compared to the timetable or schedule), and to value this ‘unreliability’ by a higher perception factor, based on research. In the GTHA, these benefits have been assessed by Steer Davies Gleave in the benefits case analysis (to varying degrees of detail) for Hamilton LRT and Eglinton Rapid Transit projects. 1.6 Given the availability of data, two key components of journey reliability will be measured:

 In-vehicle time variability – passengers experiencing unpredictable journey times; and  Headway variability – passengers experiencing unpredictable wait times.

1.7 The City of Mississauga provided the Study Team with extensive journey time data collected for bus routes 19 and 103. The dataset for Route 19 covers weekdays and Saturdays from April 16th to May 11th 2012, while the dataset for Route 103 covers weekdays and Saturdays from September 4th to October 20th 2012.

Page 2

In-Vehicle Time Variability

Route 19 In-Vehicle Time Variability 1.8 Analysis of journey times for the Route 19 shows that the average end-to-end travel time is 55 minutes for weekdays and 49 minutes for Saturdays. Figure 5 and Figure 6 shows the average journey time and standard deviation during weekdays and Saturdays for the Northbound and Southbound services respectively. 1.9 On weekdays the highest average travel time period is at 4PM (68 min Northbound and 66 min Southbound). The average hourly standard deviation, a measure of the variability of travel time, is estimated at 3.9 minutes for the weekdays and similar for Saturdays. Table 2 summarizes the weekday journey time reliability statistics. It should be noted that the statistics represent the average values over all the hourly time periods, as this is a more representative way of measuring day-to-day variability for people travelling at the same time of day. Table 1 – Route 19 Weekday Journey Time Reliability Statistics

Measure Northbound Southbound Average Average journey time (mins) 55 54 55 Journey time standard deviation (mins) 3.2 2.9 3.1 Percentage s.d. of average journey time 5.8% 5.4% 5.6%

Route 103 In-Vehicle Time Variability 1.10 Analysis of journey times for the Route 103 express shows that the average end to end travel time is 47 minutes for weekdays and 43 minutes for Saturdays. Figure 5 and Figure 6 shows the average journey time and standard deviation during weekdays and Saturdays for the Northbound and Southbound services respectively. 1.11 On weekdays the highest average travel time period is at 4PM (57 min Northbound and 55 min Southbound). The average hourly standard deviation, a measure of the variability of travel time, is estimated at 3.9 minutes for the weekdays and similar for Saturdays. Table 2 summarizes the weekday journey time reliability statistics. Table 2 – Route 103 Weekday Journey Time Reliability Statistics

Measure Northbound Southbound Average Average journey time (mins) 48 46 47 Journey time standard deviation (mins) 4.0 3.8 3.9 Percentage s.d. of average journey time 8.3% 8.3% 8.3%

1.12 Based on the analysis of journey time and variations, the express Route 103 provides shorter journey times than the stopping Route 19. However, the express Route 103 has a journey time variability that is notably higher.

Page 3

Figure 3 Average and standard deviation travel times along Route 19 – Northbound for weekdays and Saturdays

Figure 4 Average and standard deviation travel times along Route 19 – Southbound for weekdays and Saturdays

Page 4

Figure 5 Average and standard deviation travel times along Route 103 – Northbound for weekdays and Saturdays

Figure 6 Average and standard deviation travel times along Route 103 – Southbound for weekdays and Saturdays

Page 5

1.13 From the relative standard deviation of journey times observed for the routes 19 and 103 and modelled for LRT, an average in-vehicle time standard deviation of the two routes was determined to be 6.9%.

LRT In-Vehicle Time Variability 1.14 As mentioned in the introduction, the proposed LRT would deliver significantly improved in- vehicle time compared to a conventional bus service. However, there will still be some journey time variation caused by factors such as longer boarding times (e.g. for wheelchair users) or intersections blocked by traffic. 1.15 Detailed traffic simulation work undertaken for the Hamilton LRT project concluded that signal priority reduced journey time variability by 70%. Applying this to HMLRT, the standard deviation of LRT journey times would therefore be 2.1% of the journey time (30% of the average in-vehicle standard deviation for routes 19 and 103). (The VISSIM work for HMLRT will enable the 70% assumption to be reviewed and adjusted if material differences emerge for the HMLRT project.)

In-Vehicle Time Variability Benefits with LRT 1.16 Having considered the in-vehicle time variability of LRT and buses along the corridor, the net reduction in the actual in-vehicle time standard deviation for LRT users is estimated at 4.8% of the in-vehicle time. 1.17 Experience from the UK1 suggests that passengers perceive journey time unreliability as an added inconvenience and it is common to weight unreliability by a factor between 2 and 4 to reflect this. For the purpose of estimating reliability benefits we have conservatively assumed a weighting factor of 2.5, comparable to the weighting applied to the time passengers wait at transit stops. Note that this takes into account the lower perceived inconvenience for early arrival. This means that the perceived in-vehicle time saving for a passenger travelling on the LRT is 4.8%x2.5 = 12% of the LRT in-vehicle time. 1.18 Table summarizes the actual and perceived in-vehicle time variability measures.

Table 7 Actual and Perceived In-Vehicle Time Variability

Measure Value Route 19 in-vehicle time variability (% of in-vehicle time) 5.6% Route 103 in-vehicle time variability (% of in-vehicle time) 8.3% Route 19 and 103 average in-vehicle time variability (% of in-vehicle time) 6.9% LRT in-vehicle time variability (% of in-vehicle time) 2.1% Change in in-vehicle time variability with LRT (% of in-vehicle time) -4.8% Unreliability weighting factor (generalised time / actual time) 2.5 Perceived in-vehicle time variability (% of in-vehicle time) -12%

1 Department for Transport’s Passenger Demand Forecasting Handbook and Transport for London’s Business Case Development Manual Page 6

LRT Headway Benefits

Route 19 Headway Variability 1.19 Analysis of journeys for the Route 19 shows that the average headway is 17 minutes for weekdays. The average headway standard deviation across the day was found to be 5.2 minutes. Table 8 – Route 19 Weekday Headway Reliability Statistics

Measure Northbound Southbound Average Average headway (mins) 17 17 17 Headway standard deviation (mins) 4.8 5.6 5.2 Percentage s.d. of average headway 29% 34% 31%

Route 103 Headway Variability 1.20 Analysis of journey times for the Route 103 at Shoppers World shows that the average headway is 13 minutes for weekdays. The average headway standard deviation across the day was found to be 7.1 minutes. This represents 54% of the average headway, and this percentage is found to be consistent across all time periods. Table 9 – Route 103 Weekday Headway Reliability Statistics at Shoppers World

Measure Northbound Southbound Average Average headyway (mins) 14 13 13 Headway standard deviation (mins) 8.0 6.2 7.1 Percentage s.d. of average headway 59% 49% 54%

1.21 Based on the analysis, there is significant headway variation, consistent with the earlier finding that the express Route 103 has a journey time variability that is notably higher than Route 19. The average headway variability of the corridor based on routes 19 and 103 is therefore estimated at 43% of the average headway.

LRT Headway Variability 1.22 In line with assumptions regarding LRT in-vehicle variability, it has been assumed that LRT could reduce headway variability by 70%. This means that the headway standard deviation for LRT is assumed to be 43%x30% = 13%.

Headway Variability Benefits with LRT 1.23 Having considered the headway variability of LRT and buses along the corridor, the net reduction in the actual in-vehicle time standard deviation for the BAU and with LRT scenarios are calculated individually:

Page 7

 The weekday average headway for the BAU bus service is estimated at 2.3 minutes, so the headway variability for the BAU is 43%x2.3 = 0.98 minutes per user. Applying the perceived time factor of 2.5 to convert actual time to generalised (perceived) time, the perceived headway variability for a passenger travelling on the LRT is 0.98x2.5 = 2.4 generalised minutes per user.

 The weekday average headway for HMLRT is estimated at 6.0 minutes, so the headway variability for LRT is 13%x6.0 = 0.77 minutes per user. Applying the perceived time factor of 2.5, perceived headway variability for a passenger travelling on the LRT is 0.77x2.5 = 1.9 generalised minutes per user.

1.24 The change in perceived headway variability benefit is therefore 2.4-1.9 = 0.5 generalised minutes. The Table summarizes the actual and perceived headway variability measures.

Table 10 Perceived Headway Variability Benefits Calculation

Measure Value BAU Route 19/103 average headway variability (% of headway) 43% BAU Weekday average headway for Route 19/A/B in 2031 (minutes) 2.3 Unreliability weighting factor (perceived time / actual time) 2.5 Perceived BAU headway variability (generalised minutes) 2.4 With LRT LRT headway variability, assumed 30% of bus (% of headway) 13% Average with LRT headway (minutes) 6.0 Unreliability weighting factor (perceived time / actual time) 2.5 Perceived LRT headway variability (generalised minutes per passenger) 1.9

Perceived headway variability benefit of LRT over BRT (generalised 0.5 minutes per passenger)

Page 8

2. Quality Benefits

Introduction 2.1 There are intrinsic and intangible benefits perceived by passengers between LRT and conventional bus. These benefits are categorized as “quality benefits” and reflect a key component of mode choice. Quality benefits arise from parameters associated with modelling “quality” aspects of the transit system, namely those related to the ambience, ride quality or security of transit stops or vehicles. 2.2 Traditionally, “quality” is incorporated as part of a mode constant which is applied in a model as a boarding penalty or a corresponding in-vehicle weighting factor. Journey time reliability is usually considered a component of quality. Since the mode constant or in-vehicle weighting factor is said to represent those attributes for which parameters are not estimated explicitly (such as in-vehicle time, walk time, wait time, fare, transfer), the incorporation of quality benefits in this manner is implicit, not explicit. 2.3 The boarding penalty and/or in-vehicle weighting factor is a key calibration parameter for existing transit networks, a derivation from similar networks or based on stated preference surveys which can be costly to undertake.

Review of Evidence

Leeds New Generation Transit (NGT), UK 2.4 Leeds NGT is an example of how to estimate quality benefits from various attributes, including those associated with the transit stop and those associated with the quality of the transit vehicle. 2.5 Each attribute valuation was estimated individually with the aid of a comprehensive Stated Preference (SP) exercise. The quality attributes estimated were:

 Lighting at bus stop  Shelter at bus stop  Information at bus stop  CCTV at bus stop  Ticketing (e.g. driver, conductor, electronic)  Luggage space on bus  Bus “type”

2.6 Stated preference survey results concluded that a high quality BRT vehicle is ‘worth’ between 2.8 and 5.6 minutes compared to normal bus depending on the journey purpose, while bus stop environment attributes could be worth up to an additional 20 minutes.

Manchester Metrolink, UK 2.7 Quality benefits for Manchester Metrolink LRT were implicitly incorporated in the boarding penalties. That is, parameters for in-vehicle time, walk time, wait time, and fare were estimated explicitly, with all other choice “drivers”, including quality attributes, represented by the mode

Page 9

constant. The average journey on Manchester’s LRT network is around 16 minutes, with end-to- centre journey times around 40 minutes. 2.8 In the Manchester work there were separate constants for:

 Four transit sub-modes (Bus, Metrolink, BRT, Rail). Two car availability segments (car available and no-car available where car availability was a proxy for income segmentation as evidence elsewhere suggested that households with car behave differently in terms of mode choice to those households without one .  Two time periods (AM and IP). The penalties for three of these sub-modes (bus, Metrolink and Rail) were estimated from revealed preference exercises carried out on observed data collected in 2005, 13 years after Metrolink services commenced. The remaining sub-mode (BRT) was not present in the 2005 revealed preference exercise.

2.9 Additional information from a separate stated preference exercise carried out in 1998 comprised mode constants for bus, BRT and Metrolink. Whilst the precise valuations were not transferable, this information was used to determine the BRT mode constant relative to the constants for bus and Metrolink. From this data it was found that the BRT constants lie approximately half-way between those for bus and Metrolink. This relationship was applied to the model bus and Metrolink constants estimated from the 2005 data. The full set of mode constants is set out in Table 2.1.

Table 2.1 Manchester Metrolink Revealed Preference Mode Constants (2005)

Inter-peak AM Peak Car Inter-peak AM Peak No Parameter No Car Available Car Available Car Available Available Bus Boarding Penalty 20 20 6 6 Rail Boarding Penalty 6 2 2 0 LRT Boarding Penalty 5 1 1 0 BRT Boarding Penalty* 12 10 3 3 LRT benefit over Bus 15 19 5 6 *Based on stated preference 2.10 The results show that transit users in Manchester show a revealed preference for LRT over other transit modes as it has the lowest boarding penalty, all other modelled factors (journey time, frequency, fare, interchange) being equal. In particular, the difference is greatest for those with car available. The differences in mode constants can be attributed to ambience, safety or reliability factors. 2.11 LRT would therefore have a benefit of 5 to 19 minutes when compared to conventional bus. These quality benefits are perceived differently by income segments (car ownership) and journey purpose (time of day). For example, those with higher income and have car available may be less inclined to take a bus but willing to take LRT due to the higher quality, while those who have lower incomes do not perceive the quality to the same extent as they have limited alternatives or expectations on their journey ambience. On the other hand, stated preference shows that BRT passengers benefit between 3 and 12 minutes compared to bus. Page 10

2.12 Note that neither the revealed preference for the Manchester LRT system or stated preference for the BRT system reflect the “perfect” quality for the journey experience and may not be directly comparable to the benefits concluded from the Leeds NGT SP study

SkyTrain, Vancouver 2.13 SDG has been involved in modelling and evaluation work undertaken for TransLink on rapid transit projects in Vancouver. Based on the revealed preference calibration of the EMME model, quality is measured by in-vehicle weighting factors, with conventional bus weighted by 1.2 and SkyTrain by 1.0. 2.14 This means that SkyTrain users perceive in-vehicle time by 17% less than bus users, or a 4 minute benefit for a 20 minute journey, all other things being equal. It should be noted that while SkyTrain includes attributes associated with quality transit service, ride quality and safety, the stations are elevated so transit access may not be as convenient as on-street modes such as LRT or BRT.

Literature Review 2.15 We have reviewed the article The Demand Performance of Bus Rapid Transit, Graham Currie, Monash University, Journal of Public Transportation Volume 8 No.1 (2005). This article extensively examined how passengers valued trip attributes for on-street bus, BRT, LRT and heavy rail systems, compiling information from a range of studies and sources. 2.16 The conclusion was that LRT is favoured relative to conventional bus. Based on Currie’s analysis, LRT mode constants could be up to 20 minutes relative to conventional bus, with an average of 9.3 minutes across all sources. This is set out in Table 2.2. It is important to note that these values implicitly include perceived reliability benefits of the system examined.

Table 2.2 Mode Specific Constants by Mode (Perceived Minutes per Passenger)

Source Example LRT vs Bus (Mins) Van Der Waard 1988 Holland 2-3 Kilvington 1991 UK various (Car Available) 15 Kilvington 1991 Dublin (Bus Users) 16 London Railplan Review UK various (Bus Users) 8 Prosser et al 1997 Sydney (AM peak) 4 Wardman Study 13, 1991 1 Study 9, 1989 10 Study 12, 1990 18 Average 9.3 Extract from The Demand Performance of Bus Rapid Transit, Graham Currie, Monash University, Journal of Public Transportation Volume 8 No.1 (2005).

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3. Modelling Reliability and Quality for HMLRT

Representing Reliability and Quality in the HOT Model 3.1 The original Benefits Case Analysis for HMLRT assumed very limited reliability and quality differences between conventional buses and LRT (boarding penalty of 2 minutes for bus was removed for LRT) and this understates the case for LRT. Previous sections showed that there is clear evidence and precedent to include additional benefits associated with the quality of enhanced passenger journey experience. 3.2 The application of reliability and quality parameters in transportation models have been done through a fixed mode constant and/or a factor on in-vehicle time. In reality the most accurate measure would be a mixture of both, with fixed constants reflecting headway variation, stop related attributes (shelter, CCTV, real time information) and variable constants reflecting journey ambience (in-vehicle time variation, ride quality, noise, vibration, climate control).

Reliability and Quality Modelling Parameters 3.3 In the context of HMLRT, the existing buses are not significantly different to the buses compared against LRT in the literature. However, given that North America is notably more auto-dependent and has higher car ownership/car availability compared to the European examples considered in this report, the perceived benefit of quality is unlikely to be in the lower end of the ranges mentioned. 3.4 Section 1 of this technical note considered the journey reliability benefits, while section 2 considered evidence of parameters that reflect components of a journey that is not explicitly modelled – this includes quality and reliability. In addition, research2 has suggested that “packaging” of various individual benefits usually leads to an overall benefit that is worth less than the sum of the parts. 3.5 Based on the literature research, the typical combined reliability and quality benefits for LRT over bus is assumed to be 8 generalized minutes. However, the average journey time for the HMLRT trips is estimated at 10 minutes, and this is likely to be shorter than the typical 15-20 minutes for the researched transit systems. A conservative assumption would be to reduce the average benefit per passenger to 4 minutes. 3.6

2 Department for Transport’s Passenger Demand Forecasting Handbook and Transport for London’s Business Case Development Manual Page 12

3.7 Table 3.1 sets out the proposed modelling parameters that produce an average benefit per passenger of 4 generalised minutes. In-vehicle time reliability is assumed to be a function of in- vehicle time alone; headway reliability is assumed to be a fixed boarding penalty, and quality is reflected through a combination of a variable component through the in-vehicle time (journey comfort) and a fixed component through the boarding penalty (image, stop facilities).

Page 13

Table 3.1 Reliability and Quality Modelling Assumptions

In-Vehicle Average Boarding Proportion of Component Weighting Benefit Per Penalty Total Benefit Passenger* In vehicle Time Reliability 0.88 1.2 30% Headway Reliability 0.5 0.5 13% Quality 0.97 2.0 2.3 57% Total 0.85 2.5 4.0 100% *Based on passengers who switch from bus to LRT with an average journey time of 10 minutes

Page 14

EMME Model Report

APPENDIX

C

PM UPLIFT TECHNICAL NOTE

Appendix C

To CoM/CoB

Cc SNC

From Les Buckman and Tom Willis

Date 22 March 2013 memo Project Hurontario/Main Street LRT – Network Modelling Project No. 22390004

Subject PM Uplift Analysis

1.1 A process has been developed to derive PM inputs for the VISSIM modelling using the HOT model, essentially creating a PM version of the model by transposing the AM highway matrix.

1.2 As part of this, there is a recognition that this modelling process needs to reflect the generally higher traffic levels observed in the PM peak. Observed conditions in the corridor, as replicated in the VISSIM base model for 2011, reveal that the PM peak period sees 16% more vehicles in the corridor than the AM peak period.

1.3 This uplift factor can be applied for the Base PM model. However, for the 2031 PM forecasts, it is hypothesised that the gap between the peaks will narrow, given that as travel demand grows, the PM will be closer to network capacity and hence less able to grow than the AM. This memo therefore explores the historical trend in vehicle trips in Peel Region, and how this relates to the corridor data and proposes a 2031 uplift factor.

Historical data and uplift trends

1.4 Data was obtained from the historical Transportation Tomorrow Survey (TTS) records, covering the period from 1986 to 2006 at five-year intervals. The data provided the number of vehicle trips by trip start time that started or ended within Peel Region.

1.5 From this data, the highway demand for the AM and PM peaks were derived and compared to compute the equivalent uplift factor for each of the survey years; this is set out in Table 1.

TABLE 1 TREND IN OBSERVED UPLIFT FACTORS

1986 1991 1996 2001 2006

11.9% 8.7% 5.7% 6.0% 4.6%

1.6 The data indicates a definite downward trend in the PM uplift. On average, it decreased by 0.35 percentage points each year from 1986 to 2006. If this historical

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trend is applied to the 2011 observed PM corridor uplift of 16%, then it would be expected that it would fall to 9% in 2031.

1.7 However, such a linear relationship would result in the PM becoming smaller than the AM in the medium term, which is considered unrealistic. On that basis, an exponential decay function has been adopted, which provides a better fit with the data and ensures the uplift factor does not become negative (ie the uplift factor is asymptotic to 0). Over 20 years, the decay function would result in the uplift factor falling 60%, to around 6% in 2031.

1.8 The two approaches are illustrated in Figure 1, based on the Peel wide data set out above.

FIGURE 1 UPLIFT FACTOR TRENDS

VISSIM Base 2011 demands

1.9 The aforementioned 16% difference between AM and PM in the Base year varies by model sub-area; this is shown in Table 2. Of note, all but the DT Mississauga sub- area uplifts to PM are within a relatively narrow band of around 9-16%; Square 1 is materially higher at 27%, reflecting in large part the nature of the area with its material share of retail land uses, notably the Square 1 shopping mall. However, splitting this out to Square 1 mall and non-Square 1 mall indicates that the former has a much higher uplift (168%), whereas demand excluding this provides an uplift more closely aligned with the other areas.

TABLE 2 VISSIM BASE DEMAND

Subarea Description AM PM 2011 uplift

1 Port Credit 4,624 5,127 11%

2 South Corridor 13,210 14,446 9%

2

3 DT Mississauga 25,958 32,896 27%

Square 1 1,498 4,016 168%

Non-Square 1 24,459 28,880 18%

4 North Corridor 32,386 36,314 12%

5 DT Brampton 4,624 5,245 13%

Total 80,802 94,028 16%

1.10 The PM HOT modelling process treats the Square 1 demand separately (without any uplift factor) and therefore the uplift factor should be based on the non-Square 1 demand and the other model areas; this is set out in Table 3. Applying the 60% reduction (using the decay function approach) to derive a 2031 uplift, the range varies between 4% and 7%, with an average of 5.4%.

TABLE 3 VISSIM UPLIFTS

Subarea Description AM PM 2011 uplift 2031 uplift

1 Port Credit 4,624 5,127 11% 4.4%

2 South Corridor 13,210 14,446 9% 3.7%

3 DT Mississauga 24,459 28,880 18% 7.2% (Non-Square 1)

4 North Corridor 32,386 36,314 12% 4.9%

5 DT Brampton 4,624 5,245 13% 5.5%

Total 79,303 90,012 13.5% 5.4%

1.11 Given the relatively small range of factors and the relatively simplicity of the PM HOT model approach used, an uplift factor of 6% has been applied.

3

EMME Model Report

APPENDIX

D

ANNUALISATION FACTOR TECHNICAL NOTE

Appendix D

To CoM/CoB

Cc

From Les Buckman and Agata Pieniek

Date 23 January 2013 memo Project Hurontario/Main LRT Project No. 22390006

Subject Annualization factor for AM peak period boardings

1 Introduction

1.1 This note sets out the derivation of a ridership annualization factor for Hurontario LRT to convert the weekday AM peak period LRT ridership forecast from the HOT model into an annual ridership.

1.2 Boarding count datasets for MiWay (from 2010) and Brampton Transit (from 2011) have been used. The data contained boarding data for each route in the respective networks, split either by period (MiWay) or hourly (Brampton), with data for weekdays, Saturday and Sunday. The analysis has reflected the changes in route numbering since that time to reflect the current route numbers.

1.3 The annualization factor has been calculated at two levels:

I For the group of routes running along Hurontario corridor (currently MiWay 19 and 103, and Brampton 2 and 502)

I For the whole bus network in Mississauga and Brampton.

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2 Initial annualization factor - group of routes 19, 102 & 202

2.1 The initial annualization factor was calculated for the MiWay group of routes only (the Brampton data was not known at the time), and assuming a simple treatment of days in the year. This factor was employed in the derivation of annual ridership presented to the client at the modelling workshops in September and December 2012.

2.2 The following table shows the detail of boarding counts for each route in the morning peak period and for each day of service. The morning peak period to weekday daily and weekday daily to annual boardings factors, as well as the annualization factor have also been calculated.

TABLE 1 ANNUALIZATION FACTOR FOR THE GROUP OF ROUTES 19, 102 & 202

Boarding counts

Weekday Weekday Daily / morning Saturday Sunday Annual Morning Annualization Route daily Annual peaks peak / Daily Factor boardings Number of 260 52 52 364 days

19 3,932 23,268 14,764 9,423 7,307,404 5.92 314 1,858 102 (became 502) 407 1,053 0 0 273,780 2.59 260 673 202 (became 103) 731 1,761 0 0 457,860 2.41 260 626 Group of routes 5,070 26,082 14,764 9,423 8,039,044 5.14 308 1,586

2.3 Based on the group of routes 19, 102 & 202, the transit morning peak period to annual ridership annualization factor is 1,586.

2

3 Annualization factor for the group of routes 19, 202,2 & 502

3.1 With the availability of the Brampton data, the previous analysis has been updated and refined to better reflect the days of the year, with the analysis based on four routes running along the Hurontario corridor:

I Route 19 and 202 (Mississauga);

I Route 2 & 502 (Brampton). 3.2 The following table shows the detail of boarding counts for each route in the morning peak period and for each day of service and the resulting analysis.

TABLE 2 ANNUALIZATION FACTOR FOR THE GROUP OF ROUTES 19, 202, 2 & 502

Boarding counts

Weekday Weekday Daily / morning Saturday Sunday Annual Morning Annualization Route daily Annual peaks peaks / Daily Factor boardings Number of 250 52 63 365 days

19 3,932 23,268 14,764 9,423 7,178,377 5.92 309 1,826 202 731 1,761 0 0 440,250 2.41 250 602 (became 103) 2 653 3,265 1,655 1,159 975,327 5.00 299 1,494 502 1,637 7,669 4,798 2,863 2,347,115 4.68 306 1,434 Group of 6,953 35,963 21,217 13,445 10,941,069 5.17 304 1,574 routes

3.3 Based on the group of routes 19, 202, 2 and 502, the transit morning peak period to annual ridership annualization factor is 1,574. This is very similar to that calculated using MiWay data only.

3

4 Annualization factor for the whole bus network in Mississauga and Brampton

4.1 The analysis has also been undertaken for the total networks of each municipality and this is set out below.

TABLE 3 ANNUALIZATION FACTOR FOR THE COMPLETE BUS NETWORKS

Boarding counts

Weekday Weekday Daily / morning Saturday Sunday Annual Morning Annualization Route daily Annual peak peaks / Daily Factor boardings Number of 250 52 63 365 days

Mississauga 35,735 158,516 70,453 39,040 45,752,076 4.44 289 1,280

Brampton 23,792 105,702 44,646 26,049 30,388,179 4.44 287 1,277

All network 59,527 264,218 115,099 65,089 76,140,255 4.44 288 1,279

4.2 Based on the whole bus network in Mississauga and Brampton, the transit morning peak period to annual ridership annualization factor is 1,279. This is materially lower than the corridor routes only and reflects the reduced weekend operation across some of the network.

4

5 Proposed annualization

Background

5.1 The annualised demand forecast during 2012 was considered excessive and beyond reasonable benchmarked demand on existing systems. Part of the reasoning for this was traced to what is commensurately considered a high annualisation factor, particularly in comparison to the standard Metrolinx factor of 900.

5.2 A daily to annual figure of around 300 is typical and reasonable given 250 workdays/year, with allowance for overall weekend demand being comparable to a weekday (and hence an additional 50 days or so). The factor calculated from the boardings data are consistent with this. On that basis, the ‘excess’ annualisation factor must derive from a high AM peak period to daily factor. Certainly, the corridor route analysis show factors around 5.14-5.17, with the whole route analysis lower at 4.44; this is high compared to the implicit factor of 3 in the Metrolinx annual factor.

5.3 To assist in understanding of the weekday profile, the various data has been plotted to illustrate the observed profile through the weekday.

5.4 Figure 1 shows the weekday boardings for the corridor routes, split by route (left hand scale) and in total (right hand scale). The two peaks are evident, but of note is that the PM peak is higher than the AM peak (by 28% for the total of the four routes) and also that the midday demand is not materially lower than the AM peak.

FIGURE 1 WEEKDAY BOARDINGS BY ROUTE AND FOR THE GROUP OF ROUTES

5.5 A similar, albeit less pronounced pattern, is also evident in the network wide data, shown in Figure 2.

5

FIGURE 2 WEEKDAY BOARDINGS FOR THE WHOLE BUS NETWORK

5.6 Overall, the peak period to weekday factor of 4.5 to 5 reflects the demand profile on the system.

Proposed approach

5.7 As previously noted, the high annualised demand was in part considered due to the annualisation factor. It is possible that as the corridor develops and the importance of the Square One shopping centre becomes less dominant this factor could decline and become closer to the network average.

5.8 The proposed approach to deriving an annualisation factor therefore is to employ a hybrid of the corridor and network factors. This assumes the current demand has the higher factor, reflecting established demand and trip patterns, but that the demand increment arising from land use and network development will tend to reflect the network wide factor.

5.9 Using the HOT model forecasts for 2006 and 2031 BAU, as follows:

I AM peak base demand on corridor 9,613 at 1,574

I BAU demand on corridor 20,435 and hence increment of 10,822 at 1,279

I Weighted annualisation factor 1,418

6

CONTROL SHEET

Project/Proposal Name Hurontario-Main LRT

Document Title EMME Model Report

Client Contract/Project No. Click here to enter text.

SDG Project/Proposal No. 223900-04

ISSUE HISTORY

Issue No. Date Details

REVIEW

Originator Karen Crothers

Other Contributors

Review by: Print Leslie Buckman

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DISTRIBUTION

Client: City of Mississauga and City of Brampton

Steer Davies Gleave:

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Control Sheet