Using Micro-simulated Traffic Conflicts as a

Surrogate Safety Assessment Technique for

Evaluating Safety Performance of Transit Design

Alternatives at Signalized Intersections

by

Lu Li

A thesis submitted in conformity with the requirements

for the degree of Master of Applied Science

Graduate Department of Civil Engineering

University of Toronto

© Copyright by Lu Li 2015

ii

Using Micro-simulated Traffic Conflicts as a Surrogate Safety Assessment

Technique for Evaluating Safety Performance of Transit Design

Alternatives at Signalized Intersections

Lu Li

Master of Applied Science

Graduate Department of Civil Engineering

University of Toronto

2015

Abstract

This study focuses on crash prediction modelling at intersection-level using micro-

simulation to produce an effective surrogate safety assessment measure. The developed

crash prediction model followed generalized linear model with negative binomial error

structure to correlate the simulated traffic conflicts with the observed crash frequency in

Toronto, Ontario, Canada. Individual crash prediction models were developed for every

impact types and for transit-involved crash type. The resulting statistical performance

suggested adequate predictive ability. Based on the established correlation between the

simulated conflicts and observed crashes, scenarios were developed to investigate the

safety impacts of transit infrastructures by making hypothetical transit infrastructure

modifications in the micro-simulation networks. The findings implied that the existing

transit signal priority schemes implemented in Toronto had negative contributions on

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safety performance and that the existing near-sided stop positioning and streetcar transit

type were safer at their existing states than if they were replaced by their respective

counterparts.

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Acknowledgements

I would like to express my sincere appreciation to my supervisors Dr. Amer Shalaby and Dr.

Bhagwant Persaud. This thesis could not have been completed if not for their encouraging

academic guidance and supports. I would like to thank the City of Toronto for sharing the traffic

volume and signal timing data, the Toronto Transit Commission for sharing the transit service

data, and Taha Saleem for maintaining and sharing the crash data. I am grateful for the technical

and professional helps from Asmus Georgi and Mohamed Mahmoud.

I am highly indebted to my parents for their loving supports in my pursuing for this Master’s

degree. This academic endeavour would not have been motivated if not for their continuous and

unconditional care. Lastly, I am truly grateful to my friends for their inspiration and warmth in

supporting me throughout my academic pursuing and my life.

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Table of Contents

List of Tables ............................................................................................................................... viii

List of Figures ................................................................................................................................ ix

List of Abbreviations ..................................................................................................................... xi

1 Introduction ............................................................................................................................. 1

1.1 Motivation ........................................................................................................................ 1

1.2 Scope and Objective ......................................................................................................... 3

1.3 Organization of Thesis ..................................................................................................... 5

2 Literature Review.................................................................................................................... 6

2.1 Conventional Safety Evaluation Measures ...................................................................... 6

2.2 Surrogate Safety Assessment ........................................................................................... 8

2.3 Micro-simulation Technique .......................................................................................... 12

3 Methodology ......................................................................................................................... 17

3.1 Stage 1 Pilot Study ......................................................................................................... 18

3.2 Stage 2 Micro-Simulation Model Construction ............................................................. 19

3.3 Stage 3a Model Calibration ............................................................................................ 20

3.3.1 Crash Prediction Model (CPM) .............................................................................. 21

3.3.2 Goodness-of-Fit ...................................................................................................... 23

3.3.3 Calibrated Parameters ............................................................................................. 25

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3.4 Stage 3b Model Validation ............................................................................................. 27

3.5 Stage 4 Scenario Tests.................................................................................................... 30

4 Pilot Study and Data Description .......................................................................................... 34

4.1 Pilot Study ...................................................................................................................... 34

4.2 Data ................................................................................................................................ 37

4.2.1 Study Area and Period ............................................................................................ 37

4.2.2 Crash Records ......................................................................................................... 38

4.2.3 Traffic Volume and Transit Service Information ................................................... 40

4.2.4 Signal Timing Plans and Intersection Geometry .................................................... 40

5 Model Construction, Calibration and Validation .................................................................. 42

5.1 Model Construction ........................................................................................................ 42

5.2 Model Calibration .......................................................................................................... 45

5.2.1 Driver Reaction Time (DRT) and Mean Headway Time (MHT) ........................... 45

5.2.2 Coverage Area of Analysis, Time to Collision, and Travelling Speed ................... 47

5.2.3 Peak Hour Ratio (PHR) .......................................................................................... 50

5.3 Model Validation............................................................................................................ 51

6 Scenario Tests ....................................................................................................................... 59

6.1 Effects of TSP ................................................................................................................ 59

6.2 Effect of Transit Stop Positioning .................................................................................. 65

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6.3 Effect of Transit Type .................................................................................................... 69

7 Conclusion and Future Work ................................................................................................ 73

References ..................................................................................................................................... 76

Appendix A List of Modelled Micro-simulation Networks ......................................................... 81

Appendix B Detailed Scenario Test Results ................................................................................. 89

viii

List of Tables

Table 3-1 Set-ups of Scenario Tests ............................................................................................. 31

Table 4-1 Descriptive Statistic for the Selected Sample ............................................................... 39

Table 5-1 Calibration Results of Parameters DRT and MHT....................................................... 46

Table 5-2 Calibration Results of Parameters Coverage Area, TTC, and Speed ........................... 48

Table 5-3 Calibration Results for the Inclusion of PHR ............................................................... 50

Table 5-4 Model Validation Results ............................................................................................. 52

Table 6-1 Predicted Results of TSP Treatments ........................................................................... 60

Table 6-2 Predicted Results of Transit Stop Positioning Treatments ........................................... 66

Table 6-3 Predicted Results of Transit Type Treatments ............................................................. 70

ix

List of Figures

Figure 2-1 Conflict Trajectory Diagram ....................................................................................... 12

Figure 3-1 Flow Chart of the Structure of the Study .................................................................... 17

Figure 3-2 Flow Chart of the Schematics of the Generation of Traffic Conflicts ........................ 21

Figure 4-1 Screenshot of the Pilot Study Network ....................................................................... 34

Figure 4-2 Observed Number of Crashes [2006 – 2010] at the Signalized Intersections of the

Selected Sample ............................................................................................................................ 38

Figure 4-3 Observed Crash Frequency [from 2006 to 2010] for the Selected Sample................. 39

Figure 5-1 Demonstration Network 1 - Lake Shore Blvd W and Marine Parade Dr ................... 43

Figure 5-2 Demonstration Network 2 - Finch Ave E and Tapscott Rd ........................................ 43

Figure 5-3 Demonstration Network 3 - Lake Shore Blvd E and Lower Jarvis St ........................ 44

Figure 5-4 CURE Plot - All Impact Type Crashes ....................................................................... 54

Figure 5-5 CURE Plot - Angle Crashes ........................................................................................ 55

Figure 5-6 CURE Plot - Rear-End Crashes .................................................................................. 55

Figure 5-7 CURE Plot - Side-Swipe Crashes ............................................................................... 56

Figure 5-8 CURE Plot - Transit-Involved Crashes ....................................................................... 56

Figure 6-1 Absolute Change in Predicted Crash frequency as a Result of Removing TSP [by

Transit Types] ............................................................................................................................... 63

x

Figure 6-2 Percentage Change in Predicted Crash Frequency as a Result of Removing TSP [by

Transit Types] ............................................................................................................................... 64

xi

List of Abbreviations

CPM - Crash Prediction Model

DeltaS - Speed Differential

DR - Deceleration Rate

DRT - Driver Reaction Time

GLM - Generalized Linear Model

MaxS - Maximum Speed

MHT - Mean Headway Time

NB - Negative Binomial

PET - Post-Encroachment Time

SPF - Safety Performance Function

SSAM - Surrogate Safety Assessment Model

TTC - Time to Collision

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1 Introduction

Over the years, increasing efforts have been devoted into the field of traffic safety modelling. The

ability to evaluate the safety performance of an existing transportation infrastructure, or more

ideally a planned upcoming transportation infrastructure, could provide policy makers invaluable

understandings of how they could contribute towards safer roadway environment. Since

transportation infrastructures are generally irreversible in practice, the ability to predict the safety

impacts of planned transportation projects will stimulate a new facet of design considerations to

further ensure the engineering validity and robustness of transit designs.

Surface transit operation, albeit being an inseparable component of traffic flow, is often less

explored in comparison to general traffic. Aside from being dimensionally different from general

traffic, surface transit also exhibits many distinct operational characteristics, such as having transit

stops, inflexible lane usage, and occasionally priorities over other vehicles. As a result, this

academic endeavour was motivated to explore the safety impacts of transit operations, especially

those impacts not only to the transit vehicles themselves but also those imposed on general traffic.

Using micro-simulation as an effective surrogate safety assessment tool, this study seeks

quantitative answers to the questions of how, and to what extent do transit operations influence

overall intersection-level roadway safety performance.

1.1 Motivation

In the past, the designing stage of transit operations often emphasized on the more quantitatively

measurable norms such as capacity, delay, reliability, and transferability. Although some remarks

regarding safety impacts were occasionally provided, they were often of a qualitative and non-

deterministic nature. For example, in the long debated confrontation between near-sided versus

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far-sided transit stops, both types have safety benefits and harms that are easily perceivable but

not deterministically quantifiable. Transit Signal Priority (TSP), an item that facilitates transit

movement at signalized intersections, also has had controversial safety impacts. TSP has often

been serviced in green extension individually, combined green extension and red truncation, or a

mixture of basic or more advanced priority algorithms; green extension and red truncation

respectively correspond to the practices of extending the green time and shortening the red time

for the transit-facilitating direction. The safety impacts of TSP have been especially difficult to

quantify given the stochastic trigger condition and operation of TSP, let alone being in the already

complex traffic operation environment. These challenges have motivated this academic endeavour

to investigate the safety impacts of transit infrastructures and furthermore to provide directions for

future transit planning.

Traditionally, the evaluation of safety performance has been done based on observed historic data.

This method involves using a set of observable characteristics as the explanatory variables to

establish a regression relationship with the response variable of interest, which has generally been

crash frequency. This method heavily relied on the representativeness of the observed data; as a

result, any inappropriate handling of population skewness may lead to undetected erroneous

conclusions. For the context of this study, since transit routes often service areas of high population

density, which arguably also have high traffic density, the effectiveness of using this method

diminishes. Crashes are generally rare events and thus require a long observation period or many

sites to establish an adequate sample size. Such long observation period of crash events, or

attaining a large sample of sites is impractical. In addition, the traditional regression analysis

method, while providing helpful understanding of the contribution of each explanatory variable,

has very limited sensitivity to detailed geometric, signal, and operational characteristics, which are

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generally not easily quantifiable into explanatory variables. To overcome these difficulties, this

study has been motivated to adopt the micro-simulation technique as a surrogate assessment tool

for safety analysis. Micro-simulation emulates the microscopic interactions of individual roadway

users in the complex geometric, signal, and operational environment. Such interactions are an

integral component of the process through which crashes occur. A well-calibrated micro-

simulation model allows its users to observe the impacts of any experimental infrastructure

modification while maintaining all other explanatory variables status quo; this effectively allows

for a direct observation of the safety impact from, and only from the modifications made at a single

site.

1.2 Scope and Objective

Among the two commonly used measurements of safety performance, this study focuses on crash

frequency as the sole response variable. Crash frequency has generally been defined as the number

of crash occurrences within a pre-defined time period. Thus for the context of this study, the

quantitative term “crash frequency” alludes to the qualitative term “safety performance”, such that

an increasing crash frequency represents worsened safety performance and vice versa. It is worth

distinguishing crash frequency from the other safety performance measure, which is the crash

severity. Crash severity generally measures the probability of resulting in more severe injuries or

damages in the event of a crash. The evaluation of crash severity differs conceptually from that of

crash frequency and therefore does not fit within the scope of this thesis. Consequently, although

the overall safety performance is conceptually a mixed product of crash frequency and crash

severity, only the former one is used for assessing safety performance for the context of this study.

This study focuses on intersection-level safety performance. Despite of being only one component

of the large mass of traffic roadways, intersections are inherently concentrated areas of traffic

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conflicts and crashes. It is not surprising that within the study region of the city of Toronto, more

than 75% of the recorded crashes are associated with intersection traffics. Therefore, all analyses

discussed within the context of this study, allude to intersection-level safety performance.

This thesis has two primary objectives. The first objective is to test the predictive capability of the

micro-simulation technique, when applied to assess the safety performance of transit

infrastructure. Using the simulated traffic conflicts as the explanatory variable and the observed

traffic crashes as the response variable, a set of Crash Prediction Models (CPMs) is developed to

describe the relationship between the two. The CPMs are calibrated using Generalized Linear

Modeling (GLM) with a Negative Binomial (NB) error structure. Ideally after careful calibration

and validation of the micro-simulation networks, if the CPM demonstrates strong statistical

significance and goodness-of-fit, the micro-simulation technique can be regarded as having

adequate predictive capability to emulate crash behaviours in reality.

The second objective is to examine the safety impacts of a number of hypothetical transit

infrastructure modifications. This would only be possible once the first objective has been

completed and the resulting CPMs have demonstrated adequate predictive capability. Through the

use of micro-simulation models, the safety performance of three main categories of transit

operation designs, namely the TSP, the positioning of transit stops, and the transit vehicle type are

investigated. For each category, modifications are made to the existing transit infrastructure in the

controlled environment of the micro-simulation networks; the resulting safety performance

following each infrastructure modification is monitored and compared with the existing condition.

Again, because other explanatory variables are held status quo, any change in safety performance

can be reasonably assumed to only be as a result of the modifications made. The goal of this

exercise is therefore to capture the independent responsiveness of the intersection-level safety

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performance as a result of each individual modification. Such direct observations would otherwise

not have been possible in reality due to the complex and potentially interconnected characteristics

among the explanatory variables, let alone the need for a large sample of crash data.

1.3 Organization of Thesis

This thesis is organized in three sections, sequentially leading up to the objective of examining the

safety impacts of various transit infrastructure modifications. The first section discusses the

background that essentially forms the backbone of this thesis. In this section, past literature is

firstly reviewed, followed by a discussion of the methodologies adopted in this study. Then, this

section reviews the results of the pilot study, in which the viability of the available software and

data inputs were initially determined for this study. Lastly, this section discusses the collection of

these needed data, as well as other assumptions made for the construction of the micro-simulation

models.

The second section mainly focuses on the calibration and validation of the micro-simulation

technique adopted in this study. Following construction of the micro-simulation networks based

on the collected data, these networks are calibrated and validated. This section presents the

statistical significance and goodness-of-fit of the resulting CPMs, as well as a discussion of the

strength of the CPMs for prediction purposes.

The last section presents the scenario tests, in which hypothetical transit infrastructure treatments

are made to the existing micro-simulation networks. As mentioned, three main categories,

respectively TSP, transit stop positioning, and transit type, are investigated. Within each category

are several individual scenarios, such that each scenario examines one specific component of that

category. The resulting change in safety performance is discussed and compared in this section.

The practical implications of the change in safety performance are also discussed in this section.

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2 Literature Review

The evaluation of safety performance has been an increasingly studied field as the ability to

understand contributing factors towards safer roadway designs is invaluable. An extensive number

of mathematical models has been developed around the world to test the statistical significance of

various explanatory variables that were observed in historical data. The feasibility and

extensiveness of most safety performance studies have been largely dependent on the availability

of data and the credibility of the data collection method. For most studies presented in this literature

review, collision data have been collected retrospectively. This means that each reported collision

had already been recorded individually at the time of the collision occurrence prior to the collision

data being used for safety analysis.

Although there have been extensive efforts spent on safety evaluation, the transit operation aspect

has been limitedly investigated. Surface transit operation has a crucial impacts on the flow of

traffic. Many elements of transit operation, such as its stop location, stop density, signal priority,

and even its acceleration/deceleration profile can differentiate the kinematic motion of a transit

vehicle from that of a general vehicle. However, only a small number of past researches has

focused on the safety impacts of transit operations on general traffics. More importantly, the safety

implications from these literatures have been mixed (Goh et al., 2013).

2.1 Conventional Safety Evaluation Measures

Crash record, when evaluate over a period of time, is a direct and commonly available piece of

data that represent the safety performance of a study region. Traditionally, crash frequency has

often been evaluated based on the study region’s observable factors. These factors often include

traffic volume, pedestrian volume, segment length, segment width, speed, as well as other binary

factors such as the presence of driveways, intersections, dedicated lanes, etc. In the past, crash data

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from different areas in the world have been tested and researchers have reached a consensus on

that traffic volume is a key contributor towards crashes (El-Basyouny & Sayed, 2009; Hadayeghi,

Shalaby, & Persaud, 2007; Jonsson, Ivan, & Zhang, 2007; Mitra & Washington, 2007; Persaud,

Lord, & Palmisano, 2002). Traffic volume was often represented by vehicle-kilometer-travelled

for corridor-level study or by Annual Average Daily Traffic (AADT) volume for intersection-level

study. For intersection-level studies, AADT volume was often further categorized into volume in

the major direction and in the minor direction. In most of these literatures, it has been a generally

accepted practice to use GLM with NB error structure (or NB regression model for short) to model

crash frequency to account for the rare, discrete, and non-negative nature of crash occurrences.

However, other regression models were also tested. For example, El-Basyouny and Sayed (2009)

advocated the use of Multivariate Poisson-Lognormal Regression, in order to incorporate the

correlations among crashes of different severity levels. In this literature, it was demonstrated that

Multivariate Poisson-Lognormal Regression could have higher model precision and goodness-of-

fit, when compared with the conventional univariate NB regression models. In addition, due to the

ability to model crashes of different severity levels, this model formulation allowed for the

possibility to incorporate crash severity with crash frequency. Another CPM model structure, the

Negative Multinomial Regression Model was tested by Caliendo et al. (2007). In this literature it

was suggested that Negative Multinomial regression model with a carefully defined over-

dispersion parameter could have improved explanatory power than the conventional NB regression

model. Nonetheless, despite being relatively simpler to model when compared with the more

complex modelling structures, the conventional NB regression model is still a widely used GLM

structure for crash prediction.

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In comparison with the larger pool of literatures in safety analysis, studies that focused on the

transit operation aspect of safety performance were less common. In many literatures, the transit

operation element has been omitted entirely except that transit volumes were anonymously

included in the total traffic volumes. Such omission inherently implied that a transit would be

indistinguishable from a general vehicle. In literatures that focused on the safety impacts of transit

operation, studies were generally at the corridor level that was in alignment with the investigated

transit route (Hedelin et al., 1996; Cheung et al. 2009; Goh et al. 2014). Many transit operation

elements were found to have statistically significant impacts to safety performance. For example,

in one of the literature, Cheung et al. (2008) used the NB regression model to investigate transit-

involved collisions at corridor-level in Toronto and uncovered that in addition to traffic volumes,

transit volume and near-sided stops also contributed to higher crash frequency. The corridor-level

analysis was followed by a zonal-level model, which included zonal transit operation elements

such as transit-kilometer-travelled, bus stop density, and percentage of near-sided stops; these

transit operation elements were also found to contribute to higher crash frequency. A very similar

result was found by Shahla et al. (2009), which also applied the NB regression model to study the

crash data in Toronto but focused on the intersection-level. In this literature it was also found that

near-sided stops and streetcars would contribute to high crash frequency and thus worsened safety

performance. Additionally, in this literature it was found that the presence of TSP was also

positively correlated with crash frequency.

2.2 Surrogate Safety Assessment

Aside from studying explanatory variables that were directly obtainable from field, researches

have also demonstrated that traffic conflict, a form of surrogate safety measure, is also applicable

for the evaluation of safety performance (Gettman et al., 2008). The idea of using traffic conflict

9

as a surrogate safety measure originated as early as 1967, when this technique was used to measure

the type and frequency of crashes at intersections (Perkins & Harris, 1967). The literature found

that drivers would react to traffic conflicts in an aggressively evasive maneuver in order to avoid

crashes. Thus, a traffic conflict was defined as the potential collision of any two vehicles had their

original moving trajectories be continued. In other words, this definition implied that every crashes

would originally be conflicts but only an unavoidable portion of the total conflicts would result in

crashes. Since then, the use of traffic conflict technique has gained more attention amongst

researchers and practitioners.

Traditionally without the aid of computers, traffic conflicts were collected by field observers

(Older & Spicer, 1976; Zegeer & Deen, 1978). However, it was soon acknowledged that human

observation of conflicts had an uncontrollable degree of subjectivity, which deteriorated the

validity of the data. Furthermore, the precision of whether an event should be classified as a

conflict largely depends on the judgement of the field observers, which in many cases had

inconsistent opinions. Nevertheless, the observed traffic conflicts were shown to have some

correlation with crash frequency. With the emerging computer-aided image recognition ability, the

use of video-camera to capture vehicle conflicts has also been continuously gaining attention. This

technique, if calibrated correctly, would greatly alleviate the labour pressure and remove human

errors. Another noteworthy advantage of using the traffic conflict technique was the avoided needs

for long observation periods to collect the rarely occurring and unwanted crash events. Traffic

conflicts by definition are more frequent events than traffic crashes. As a result, the traffic conflict

technique would be especially helpful for before-after studies, since the observation period would

have been much shorter for reaching an adequately large sample size of traffic conflicts than traffic

crashes. Autey et al. (2012) applied the video-captured traffic conflict technique to investigate the

10

safety impacts of decreased turning angle of channelized right turns. The data collection periods

were as short as four days for both before and after the treatment. Such data collection period

would likely have extended to several years, had crash frequency been used instead of traffic

conflicts. The findings from this literature suggested that the total hourly conflicts were reduced

by almost half due to the right turn treatment and furthermore demonstrated the capability of

applying the traffic conflict technique for before-after studies. One shortcoming of this literature

was the missing linkage between the observed traffic conflicts and the observed crash frequency.

Consequently, even though the results were undeniably promising, they were not yet relatable with

the more straightforward crash frequency.

Motivated by this missing linkage between traffic conflicts and crash frequency, El-Basyouny &

Sayed (2013) proposed one approach to model this linkage. In this literature, a two-phased

approach was proposed. In the first phase, a lognormal regression model was deployed to model

traffic conflicts based on observable explanatory variables such as traffic volume, area type, and

geometric characteristics. The modelled traffic conflicts were then taken as the explanatory

variable in the second phase, in which the crash frequency was modelled using the conventional

NB regression model. Since all observable explanatory variables were used in the first phase for

the modelling of traffic conflicts, in turn traffic conflicts were the sole explanatory variable in the

second phase. For both phases, their respective lognormal and NB regression model demonstrated

adequate goodness-of-fit. Also this literature confirmed the positive association between traffic

conflicts and traffic frequency with the use of the NB regression model. However, this study was

limited by its small sample size, which modelled only 51 signalized intersections.

Clearly, these literatures have strongly demonstrated that the traffic conflict technique is a viable

alternative in the field of safety evaluation. Regardless of the applied technology to capture the

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vehicle trajectories, traffic conflicts could be computationally identified based on the observed

vehicle trajectories and other pre-defined kinematic parameters. Amongst the many thresholds are

the Time-to-Collision (TTC) and Post-Encroachment Time (PET). TTC is defined as the time from

when the two vehicles are on a collision course to when they would collide had their trajectories

not been changed. PET is the time between when the first vehicle initially occupied a spatial

location and when the second vehicle subsequently arrived at the same location (Gettman et al.,

2008). A figurative illustration of TTC and PET is shown by Figure 2-1 below. According to these

definitions, a conflict would be more dangerous and arguably more likely to result in a severe

crash, if it has lower values of TTC or PET. Although TTC and PET have been widely accepted

as criteria of identifying traffic conflicts, unfortunately no universal standards have been set on

their exact threshold values for which conflicting trajectories would be considered conflicts.

12

Figure 2-1 Conflict Trajectory Diagram

Again, TTC and PET are generally latent parameters that are complex to be observed directly from

the field, and are therefore obtained via post-data-processing based on the observed vehicle

trajectories that are comprised of detailed measurable variables such as spatial location and speed

profile. Currently, two common techniques of capturing vehicle trajectories are the video-captured

traffic conflict technique which was briefly discussed above, and the micro-simulation technique

which is discussed next.

2.3 Micro-simulation Technique

Micro-simulation has been a widely applied technique in many fields of transportation. A

cautiously constructed and well calibrated micro-simulation model grants its users the ability to

emulate real world traffic operations. However, the process of the micro-simulation technique can

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be highly data demanding. The construction of micro-simulation networks often requires many

pieces of data, which sometimes may be from different sources, to achieve a desired level of

modelling precision. In addition, extensive calibration and validation efforts are generally needed

to validate that the micro-simulation model is representative of its emulated reality.

Once calibrated and validated, a micro-simulation model can be incredibly powerful in many

analyses. The most noteworthy value added of the micro-simulation technique is that it allows its

users to conduct scenario tests in a controlled environment in which hypothetical treatment can be

made. Such treatment would generally have been costly, time-consuming, and most importantly

irreversible for many transportation infrastructures in reality. In addition, the controlled

environment within the micro-simulation models allows for specific infrastructure treatment while

maintaining all other elements status quo. As a result, the observed changes in the response

variables could only be as a result of the treatments made. Such observations would have been

difficult and time-consuming in the uncontrollable reality, since other observable characteristics,

such as traffic volume and pedestrian volume, may be volatile from day to day. By applying the

micro-simulation technique, researchers can overcome these challenges and can often estimate the

effect of a proposed infrastructure treatment before actually undertaking the treatment. As a result,

the micro-simulation technique can be regarded as a powerful alternative to the traditional before-

after studies. In the past, most literatures have devoted efforts on developing micro-simulation

models to evaluate directly quantifiable norms such as flows, speed, queues, and other variables

of interests. It was not until more recently when researchers have been motivated to explore the

potentials of using the micro-simulation technique for evaluating traffic safety, which are more

difficult to quantify directly (Archer, 2004; Gettman & Head, 2003). In addition to the use of TTC

and PET thresholds as identifiers of traffic conflicts, other identifiers, including Maximum Speed

14

(MaxS), speed differential (DeltaS), and initial Deceleration Rate (DR), were also presented by

Gettman and Head (2003). MaxS measures the higher one of the two conflicting vehicles’

travelling speeds; DeltaS measures the difference in the travelling speeds of the two conflicting

vehicles. Thirdly, Initial DR measures the initial deceleration rate of the second vehicle in the

evasive action to avoid the potential crash. Intuitively, a conflict with a larger value of DR indicates

a higher probability of resulting in crash. On the other hands, the Maxs and DeltaS indentifiers

were argued to be more associated with the severity of the resulting crash, had the crash event

occurred.

The development of these kinematic identifiers of traffic conflicts lead to the development of the

software Surrogate Safety Assessment Model (SSAM) by the U.S. Federal Highway

Administration (Gettman et al., 2008). SSAM aimed to provide additional safety analyzing

capability to existing simulation softwares and currently supports Aimsun, Q-Paramics, TEXAS,

and VISSIM. By studying the vehicle trajectories outputted by the micro-simulation software,

SSAM is capable of recognizing traffic conflicts based on many identifiers such as TTC, PET, DR,

MaxS, DeltaS, etc. Gettman et al. (2008) also demonstrated that with the combined use of the

micro-simulation software VISSIM and SSAM, NB regression models could be developed to

correlate the simulated traffic conflict frequency with the observed crash frequency. A positive

association was found between the simulated traffic conflict frequency and the observed crash

frequency; such positive association was in alignment with that found from the video-captured

traffic conflict technique.

Since the development of SSAM, researchers have devoted more efforts in using micro-simulation

to study traffic safety and the results have been promising. Huang et al. (2013) used the micro-

simulation package VISSIM and found statistically significant correlation between the simulated

15

conflicts and observed conflicts obtained from using video-captured conflict techinique. In this

literature a two-staged model calibration procedure was applied, whereas the first stage calibrated

the model with the observed traffic volume, speed, and headways and the second stage calibrated

the model with the observed video-captured conflicts. A linear regression technique was applied

to correlate the simulated conflicts with the observed video-captured conflicts and strong positive

association was found. One limitation of this literature was the missing linkage between the

simulated conflict frequency and the observed crash frequency. In addition, this literature

somewhat reflected the data demanding nature of the micro-simulation technique, since during the

process many pieces of information were used in the two-staged model development.

Ariza et al., 2011 used the micro-simulation package Paramics and validated a large-scale network.

In this literature the simulated conflicts were also found to have a strong statistical relationship

with observed crashes. Two SPFs, respectively for corridor-level and intersection-level were

developed in this literature. In the comparison it was found that the corridor-level SPF performed

inadequately with poor goodness-of-fit. In contrast, the intersection-level SPF demonstrated a

better goodness-of-fit, thus suggesting more predictability at the intersection-level. The evaluation

of intersection-level safety was extended by Saleem et al. (2014), in which VISSIM was used to

confirm the strong statistical correlation between simulated conflicts and observed crashes. Also

in this literature, a hypothetical treament on modifying existing permissive left turns to protected-

permissive was also evaluated. The results suggested that such treatment would be beneficial and

would reduce the number of angle and turning crashes.

To the knowledge of the author, few literatures have applied the micro-simulation technique to

examine the effect of transit operation on road safety. Although there have been researches focused

on the safety of transit itself, to what extent does transit operation influence general traffic is yet

16

an under-explored field. The closest literature to the scope of this research is that by Goh et al.

(2014). In this literature, dedicated bus priority lanes were micro-simulated using Aimsun and the

effects of such infrastructure treatment on general traffic was examined. It was suggested that

dedicated bus lanes would improve the overall safety performance by reducing the number of

conflicts. However, the analyses in the literature were at the corridor-level along the investigated

transit route and consisted only a limited number of major arterial intersections. As a result, it

lacked the capability of investigating the safety impacts of many intersection-level transit

operation elements, such as transit stop positioning and TSP.

17

3 Methodology

For every micro-simulation model to effectively replicate its real world counterparts, significant

efforts in data acquisition, model calibration, and model validation must be done. Once the micro-

simulation model has been fully constructed, calibrated and validated, it can be considered

adequately representative of its real world counterparts. Then, hypothetical scenarios can be tested

in the controlled environment of the micro-simulation network. Figure 3-1 below presents a flow

chart illustrating the sequential steps undertaken in this study:

Figure 3-1 Flow Chart of the Structure of the Study

As depicted by the flow chart, items that are within the same stage were completed from the top

to bottom. An item did not need to be fully completed to proceed to the next item within the same

stage. However, every item(s) within the same stage must be fully completed in order to start the

first item in the subsequent stage. This chapter illustrates the methodologies undertaken for each

of the four stages of this study.

18

3.1 Stage 1 Pilot Study

Initially it was anticipated that the construction, calibration, and validation would be the most time

consuming items. Thus, a pilot study was first conducted to ensure these subsequent efforts can be

successfully and time-efficiently executed. Another importance of the pilot study was to test if the

available micro-simulation package, Paramics, is a suitable program for all the subsequent works.

More specifically, the pilot study was to investigate the following:

1) the capability of Paramics to incorporate the transit operation, pedestrian, and variable

signal operation elements into its simulation;

2) the capability of SSAM to interpret the trajectories of transit and pedestrian,

3) an acceptable study size such that the simulation time by Paramics and the analysis time

by SSAM are reasonable and not undesirably long; the study size should neither be too

small to avoid poor statistical significance; and,

4) data needs based on the capability of Paramics to implement them.

Despite being the least time-consuming item of all, the pilot study was crucial for all subsequent

efforts. In the pilot study an isolated intersection was created for a simulation duration of 90

minutes (including 30 minutes of warm-up period and 60 minutes of data collection). Vehicle

volume, transit service, and signal timing were introduced to the network. The trajectories for the

duration of the simulation were collected and analyzed in SSAM. Following this, pedestrian,

streetcar, and TSP were also introduced to examine the sensitivity of the trajectories in response

to these modifications.

19

3.2 Stage 2 Micro-Simulation Model Construction

Following the pilot study was the construction of the actual micro-simulation networks. At this

stage, the sample size of the number of isolated intersection networks to be studied was already

estimated from the pilot study. This sample size should be sufficiently large to avoid poor statistical

significance and at the same time not exceedingly large to be undesirably time-consuming.

Moreover, the sample size should be large enough such that any subgroup of interest within the

sample was also reasonably large. Here, a subgroup is a portion of the sample that exhibited a

common characteristic that would later be investigated in scenario tests, such as having streetcars

or TSP. Since random sampling technique was used, a large sample size of the subgroups would

allow for easier generalization to the population.

The micro-simulation models were constructed at the isolated intersection level. Every approach

upstream of the studied intersection would only extend upstream for 120 to 150 meters. The length

of the upstream link varied among intersections, but in general would not overextend into its

upstream intersections; it neither would be too short to avoid the study region becoming

unrepresentative.

The duration of the micro-simulation models was set to 90 minutes, which again included 30

minutes of warm-up period and 60 minutes of statistics collection period. The first 30 minutes of

warm-up period was to stimulate the initially empty networks with the specified vehicle volumes.

This would allow for a more accurate representation of the network once the warm-up period had

been completed. The 60 minutes duration of the simulation was injected with traffic volumes

observed from the PM peak hour so that the simulation would replicate the PM peak hour traffic

operation of the studied intersections. It was demonstrated in previous literature that, peak hour

20

operation, which ideally should represent the critical design performance, was both sufficient and

efficient for safety modelling (Gettman et al., 2008, Saleem et al., 2014).

3.3 Stage 3a Model Calibration

For every micro-simulation model, its calibration and validation must be carefully executed for it

to be representative of the reality. Calibration refers to the practice in which various parameters in

the model structure were adjusted for optimal statistical performance. The statistical performance

of the micro-microsimulation model often depends on a pre-defined set of criteria, which most

frequently are the goodness-of-fit of the simulated variables for fitting the observed variables. The

calibration was performed on a randomly selected subset from the entire sample size. Following

model calibration, the micro-simulation was validated on the entire sample size to ensure the

credibility of the model and further to demonstrate its ability to emulate real-world traffic

operation.

For the context of this study, since the micro-simulation networks were built at isolated intersection

level, the calibration work for such smaller-scale networks was considerably different from that of

the large-scale regional-level networks. Generally for large-scale networks, in which the traffic

volume in the simulation is obtained through traffic assignments, the simulation’s traffic volume

needs to be calibrated with the observed traffic volume. For this study, having the networks at

isolated intersection level avoided the need of a traffic assignment and thus allowed the observed

traffic volume to be used directly as the simulation’s traffic volume. Theoretically it would be ideal

to calibrate and validate the simulated crashes with the observed crashes. However, the micro-

simulation software does not directly simulate crashes but rather simulates vehicle trajectories,

which can be analyzed to produce conflicts. As a result, in this study the calibration and validation

21

of the model were done by assessing the goodness-of-fit of the simulated conflicts with the

observed crash frequency by applying the concept of SPF.

The generation of the traffic conflicts was a sequential step involving both Paramics and SSAM.

Firstly Paramics generated vehicle trajectories once the constructed networks were simulated.

Then taking these trajectories as an intermediary input, SSAM could analyze the trajectories and

output the corresponding traffic conflicts. A schematic of the generation of traffic conflicts is

illustrated by Figure 3-2 below:

Figure 3-2 Flow Chart of the Schematics of the Generation of Traffic Conflicts

3.3.1 Crash Prediction Model (CPM)

CPM belongs to the family of SPF that correlates a set of explanatory variables to the response

variable, which in this case is the observed crash frequency, leading to the terminology of “Crash

Prediction Model”. The simulated conflicts were used as the explanatory variable of the CPM. As

was presented in the literature review, it has been a generally accepted practice to use the GLM

with NB error structures, also known as the NB regression, for predicting frequency. This is

because the NB regression accounts for the discrete, non-negative, and rarely-occurring properties

of crash events.

Let 𝑌𝑖 denotes the crash frequency at intersection i, the expected mean and variation of the crash

frequency of NB regression follows that:

22

𝐸(𝑌𝑖) = �̂�𝑖 and

Equation 1

𝑉𝑎𝑟(𝑌𝑖) = �̂�𝑖 + 𝛼 ∗ �̂�𝑖2 Equation 2

where 𝑌�̂� represents the predicted crash frequency and α represents the dispersion parameter to be

estimated from the NB regression model. Note that the dispersion parameter α also uses one degree

of freedom during parameter estimation. α indicates the strength of the assumption of the negative

binomial error structure. If α is large, over-dispersion is present and the use of NB regression might

become inappropriate. In other words, a smaller value of α indicates better model fitting. It is worth

noting that some statistical software, such as R, uses the inverse of the dispersion parameter and

should not to be confused with the definition in this study (R Development Core Team, 2008). The

concept of over-dispersion will be discussed further in the Section 3.3.2. By applying the NB

regression, the CPM for the predicted crash frequency 𝑌�̂� resembles the following form:

�̂�𝑖 = 𝑒𝜆 ∗ 𝑋𝑖𝛽 or, ln �̂�𝑖 = 𝜆 ∗ 𝛽ln(𝑋𝑖)

Equation 3

where the explanatory variable 𝑋𝑖 represents the simulated traffic conflict frequency for each

intersection i, and 𝜆 and β are parameters of the CPM to be estimated. Since all simulations had a

duration of one hour, the explanatory variable 𝑋𝑖 is more conveniently referred to as “simulated

conflicts”. Similarly, since the recorded frequency for the investigated networks were collected

from the same study period, the response variable �̂� is more conveniently referred to as “predicted

crashes”. The first determination of the strength of the CPM is to examine whether the estimated

parameters were statistically significant. For each variable, the SAS software estimated the p-

value, for which smaller values indicate more evidence against the null hypothesis that the

variable’s parameter is zero. A p-value of less than 0.05 indicated 95% confidence in rejecting the

null hypothesis and the variable can be regarded as statistically significant. For this study, 95%

23

confidence was used in assessing the statistical significance of variables of interest. Since the

simulated conflicts were the sole explanatory variable being investigated, if the parameter for this

variable was determined to be non-significant, the model would have no predictive capability.

3.3.2 Goodness-of-Fit

The statistical software SAS was used for the estimation of parameters and the assessment of the

CPM’s goodness-of-fit. Since the NB regression belongs to the family of GLM, the PROC

GENMOD procedure of SAS software was used, which allowed for the maximum likelihood

estimation of the model parameters in the CPM (SAS Institute Inc., 2008). The goodness-of-fit

measures assessed the strength of the modelled relationship between the simulated conflicts and

the observed crashes. Three measures of goodness-of-fit were evaluated: the scaled deviance, the

Pearson χ2, and the Miaou’s R2.

The Scaled Deviance (SD) is the ratio that measures twice the difference between the maximum

log likelihood of the proposed CPM and the maximum achievable log likelihood of a “full” model

(or sometime referred to as the “saturated” model). A “full” model would have as many parameters

as the number of observations and thus would perfectly fits the observed crashes, providing a

benchmark for assessing the goodness-of-fit. McCullagh and Nelder (1989) demonstrated that the

SD for NB regression model follows the form:

𝑆𝐷 = 2∑[𝑦𝑖 ln (

𝑦𝑖

�̂�𝑖) − (𝑦𝑖 − 1/𝛼) ln (

𝑦𝑖 + 1/𝛼

�̂�𝑖 + 1/𝛼)]

𝑛

𝑖=1

Equation 4

where 𝑦𝑖 represents the observed crashes at the intersection i, and n is the total number of

intersections being studied.

24

The Pearson χ2 was the second goodness-of-fit measure, which is the summation of squared

residuals scaled by the variance of the modelled variable. The Pearson χ2 can be mathematically

illustrated as follow:

𝑃𝑒𝑎𝑟𝑠𝑜𝑛χ2 =∑

[𝑦𝑖 − �̂�𝑖]2

𝑉𝑎𝑟(𝑌𝑖)

𝑛

𝑖=1

, 𝑤ℎ𝑒𝑟𝑒𝑉𝑎𝑟(𝑌𝑖) = �̂�𝑖 + 𝛼 ∗ �̂�𝑖2

Equation 5

Both the SD and the Pearson χ2 are asymptotically distributed following χ2 distributions with n-p

degrees of freedom. Again, n is the sample size and the p is the number of parameters being

estimated. If the SD and the Pearson χ2 are smaller than the critical χ2 value at the modelled degree

of freedom, it can be concluded at 95% confidence that the NB regression model has provided an

adequate fit to the observed data. In addition, the effect of dispersion can be estimated by assessing

the ratios of either the SD or the Pearson χ2 divided by the n-p degree of freedom. More

specifically, both 𝑆𝐷/(𝑛 − 𝑝)and Pearsonχ2/(𝑛 − 𝑝) should be close to the value of 1.0 for

proper dispersion. If these indicators are larger than 1.0 then over-dispersion is present; conversely

if they are less than 1.0 then under-dispersion is present. For this study, values between 0.8 and

1.2 would be considered good fitting for the dispersion of the specified NB regression model.

The third goodness-of-fit measure was the Miaou’s 𝑅𝛼2, which is explicitly based on the dispersion

parameter α. This measure was proposed by Miaou (1996) and the formulation follows the form:

Miaou′s𝑅𝛼2 = 1 −

𝛼

𝛼𝑚𝑎𝑥

Equation 6

where 𝛼𝑚𝑎𝑥 is the maximum possible dispersion parameter estimated by using only a constant

term as the explanatory variable in the SPF. The Miaou’s 𝑅𝛼2 resembles the concepts of the R2,

which is the more traditional measure used to evaluate the goodness-of-fit of the ordinary least-

25

square regression. Miaou’s 𝑅𝛼2 ranges between the value 0 and 1, with higher 𝑅𝛼

2 indicating more

explanatory power of the NB regression model.

3.3.3 Calibrated Parameters

During the model calibration, key parameters were adjusted so that the resulting simulated

conflicts had a statistically defensible relationship with the observed crashes. Both Paramics’

simulation parameters and SSAM’s filtering parameters would dictate the simulated conflicts and

therefore would need to be calibrated. The process of parameter calibration was an iterative

approach of seeking the optimal set of parameters within both Paramics and SSAM such that the

resulting CPM would have the best performing goodness-of-fit.

In Paramics, Driver Reaction Time (DRT) and Mean Headway Time (MHT) are two of the core

parameters modifiable within network attribute. These parameters are closely connected with the

fundamental car-following, gap acceptance, and lane-changing models embedded in Paramics

(Duncan, 1997). DRT specifies the average delay in response time of the following vehicle in

response to a change in speed of the preceding vehicle. MHT specifies the average target headway

maintained between the preceding and the following vehicles. It is worth mentioning that both

parameters only represent the desired average value. The actual individual DRT and MHT

assigned to each driver vehicle unit respectively follow normal probability distributions with

means equal to the specified DRT and MHT. A reduction in either of the DRT or MHT would

result in more aggressiveness in average driving behaviour. Thus, both DRT and MHT must be

calibrated such that the simulation model had the most realistic driving behaviour for intersection-

level safety modelling.

Following adjusting the simulation parameters in Paramics, it was also essential to test the

sensitivity of the filtering parameters embedded in SSAM. These filtering parameters dictate the

26

proportion of raw conflicting trajectories to be pertained as conflicts. Two filtering criteria were

tested in this section, namely the coverage area of the analysis and the simultaneous adjustment of

TTC and travelling speed. The coverage area of the analysis specifies the lateral and longitudinal

range in which SSAM conducts its trajectory analysis. In other words, the coverage area dictates

the distance upstream of the intersection beyond which would be filtered out by SSAM. In reality,

if a crash did not occur within obvious proximity of the intersection, whether it would be classified

as midblock or intersection would largely depend on the recorder’s own judgement on an

acceptable upstream distance. Thus the goal of adjusting the coverage area was to match the

analysis area with the actual area in which crashes would be recorded as intersection crashes. Next,

TTC and travelling speed were also considered together as a filtering criteria to eliminate conflicts

from questionable or impractical driving behaviour. This filtering criteria was suggested by

Gettman et al. (2008). The criteria invovled setting a minimum TTC threshold of 0.05 seconds and

a minimum travelling speed of 16.1 km/hr. The minimum TTC threshold is to account for the rare

occasions in which two simulated vehicles occupy the same physical location, which is not

possible in reality. The minimum travelling speed threshold is included for the reason that a

conflict arguably would have been avoided or not reported in reality if both vehicles had been at

very low speed. Note that the maximum thresholds for TTC and PET were kept at their default

values, respectively 1.5 and 4.8 seconds. These two parameters, despite having an important

influence in dictating traffic conflicts computationally, are arguably more connected with crash

severity than with crash frequency. Since crash severity is a subject beyond the scope of this study,

the maximum thresholds for TTC and PET were not calibrated.

After the parameter calibration for both Paramics and SSAM, the CPM werealso tested to examine

the effect of introducing additional explanatory variables aside from the simulated conflicts.

27

Thereotically, every pieces of data that were initially used to develop the micro-simulation model,

should have already contributed to the generation of the simulated conflicts. Therefore the

introduction of new explanatory variables can only be those not used in the construction of the

micro-simulation model. In this study, the effect of Peak Hour Ratio (PHR) was investigated.

PHR was suggested by Saleem et al. (2014) to account for the representativeness of the peak hourly

traffic pattern to the daily traffic pattern. Since the simulation model captured only the PM peak

hour, its representativeness of the daily crash pattern should be investigated. In the study, the PHR

was assumed to be the same proportionality of the peak hourly traffic volume to the daily traffic

volume, which varied among intersections.

Ideally, the calibration of Paramics parameters, SSAM parameters, and the new explanatory

variable PHR should be simultaneous, such that all exhaustive combinative possibilities were

investigated. However, this would extend the time of the calibration process considerably. Thus

for this study model calibration was performed in sequential fashion, in which Paramics

parameters were calibrated first, SSAM parameters were calibrated second, and the effect of PHR

in the CPM was calibrated last. This sequence was in accordance with that in the generation of the

simulated conflicts. In addition, only the total simulated conflicts and total observed crashes were

correlated and compared in model calibration. In other words, the calibration of parameters was

based on the goodness-of-fit of modelling only the total observed crashes, which was not yet

dissectted by their impact types or by the involvement of transit until model validation.

3.4 Stage 3b Model Validation

Following model calibration on the randomly selected subset of samples, the optimal set of

parameters was applied to the entire sample size, thus validating the micro-simulation model. The

objective of the model validation was to ensure that the calibrated parameters in Paramics and

28

SSAM would also be adequate when applied to the entire sample size. This was tested by

examining the goodness-of-fit of the CPMs, when the entire sample had its simulated conflicts

tested with its observed crashes. A total of five CPMs were developed; the first one modelled the

relationship between all simulated conflicts and all observed crashes; the next three CPMs

modelled the relationship between angle, rear-end, and side-swipe impact type of simulated

conflicts and their respectively corresponding observed crashes; finally the last CPM modelled the

relationship between transit-involved simulated conflicts and transit-involved observed crashes.

Four goodness-of-fit measures were used in model validation. The first three measures were again

the SD, the Pearson χ2, and the Miaou’s R2, as were introduced in the Model Calibration section.

One additional graphical representation of the goodness-of-fit was used here, which was the

CUmulative REsidual (CURE) analysis. The CURE analysis is a powerful tool for assessing

whether the specified relationship between the response and explanatory variables is justifiable. In

this study, scaled residuals, rather than raw residuals, were used. Scaled residuals are the difference

between the estimated and the observed response variable, scaled by response variable’s standard

deviation. The scaled residuals are also sometimes referred to as the Pearson’s residuals, because

the summation of squared scaled residuals would lead to the Pearson’s χ2. However in the CURE

analysis, the cumulative scaled residuals are not squared and thus can be either positive or negative.

The observations are ranked based on the numerical values of the leading explanatory variable in

ascending order. The leading explanatory variable, which in this study would be the simulated

conflicts, is taken as the x-axis in the CURE analysis plot. The cumulative scaled residuals are

plotted as the y-axis. The formulation of the cumulative scaled residuals is as follow:

CumulativeScaledResiduals ateachobservationk

=∑𝑦𝑖 − �̂�𝑖

√𝑉𝑎𝑟(𝑌𝑖)

𝑘

𝑖=1

, 𝑓𝑜𝑟𝑘 ≤ 𝑛 Equation 7

29

where k is the k-th observation within the n number of total observations. For each observation,

the scaled residuals should be approximately unbiased and independent. Thus the pattern of plotted

cumulative scaled residuals should resemble that of the random walk phenomenon. Since the slope

of the cumulative scaled residuals plot is the scaled residuals, it can be used to identify biasness.

If the slope of the cumulative scaled residuals plot is consistently positive, the CPM underestimates

the response variable; conversely if the slope is consistently negative, the CPM over-predicts the

response variable. The summation of the scaled residuals (i.e. the cumulative scaled residuals when

k = n) should be approximately zero to indicate non-biasness; otherwise, a pattern in the direction

of cumulative scaled residuals suggests an unobserved systematic effect with increasing k. Such

systematic effect would also suggest that alternative model formulation, or even high-ordered non-

linear model structure might be possible.

In the CURE analysis, the upper and lower bands of the random-walk phenomenon were also

plotted to assist the assessment of the goodness-of-fit of the plotted cumulative scaled residuals.

The random-walk phenomenon, when sample size is large to invoke the central limit theorem,

would resemble normal distribution with a mean of zero and a variance of 𝜎∗2. The formulation

of 𝜎∗2 follows that (as was proven in Hauer & Bamfo (1997)):

𝜎∗2 = 𝜎2(𝑘)[1 −

𝜎2(𝑘)

𝜎2(𝑛)] Equation 8

where 𝜎2(𝑘) and 𝜎2(𝑛) are respectively the variance of the cumulative residuals when i=k-th

intersection and when i=n total number of intersections. The upper and lower bands are

respectively ±2𝜎∗ from the mean of zero, indicating the 95% confidence level of the range of the

random walk phenomenon. Ideally, the plots of the cumulative scaled residuals should fit within

the two bands for an appropriate fitting.

30

Once all goodness-of-fit measures were determined to be acceptable, the micro-simulation

networks and the CPM formulation would be considered representative of the reality and thus

validated. Since the sample was to be randomly selected from the population, this would imply the

established CPM should be representative of the entire population. Therefore, following successful

model validation, the effect on the response variable from any infrastructure change in the micro-

simulation networks could be monitored. This lead to the scenario tests, which is discussed next.

3.5 Stage 4 Scenario Tests

In this study, scenario tests refer to the practice of modifying an existing transit infrastructure in

the micro-simulation models and observing the resulting predicted crashes in response to the

modification. It is worth emphasized again that scenario tests are only possible once the parameters

within the micro-simulation models have been carefully calibrated and validated, such that the

models are a statistically defensible representation of the reality. Through the use of the micro-

simulation models, the effect of an infrastructure change can be observed in a closed and controlled

environment in which other factors would remain status quo. However, modification could only

be made to existing infrastructures for which data were initially available to describe their status

quo conditions. In other words, if an existing infrastructure setting in the micro-simulation model

was based on assumptions, such infrastructure would not be modified in the scenario tests. As was

outlined in the thesis objective, this study investigated the safety impacts of three main categories

of transit infrastructures, namely TSP, transit stop positions, and transit type.

For each scenario, only a number of qualified micro-simulation networks that exhibited the

infrastructure under investigation were included in the scenario. A total of nine scenarios were

designed and are described in more details in Table 3-1 below:

31

Table 3-1 Set-ups of Scenario Tests

Scenario

#

Investigated

Transit

Infrastructure

Investigated

Effects

Network

Selection Criteria

1

TSP

Removal of green extension Have green extension

2 Removal of red truncation Have red truncation

3 Removal of TSP Have TSP

4

Transit Stop

Positions

Near-sided to far-sided

Have near-sided stops along its

major direction

Do not have TSP

5 Near-sided to far-sided

Have near-sided stops along its

minor direction

Do not have TSP

6 Near-sided stop to no stop

Have near-sided stops along its

major direction

Do not have TSP

7 Near-sided to Far-sided

Have near-sided stops along its

TSP-servicing direction

Have TSP

8

Transit Types

Streetcar to Bus (1:1

replacement)

Have streetcar making through

movement

9

Streetcar to Buses (1:n

replacement for same

operating capacity)

Have streetcar making through

movement

As depicted by Table 3-1 above, a total of three categories were being investigated. The first

category investigated the safety impacts of TSP and consisted three scenarios. The three scenarios

respectively tested the removal of green extension, removal of red truncation, and removal of TSP

as a whole (i.e. both green extension and red truncation). Note that only the effect of removing a

TSP scheme was investigated. This is because it was practically more convenient to remove an

existing TSP scheme, than to design and implement a new TSP scheme, which would involve

making new assumptions for the TSP parameters including detection distance, decision point,

increment time, extension/truncation time, etc.

In the second category, four scenarios were designed to investigate how the positioning of transit

stops influences safety performance. The four scenarios respectively tested the effects of switching

32

near-sided transit stops to (1) far-sided stops along major directions at non-TSP intersections, (2)

far-sided stops along minor directions at non-TSP intersections, (3) no stops along major directions

at non-TSP intersections, and (4) far-sided stops along TSP-servicing directions at TSP

intersections. Note that in all four scenarios, the status quo condition was near-sided stops; this is

due to the fact that the majority of the transit stops in the study region was near-sided stops. When

the near-sided stops were switched to far-sided stops, the exact positioning of the far-sided stops

was largely dependent on the geometric environment of each individual network. In accordance

with the general practice of far-sided stops in the study area, the positioning of the far-sided stops

was generally 20 meters downstream of the intersection but may extend further downstream to

avoid obstructing driveways. In addition, the effect of near-sided stops versus far-sided stops was

studied separately for TSP and non-TSP serviced intersections, to avoid the biasness that TSP

generally favors far-sided transit stops with respect to delay performance. Lastly, one scenario was

designed to test the effect of removing transit stops along the major direction entirely to investigate

the safety impacts of having transit stops at intersection-level; the practical implication of this

scenario could be treated as if the transit stops were relocated to midblock.

In the third category, two scenarios were designed to investigate the safety impacts of streetcars

versus buses. In both scenarios, the networks being investigated had their through-moving

streetcars along their major directions being replaced with buses. The networks were only qualified

if their streetcars were making thru-movement; this criterion was to avoid (1) the under-

representation of networks in which streetcars, in very rare occasions, make left-turn/right-turns,

and (2) the biasness in mixing turning movements with thru movement, since the lane usage of a

streetcar’s turning movement is inherently different from a bus’s turning movement. Also, only

streetcars that operated under mixed right-of-way with general vehicles were being replaced in

33

these scenarios. The original median-lane streetcar stops were replaced with curb-lane bus stops.

Additionally, since originally the streetcar’s median-lane stops prohibited upstream vehicles to

pass stopped streetcars, this constraint was also removed when the median-lane streetcar stops

were replaced with curb-lane bus stops. In the first scenario, the streetcars were replaced with

buses according to one to one ratio; this practice was to maintain the same overall transit volume

and thus traffic volume. This scenario would investigate the safety impact of streetcars versus

buses with respect to the contribution of each transit vehicle unit. In the second scenario, each

streetcar was replaced by multiple buses such that the same operating capacities were met for the

transit routes. This scenario was more fair and practical from the transit operation perspective and

would provide insights regarding the overall safety impacts of each transit types.

Following the transit infrastructure modification in each of the nine scenarios, the micro-

simulation networks would be simulated again, using the same seed numbers as were used in the

simulations for the status quo condition. In Paramics, seed number specifies the random number

generation and the vehicle release sequence into the micro-simulation networks. Thus by using the

same seed numbers, the networks would service the same arrival pattern of vehicles released into

the networks for both before and after the infrastructure modification. This eliminated the

systematic randomness in the comparison process and therefore allowed for a more fair

comparison. Using the established CPMs relationship determined from model validation, the

predicted crashes were modelled for both the hypothetical scenarios and their corresponding status

quo condition. Then, the two predicted crashes were compared to examine the relative safety

performance.

34

4 Pilot Study and Data Description

4.1 Pilot Study

The intersection of King Street E and Church Street was used as a template for the micro-

simulation network of this pilot study. The micro-simulation network resembled the actual

physical geometry and all other characteristics, such as traffic volume, pedestrian volume, and

transit operation. Nevertheless, it is worth mentioning again that the objective of the pilot study

was to test the capability of the micro-simulation software, so that subsequent efforts in model

construction, calibration, and validation would be more efficient. A screenshot of this micro-

simulation network is illustrated by Figure 4-1 below:

Figure 4-1 Screenshot of the Pilot Study Network

35

From the pilot study, it was found that:

1) Paramics is capable of incorporating the transit operation element, including adjustable

stop location, dwell time, service headway, median-lane streetcars, and TSP. TSPs such as

green extension, red truncation, or transit pre-emption are not included in the basic set of

Paramics features and require additional user-written programs to override existing signal

operation. Also the re-allocation of unused green-time, which is a common signal operation

scheme in the city of Toronto, can also be simulated in Paramics using user-written

programs. Paramics also has the capability to model detailed pedestrian movement and

pedestrian-vehicle interaction with its Urban Analytics Framework (UAF).

2) SSAM, by default, has the ability to categorize conflicts by their impact type, including

rear-end, angle, or side-swipe. SSAM is also capable of identifying a conflict by its vehicle

type, thus allowing the categorization of transit-related conflicts. However the

identification of vehicle type is not an inherently available function and must be done

through manually filtering the detailed conflict output generated by SSAM. One critical

finding is that Paramics does not generate the trajectories of pedestrians. As a result, neither

pedestrian-pedestrian nor pedestrian-vehicle conflicts can be identified by SSAM. Despite

the inability to recognize pedestrian trajectory, the inclusion of pedestrian does influence

the trajectory of general vehicles due to yielding rules and additional pedestrian demand

on actuated green intervals. To the knowledge of the author, the only micro-simulation

program capable of generating pedestrian trajectory is VISSIM (U.S. Department of

Transportation, 2011). However, as SSAM was initially developed for the analysis of

vehicle-vehicle conflicts, the credibility this technique for analyzing pedestrian-vehicle and

pedestrian-pedestrian conflicts remains questionable.

36

3) Excluding the pedestrian element, Paramics takes approximately 5 minutes to simulate the

pilot study’s network 10 repeated times. Then, SSAM takes approximately another 5

minutes to analyze the trajectory files. Thus if 100 networks of isolated intersections, each

having 10 runs are to be simulated, it would take approximately 16.7 hours. If the

pedestrian element is to be incorporated into the simulation model, the simulation time

would be significantly longer by approximately 3 times. The analysis time by SSAM is

unaffected by the inclusion of pedestrians since pedestrian trajectory is not generated by

Paramics.

4) Traffic volume, vehicle composition, intersection geometry, and signal timing plan data,

are essential data for constructing the simulation networks. Additionally, transit schedules

are also needed as transit arrivals to an intersection affect upstream traffic and in some

cases override the existing signal operation by signal priority. In addition, data regarding

dwell times, stop location, and detailed override scheme of the TSP should also be acquired

for the most realistic representation of transit operation.

From the above findings, it was determined that 100 intersections should be constructed in total.

The chosen sample size of 100 was based on securing a sufficiently large sample size for

developing CPM and conducting scenario tests, while maintaining a reasonable simulation and

analysis time.

The pedestrian element was determined to be omitted from the micro-simulation model. The

inclusion of pedestrians would have incurred significantly longer time in both model construction

and especially in simulation. Also as was mentioned earlier, pedestrian-pedestrian and pedestrian-

vehicle conflicts could not be identified from Paramics simulations; so the value added of

introducing pedestrians would only be their presence to influence yielding rules and additional

37

pedestrian demands on green time at actuated phases. Most importantly, calibration of pedestrian

movements would have been very different compared to that of vehicle movements. It would also

require more data and significantly more efforts. Without careful calibration of pedestrian

behaviours, the inclusion of pedestrians might not necessarily make the micro-simulation model

more realistic. Therefore, even though pedestrians would have an influence on general traffic, they

were not implemented in the micro-simulation models.

4.2 Data

From the pilot study, all necessary pieces of data for the construction, calibration, and validation

of the micro-simulation models were identified. These include (1) Crash data, (2) Traffic volume

data, (3) Transit service data, (4) Signal Timing Plans, (5) TSP operation data if applicable, and

(6) Intersection Geometry data.

4.2.1 Study Area and Period

From the pilot study, 100 micro-simulation networks were determined to be built and studied. The

City of Toronto has a total of approximately 1970 signalized intersections, of which 647 have both

crossing streets being arterial roads. These 647 arterial intersections are this study’s population of

interest. So 100 signalized intersections were drawn randomly from this population. The random

selection process avoided biasness and ensured representativeness of the population. A detailed

list of the selected 100 intersections can be found in Appendix A. The time period of analysis was

chosen to be between 2006 and 2010, inclusively. A GIS map is provided below (Figure 4-2) to

visually depict the geographical location and the observed crash frequency for each of the 100

selected signalized intersections.

38

Figure 4-2 Observed Number of Crashes [2006 – 2010] at the Signalized Intersections of the

Selected Sample

4.2.2 Crash Records

The crash data for the 100 intersections were extracted from the crash records administered by the

City of Toronto. The data were by default recorded in person-based format, such that each

observation represented one victim involved in a crash. For each victim involved in a crash, the

dataset included a number of descriptive factors such as time, date, impact type, vehicle type,

severity, individual’s condition etc. Data handling was needed to convert the data into crash-based,

such as each observation represents one crash. For each crash, the descriptive factors of every

victims involved were also attached to that crash. This allowed a crash to be identified by its impact

types or whether it involved certain vehicle types. Among the sample size of 100 selected

intersections, the observed crash frequency ranged from 21 to 368 crashes within the 5 years of

39

analysis window. Figure 4-3 below illustrates the observed crash frequency within the sample,

along with sample average and population average. Table 4-1 presents the statistics of the crash

frequency and peak hour volume for the selected sample.

Figure 4-3 Observed Crash Frequency [from 2006 to 2010] for the Selected Sample

Table 4-1 Descriptive Statistic for the Selected Sample

Number of Intersections Peak Hour Volume Crash Frequency

Min Max Mean Min Max Mean Total

Full Sample [100] 1707 7991 3520 21 368 113 11263

With Bus Service [87] 1716 7991 3647 21 368 117 10219

With Streetcar Service [20] 1707 3875 2514 33 163 89 1778

With Both Bus and Streetcar Services [16]

1716 3875 2491 33 163 88 1319

With Near-Sided Stops [95] 1707 7991 3567 21 368 113 10593

With Far-Sided Stops [43] 1777 6438 3751 28 295 127 5476

With Green Extension [20] 1707 5202 3006 39 234 98 1961

With Green Extension and Red Truncation [10]

1707 3675 2382 39 133 75 754

0

50

100

150

200

250

300

350

400

1 11 21 31 41 51 61 71 81 91

Observed Crash Frequency [from 2006 to 2010] for the Selected Sample

Observed Crash Frequency at each intersection Sample Average Population Average

40

4.2.3 Traffic Volume and Transit Service Information

Traffic volume data for the selected 100 intersections were also obtained from the City of Toronto.

For each intersection, the date for when the traffic volume data were originally collected had to

range within 2006 and 2010 in order to match the study period. If multiple traffic counts had been

done within the study period for an intersection, the earlier one was used for consistency. As a

result, a large number of the selected intersections had their traffic volume data from 2006.

Transit service data were obtained from the Toronto Transit Commission. The transit service data

provides many information needed for the simulation, such as transit routes and service headway.

The 2005.10.15 copy of the transit service data was used as this represented the transit service

condition at the beginning of the study period. This was to correspond with the dates of the traffic

volumes, as mentioned earlier that a large number of the traffic volume counts was from 2006.

One piece of transit operation information that was not in the transit service data was the dwell

time at each transit stop. Dwell times are closely tied to the boarding and alighting profiles at each

particular transit stops. Due to the absence of this information, the dwell time at each transit stop

was assumed to be at the Paramics’ default value.

4.2.4 Signal Timing Plans and Intersection Geometry

Signal timing plans for the selected 100 intersections were obtained also from the City of Toronto.

Each traffic signal’s traffic controller system could be one of TranSuite, MTSS, or SCOOT. Thus

the format of the signal timing plans was not uniform and could vary, depending on their traffic

controller system. Each signal timing plan provided detailed signal operation schematics including

phases, movement priorities, green/amber/red times of each phase, gap extension, etc. Additional,

if an intersection had TSP, the TSP operation schematic was also provided in the signal timing

plan. The TSP operation schematic included all essential parameters needed to program the TSP

41

including the type of TSP (green extension or red truncation or both), maximum

extension/truncation time, incremental time, decision time, etc. If multiple signal timing plans

existed within the study period, the earlier version was used for consistency with other data.

Lastly the intersection geometry was obtained from the City of Toronto’s web tool “Interactive

Toronto Map” (City of Toronto, 2015). This tool allowed for an accurate distance measurement

from the map’s aerial image, which had very high image resolution and clarity. The high image

quality also allowed for the confirmation of the transit stops with the transit service data. The tool

had many versions of aerial image taken from years 2005, 2009, 2011 and 2012. The version of

the aerial image used for this study was from the year 2005, again for the purpose of consistency

with other data.

42

5 Model Construction, Calibration and Validation

5.1 Model Construction

For each of the 100 randomly selected intersections under investigation, an isolated micro-

simulation network was constructed. Within each micro-simulation network, the intersection was

modelled to the incorporate all infrastructure elements for which data were available. The

geometric layout for each intersection was first constructed in the micro-simulation models in

accordance with the measurements obtained from the 2005’s aerial images. Then, vehicle and

transit demands were created, respectively, in accordance with the vehicle volume data and the

transit service data. Also in this step, the proportion of heavy vehicles was adjusted to match the

observed proportion in the traffic volume data. The operating characteristics of transit units,

including the length, width, maximum speed, maximum acceleration, and maximum deceleration,

were also adjusted in the micro-simulation models in accordance with their actual performance.

Lastly, the traffic signal in each micro-simulation models was signalized in accordance with the

signal timing plans. In the cases where actuated signal operation, variable signal operation, or TSP

were present in the signal timing plans, additional control files were written manually to constitute

their corresponding signal operations in the micro-simulation. Figure 5-1, Figure 5-2, and Figure

5-3 below illustrate three demonstrations of the constructed micro-simulation networks.

43

Figure 5-1 Demonstration Network 1 - Lake Shore Blvd W and Marine Parade Dr

Figure 5-2 Demonstration Network 2 - Finch Ave E and Tapscott Rd

44

Figure 5-3 Demonstration Network 3 - Lake Shore Blvd E and Lower Jarvis St

Despite the efforts of using the available data exhaustively to construct the micro-simulation

models to the best replication of reality, two pieces of information were unavailable and required

assumptions to be made. The first one was the unavailability of transit dwell time at transit stops.

As a result, the default dwell time in Paramics was used, which assumed an arrival and alighting

rate of 12 passengers per hour and 2 seconds of boarding/alighting per passenger for every transit

stops. So for example if a transit service had a frequency of two transits per hour, each transit

would have 6 boarding passengers, 6 alighting passengers, and 12 seconds of dwell time (as

Paramics by default assumed simultaneous boarding and alighting). This assumption was arguably

conservative for locations with denser transit rider profile. The second assumption was the

omission of on-street parking. This omission was not due to the unavailability of parking regulation

but rather insufficient information regarding how on-street parking was used. Fortunately, on-

street parking was uncommon in the studied intersections; even if it was available, in most cases

it was prohibited during the studied PM peak hour. Nonetheless, in a few of the studied networks,

on-street parking was available and unrestricted, which lead to one source of inaccuracy.

45

5.2 Model Calibration

As was introduced in Chapter 3, the objective of the model calibration was to iteratively seek an

optimal set of parameters within Paramics and SSAM such that the resulting CPM has the best

goodness-of-fit (i.e. most representative of the reality). However, as both simulation by Paramics

and analysis by SSAM were very time-consuming processes, only 40 networks, randomly chosen

from the 100 networks, were simulated for this purpose. Given that the sampling method of 40

networks was by random selection, the resulting goodness-of-fit was expected to be representative

of the 100 networks of population. In every set of tested parameters, 10 simulation runs, each with

a distinct seed number from 100 to 109, were conducted. Only 10 simulation runs were chosen,

again because of the time-consuming nature of the simulation process. Theoretically, more

simulations runs would have provided more precision on the statistical average of the tested

parameters. This chapter discusses the resulting performance of each set of tested parameters and

the set of parameters selected for model validation.

5.2.1 Driver Reaction Time (DRT) and Mean Headway Time (MHT)

The default value of both DRT and MHT are 1.0 seconds in Paramics. However it was suggested

that MHT is set to 0.85 to 0.90 for urban areas (Department of Transportation Wisconsin, 2012).

10 different sets of DRT and MHT are tested. The first 9 sets were exhaustive combinations of

DRT values of 0.71, 0.55, and 0.40 and MHT values of 1.00, 0.86, and 0.50. The 10th set tested an

extremely aggressive scenario, in which the DRT and MHT were at low values of 0.40 and 0.30

respectively. The tested DRT and MHT values were below the default values of 1.0 for the reason

that vehicle generally behave more aggressively near intersections. The coverage area of analysis

was chosen to be 80 meters upstream of the intersection in SSAM and other parameters were kept

46

at their default values. The resulting goodness-of-fits for each set of tested parameters are

presented below in Table 5-1.

Table 5-1 Calibration Results of Parameters DRT and MHT

Testing Number 1 2 3 4 5

Tested

Paramics

Parameters

DRT 0.710 0.550 0.400 0.710 0.550

MHT 0.860 0.860 0.860 0.500 0.500

CPM

Parameter

Estimate

λ 1.3902 1.4438 1.4425 1.2928 1.3657

Significance of λ 0.0283 0.0198 0.0200 0.0352 0.0282

β 0.5450 0.5404 0.5447 0.5463 0.5400

Significance of β 0.0001 0.0001 0.0001 0.0001 0.0001

Dispersion

parameter 0.1498 0.1488 0.1487 0.1416 0.1455

CPM

Goodness-

of-fit

Scaled Deviance 41.0241 41.0201 41.0255 41.0127 41.0210

SD/(n-p) 1.0796 1.0795 1.0796 1.0793 1.0795

Pearson χ2 42.7675 43.5894 44.0964 42.9209 43.7166

Pearson χ2/(n-p) 1.1255 1.1471 1.1604 1.1295 1.1504

Miaou’s R2 0.4025 0.4065 0.4069 0.4352 0.4196

Testing Number 6 7 8 9 10

Tested

Paramics

Parameters

DRT 0.400 0.710 0.550 0.400 0.400

MHT 0.500 1.000 1.000 1.000 0.300

CPM

Parameter

Estimate

λ 1.3724 1.5159 1.4225 1.5525 1.4827

Significance of λ 0.0293 0.0154 0.0224 0.0107 0.0194

β 0.5441 0.5314 0.5508 0.5322 0.5198

Significance of β 0.0001 0.0001 0.0001 0.0001 0.0001

Dispersion

parameter 0.1468 0.1533 0.1491 0.1507 0.1512

CPM

Goodness-

of-fit

Scaled Deviance 41.0096 41.0282 40.9994 41.0307 41.0251

SD/(n-p) 1.0792 1.0797 1.0789 1.0798 1.0796

Pearson χ2 44.6518 42.8810 43.4974 44.4789 43.2664

Pearson χ2/(n-p) 1.1750 1.1284 1.1447 1.1705 1.1386

Miaou’s R2 0.4144 0.3885 0.4053 0.3989 0.3969

GLM Form: 𝐶𝑟𝑎𝑠ℎ𝑒𝑠(𝑃𝑒𝑟5𝑌𝑒𝑎𝑟𝑠) = [𝐶𝑜𝑛𝑓𝑙𝑖𝑐𝑡𝑠(𝐻𝑜𝑢𝑟𝑙𝑦)]𝛽 ∗ 𝑒λ

47

In all 10 sets of tested parameters, the results were relatively similar. The estimate for the CPM

parameters β was significant at 95% confidence level for all 10 tests. In terms of goodness-of-fit,

the SD ranged from 40.9994 to 44.0964. Given that the degree of freedom for this CPM was 38,

the SD divided by the degree of freedom ranged from 1.0789 to 1.0798. The Pearson χ2 ranged

from 42.8810 to 44.6518. The Pearson χ2 divided by the degree of freedom ranged from 1.1255 to

1.1750. Both the SD/(n-p) and the Pearson χ2/(n-p) were close to 1.0 and thus the dispersion effect

was acceptable. The critical value of the χ2 distribution is 53.3835 for a degree of freedom of 38 at

the 95% confidence level. This critical value was not exceeded by any of the SD or Pearson χ2

from the 10 tested sets; therefore this result indicated that for every set of tested parameters, the

CPM was not rejected at 95% level of confidence.

The above calibration attempt suggested that all 10 sets of DRT and MHT were statistically

defensible for developing CPMs. In selecting the optimal set of DRT and MHT, the Miaou’s R2

was compared in addition to the SD and Pearson’s χ2. The baseline dispersion parameter, which

was obtained by having only one constant term as the CPM’s explanatory variable, was 0.2507.

The lowest dispersion parameter, and thus the highest Miaou’s R2 of 0.435, was observed in the

4th test. As a result, the 4th set of parameters with {DRT = 0.710 and MHT =0.500} was selected

as the most optimal amongst the 10 sets. Hence, this combination of DRT and MHT was used to

conduct subsequent calibration works for other model parameters. In addition, the parameters from

the first set {DRT = 0.710 and MHT =0.860} resulted in the lowest Pearson χ2, and were also kept

to conduct subsequent calibration works.

5.2.2 Coverage Area of Analysis, Time to Collision, and Travelling Speed

Since the size of each network being micro-simulated was approximately 120~150m upstream of

every approach, the absolute analysis area could only be as much upstream as this value.

48

Additionally approximately 20 meters at the start of every approaching link was set as the vehicle

release zone and could not be included in the coverage area of the conflict analysis. As a result,

the effective coverage area of each micro-simulation network could at most be 100m upstream of

every approach. The effects of using three different coverage areas in SSAM, respectively 65m,

80m, and 95m horizontally and vertically afar from the centre of the network, were investigated.

Each coverage area is denoted as { - L, L}, where L represents the distance from the centre of the

networks to each of the four directions.

Another tested parameter was the simultaneous use of the TTC and speed thresholds. As was

introduced in the Methodology section, these thresholds were applied simultaneously to filter out

the questionable or impractical conflicts. In SSAM, MaxS is a filtering parameter that defines the

greater of the two travelling speed of the two conflicting vehicles. Thus by setting a minimum

MaxS this ensured both conflicting vehicles would be travelling above this specified threshold.

This parameter was binary and so the minimum TTC and speed thresholds were either “used” or

“not used”. If the thresholds were “used”, any conflict that had a TTC of less than 0.05 seconds

and a MaxS of less than 16.1 km/hr would be filtered out by SSAM.

In tests 11 to 20, different combinations of the coverage area and the TTC and MaxS thresholds

were tested. The first five tests from 11 to 15 were based on the Paramics setting of DRT = 0.710

and MHT = 0.500 (previously test number 4). The next five tests from 16 to 20 were based on the

simulated result from the default Paramics setting of DRT = 0.710 and MHT = 0.860 (previously

test number 1). The resulting goodness-of-fit for these 10 new tests are shown in Table 5-2 below:

Table 5-2 Calibration Results of Parameters Coverage Area, TTC, and Speed

Testing Number 11 12 13 14 15

Tested

SSAM

Parameters

Coverage Area {-65,65} {-95,95} {-65,65} {-80,80} {-95,95}

TTC and MaxS

Thresholds Filter None None Used Used Used

49

CPM

Parameter

Estimate

λ 1.0600 1.4691 1.9908 2.0793 2.1660

Significance of λ 0.1238 0.0102 0.0005 0.0001 0.0001

β 0.5988 0.5099 0.5357 0.5034 0.4515

Significance of β 0.0001 0.0001 0.0001 0.0001 0.0001

Dispersion

parameter 0.1479 0.1392 0.1609 0.1560 0.1516

CPM

Goodness-

of-fit

Scaled Deviance 41.0301 41.0067 41.2265 41.2139 41.1967

SD/(n-p) 1.0797 1.0791 1.0849 1.0846 1.0841

Pearson χ2 43.7792 42.6048 42.0819 41.2839 40.8568

Pearson χ2/(n-p) 1.1521 1.1212 1.1074 1.0864 1.0752

Miaou’s R2 0.4101 0.4448 0.3582 0.3777 0.3953

Testing Number 16 17 18 19 20

Tested

SSAM

Parameters

Coverage Area {-65,65} {-95,95} {-65,65} {-80,80} {-95,95}

TTC and MaxS

Thresholds Filter None None Used Used Used

CPM

Parameter

Estimate

λ 1.0327 1.5678 2.0064 2.1202 2.1690

Significance of λ 0.1531 0.0074 0.0007 0.0001 0.0001

β 0.6218 0.5059 0.5530 0.5145 0.4958

Significance of β 0.0001 0.0001 0.0001 0.0001 0.0001

Dispersion

parameter 0.1537 0.1468 0.1656 0.1616 0.1580

CPM

Goodness-

of-fit

Scaled Deviance 41.0381 41.0113 41.2622 41.2335 41.2133

SD/(n-p) 1.0800 1.0792 1.0858 1.0851 1.0846

Pearson χ2 43.7692 42.4641 42.5535 41.3501 40.7824

Pearson χ2/(n-p) 1.1518 1.1175 1.1198 1.0882 1.0732

Miaou’s R2 0.3869 0.4144 0.3394 0.3554 0.3698

GLM Form: 𝐶𝑟𝑎𝑠ℎ𝑒𝑠(𝑃𝑒𝑟5𝑌𝑒𝑎𝑟𝑠) = [𝐶𝑜𝑛𝑓𝑙𝑖𝑐𝑡𝑠(𝐻𝑜𝑢𝑟𝑙𝑦)]𝛽 ∗ 𝑒λ

Again, for all calibration tests, the estimates for the CPM parameters β were significant at 95%

confidence level, suggesting strong correlation between simulated conflicts and observed crashes.

The goodness-of-fit performance had more variation among the 10 tests. The scaled deviance

ranged from 40.0067 to 41.2622. The scaled deviance divided by the degree of freedom ranged

from 1.0791 to 1.0858. The Pearson χ2 ranged from 40.7824 to 43.7792. The Pearson χ2 divided

by the degree of freedom ranged from 1.0732 to 1.1521. Both the SD/(n-p) and the Pearson χ2/(n-

50

p) were reasonably close to 1.0 and thus the dispersion effect was acceptable. The critical χ2 value

of 53.3835 was again not exceeded by any of the Pearson χ2 from the 10 tested sets; therefore this

result indicated that for every set of tested parameters, the CPM was not rejected at 95% level of

confidence.

One interesting finding was that the introduction of the TTC and MaxS simultaneous filtering did

not seem to improve the overall goodness-of-fit of the CPM. In all tests in which this filtering

criterion was applied, the resulting CPMs had higher dispersion parameter. Thus this filter was not

applied for any subsequent calibration and validation works. Another interesting finding was that

the upstream distance of 95m seemed to result in the lowest dispersion parameter; this was intuitive

since this enclosed most of the study areas that were initially constructed. Ultimately, the

parameter set from test 12 was chosen as the most representative of the reality, as this set had the

lowest dispersion parameter and the highest Miaou’s R2. The use of this parameter set led to the

final calibration effort, which was to test the significance of the inclusion of PHR.

5.2.3 Peak Hour Ratio (PHR)

With the inclusion of the PHR, which was the proportion of the peak hourly volume to the daily

volume, the CPM form would consist two explanatory variables. This resulted in one additional

CPM model parameter and therefore a reduction in the degree of freedom by one. The parameter

estimates and the goodness-of-fit measures are presented in Table 5-3 below:

Table 5-3 Calibration Results for the Inclusion of PHR

Testing Number 21

CPM

Parameter

Estimates

λ 0.1531

Significance of λ 0.9172

β 1 0.5486

Significance of β 1 0.0001

β2 0.6034

Significance of β2 0.3539

51

Dispersion parameter 0.1448

CPM

Goodness-

of-fit

Measures

Scaled Deviance 41.0058

SD/(n-p) 1.1083 Pearson χ2 43.3159

Pearson χ2/(n-p) 1.1707

Miaou’s R2 0.422

GLM Form: 𝐶𝑟𝑎𝑠ℎ𝑒𝑠(𝑃𝑒𝑟5𝑌𝑒𝑎𝑟𝑠) = [𝐶𝑜𝑛𝑓𝑙𝑖𝑐𝑡𝑠(𝐻𝑜𝑢𝑟𝑙𝑦)]𝛽1 ∗ [𝑃𝐻𝑅]𝛽2 ∗ 𝑒λ

Interestingly, with an estimated p-value of only 0.3539, the coefficient of the explanatory variable

PHR showed no statistical significance. The poor statistical strength of the PHR indicated that the

introduction of this explanatory variable did not improve the CPM, despite having a comparable

goodness-of-fit with the earlier CPMs. As a result, this estimated CPM form was inferior to the

CPM form from test 12. The final parameter set was therefore chosen as the ones used in test 12,

with DRT = 0.710, MHT = 0.500, and Coverage Area = {-95, 95}. All other parameters were kept

at their default settings in both Paramics and SSAM.

5.3 Model Validation

Upon calibrating the model and finding the parameter set that produced the optimal replication of

reality, model validation was executed. Here, all 100 networks were simulated in Paramics and

analyzed in SSAM using the parameter set determined from model calibration. The objective of

model validation was to confirm that the optimal parameter set determined from model calibration

remains suitable and statistically defensible when applied to the full study size. Again, each

network was simulated 10 times with seeds number from 100 to 109. In addition to the validation

of CPM of simulated conflicts with observed crashes of all impact types, CPMs were also

developed based on impacts type. Namely, CPMs for the impact types of angle, rear-end, and side-

swipe were also developed and validated. Additionally, simulated conflicts that involved transit-

vehicles were regressed against the observed crashes that involved transit vehicles. Again, the

52

filtering of transit-involved conflicts was not an inherent feature and was achieved manually by

studying the analyzed conflicts in Excel. The simulated angle, rear-end, and side-swipe, and

transit-involved conflicts were respectively validated against their crash counterparts, i.e. angle,

rear-end, side-swipe, and transit-involved crashes. The result of the validation is shown by Table

5-4 below:

Table 5-4 Model Validation Results

Model Parameters

All

Crash

Types

Angle

Crashes

Rear-

End

Crashes

Side-

Swipe

Crashes

Transit-

Involved

Crashes

Parameter

Estimate

λ 1.1789 1.0503 -0.5010 0.5765 -0.2476

Significance of λ 0.0031 0.0009 0.2310 0.1071 0.5075

β 0.5722 0.6104 0.7175 0.5647 0.5482

Significance of β 0.0001 0.0001 0.0001 0.0001 0.0001

Dispersion parameter 0.1931 0.1874 0.2660 0.3338 0.4950

Goodness-

of-Fit

Scaled Deviance 103.2580 102.3342 104.6372 103.6942 106.4388

SD/(n-p) 1.0537 1.0442 1.0677 1.0581 1.1323

Pearson χ2 109.2039 106.9563 122.5254 119.5977 103.4132

Pearson χ2 / (n-p) 1.1143 1.0914 1.2503 1.2204 1.1001

Miaou’s R2 0.4285 0.4144 0.5015 0.3014 0.2908

GLM Form: 𝐶𝑟𝑎𝑠ℎ𝑒𝑠(𝑃𝑒𝑟5𝑌𝑒𝑎𝑟𝑠) = [𝐶𝑜𝑛𝑓𝑙𝑖𝑐𝑡𝑠(𝐻𝑜𝑢𝑟𝑙𝑦)]𝛽 ∗ 𝑒λ

As depicted by Table 5-4, the parameter estimates for each impact type differed among each other.

For the CPM for all crashes, both λ and β were statistically significant. It is worth noting that the

value of λ and β were similar to those estimated from model calibration. The SD and Pearson’s χ2

statistics were respectively 103.2580 and 109.2039, which did not exceed the critical χ2 value of

122.1077 for 98 degree of freedoms. The Miaou’s R2 was 0.4285, indicating a slight reduction

relative to the Miaou’s R2 computed from model calibration.

53

The CPMs for angle, rear-end, and side-swipe impact types differed slightly with the CPM for all

crashes. However, in all three impact-type-specific CPMs, their respective β were statistically

significant, indicating that simulated conflicts were strongly correlated with crashes. It is also

worth noting that the Pearson χ2 for rear-end and side-swipe models were relatively high and

possibly indicated over-dispersion. Nevertheless since their SDs were well within the acceptable

range, the CPMs were still used for subsequent analyses despite having potential over-dispersion.

When comparing the Miaou’s R2, rear-end GLM had the highest performance, indicating the

highest degree of variance explained within the model. In contrast, side-swipe GLM had the lowest

degree of variance explained within the model, which implied a less predictive nature of side-

swipe crashes using micro-simulation.

Also as shown by Table 5-4, the CPM for transit-involved crashes was adequately fitted. The

estimate for the β parameters was similar to that in the all-crash-types model. In the transit-

involved CPM, the SD and Pearson χ2 were also within the acceptable threshold of the critical χ2

value. When these two goodness-of-fit measures were scaled by the degree of freedom, the

resulting SD/(n-p) and Pearson χ2/(n-p) aligned closely to 1.0, indicating appropriate dispersion.

The Miaou’s R2 was however the lowest among all the CPMs presented in this section. This

indicated that a great proportion of the variance in the observed transit-involved crashes was yet

unexplained by the CPM, and furthermore implied that transit-involved crashes could be more

difficult to predict relative to general vehicle crashes. This difficulty in prediction may be due to

the significantly lower values in both the simulated transit-involved conflicts and observed transit-

involved crashes, when compared to their general vehicle counterparts. Nevertheless, given that

the coefficient estimate was strongly statistically significant and that both the SD and Pearson χ2

54

goodness-of-fit measures were acceptable, the transit-involved CPM was used for subsequent

analyses.

In addition to the three goodness-of-fit measures presented above, the fourth measure, the CURE

analysis was also conducted. In the CURE analysis, the cumulative scaled residuals for each of the

five CPMs presented earlier were plotted. The two standard deviation upper and lower bands of

each CPM’s corresponding random walk phenomenon, were also plotted to assist the graphical

illustration of the appropriateness of the modelling structure. The CURE analysis for the five

CPMs of all-impact-types, angle, rear-end, side-swipe, and transit-involved crashes are shown by

Figure 5-4 through Figure 5-8 below:

Figure 5-4 CURE Plot - All Impact Type Crashes

-12.0

-10.0

-8.0

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

0 200 400 600 800 1000 1200 1400 1600

Cu

mu

lati

ve S

cale

d R

esid

ual

s

Simulated Conflicts - All Impact Types

CURE Plot - All Impact Type Crashes

Cumulative Scaled Residuals

Upper 2σ Band

Lower 2σ Band

55

Figure 5-5 CURE Plot - Angle Crashes

Figure 5-6 CURE Plot - Rear-End Crashes

-12.0

-10.0

-8.0

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

0 20 40 60 80 100 120 140 160 180 200

Cu

mu

lati

ve S

cale

d R

esid

ual

s

Simulated Conflicts - Angle

CURE Plot - Angle Crashes

Cumulative Scaled Residuals

Upper 2σ Band

Lower 2σ Band

-12.0

-10.0

-8.0

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

0 200 400 600 800 1000 1200 1400

Cu

mu

lati

ve S

cale

d R

esid

ual

s

Simulated Conflicts - Rear-End

CURE Plot - Rear-End Crashes

Cumulative Scaled Residuals

Upper 2σ Band

Lower 2σ Band

56

Figure 5-7 CURE Plot - Side-Swipe Crashes

Figure 5-8 CURE Plot - Transit-Involved Crashes

The CURE analysis revealed that the cumulative scaled residuals fell within the acceptable two

standard deviations envelope of the random walk phenomenon for all five CPMs. For the all-crash-

-12.0

-10.0

-8.0

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

0 20 40 60 80 100 120 140 160 180

Cu

mu

lati

ve S

cale

d R

esid

ual

s

Simulated Conflicts - Side-Swipe

CURE Plot - Side-Swipe Crashes

Cumulative Scaled Residuals

Upper 2σ Band

Lower 2σ Band

-12.0

-10.0

-8.0

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0

Cu

mu

lati

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cale

d R

esid

ual

s

Simulated Conflicts - Transit-Invovled

CURE Plot - Transit-Involved Crashes

Cumulative Scaled Residuals

Upper 2σ Band

Lower 2σ Band

57

types, angle, rear-end, and side-swipe CPMs, their corresponding cumulative scaled residuals plot

generally oscillated about the x-axis. Such oscillating pattern was ideal as it suggested no

systematic overestimation or underestimation of the model structure. For the transit-involved

CPM, the cumulative scaled residuals plot appeared to have continuously negative slope when the

explanatory variable was low, followed by continuously positive slope when the explanatory

variable was high. This upward concaving pattern suggested that the CPM may systematically

overestimate crashes when the simulated conflicts were low and conversely may underestimate

crashes when the simulated conflicts were high. This pattern also demonstrated that transit-

involved crashes could be more difficult to predict than general traffic crashes.

Based on the above model validation results, all five CPMs were considered acceptable to be used

for the purpose of crash prediction. This was based on the reasons that (1) the coefficients for the

simulated conflicts were highly statistically significant, and (2) the four goodness-of-fit measures,

respectively the SD, Pearson χ2, Miaou’s R2, and CURE analysis demonstrated appropriate fitting

for each of the CPMs. The equations for the five studied CPMs are therefore:

𝐴𝑙𝑙𝐶𝑟𝑎𝑠ℎ𝑒𝑠(𝑃𝑒𝑟5𝑌𝑒𝑎𝑟𝑠) = [𝐴𝑙𝑙𝐶𝑜𝑛𝑓𝑙𝑖𝑐𝑡𝑠(𝐻𝑜𝑢𝑟𝑙𝑦)]0.5722 ∗ 𝑒1.1789

Equation 9

𝐴𝑛𝑔𝑙𝑒𝐶𝑟𝑎𝑠ℎ𝑒𝑠(𝑃𝑒𝑟5𝑌𝑒𝑎𝑟𝑠) = [𝐴𝑛𝑔𝑙𝑒𝐶𝑜𝑛𝑓𝑙𝑖𝑐𝑡𝑠(𝐻𝑜𝑢𝑟𝑙𝑦)]0.6104 ∗ 𝑒1.0503

Equation 10

𝑅𝑒𝑎𝑟𝐸𝑛𝑑𝐶𝑟𝑎𝑠ℎ𝑒𝑠(𝑃𝑒𝑟5𝑌𝑒𝑎𝑟𝑠) = [𝑅𝑒𝑎𝑟𝐸𝑛𝑑𝐶𝑜𝑛𝑓𝑙𝑖𝑐𝑡𝑠(𝐻𝑜𝑢𝑟𝑙𝑦)]0.7175 ∗ 𝑒−0.5010

Equation 11

𝑆𝑖𝑑𝑒𝑆𝑤𝑖𝑝𝑒𝐶𝑟𝑎𝑠ℎ𝑒𝑠(𝑃𝑒𝑟5𝑌𝑒𝑎𝑟𝑠) = [𝑆𝑖𝑑𝑒𝑆𝑤𝑖𝑝𝑒𝐶𝑜𝑛𝑓𝑙𝑖𝑐𝑡𝑠(𝐻𝑜𝑢𝑟𝑙𝑦)]0.5647 ∗ 𝑒0.5765

Equation 12

𝑇𝑟𝑎𝑛𝑠𝑖𝑡 − 𝑖𝑛𝑣𝑜𝑙𝑣𝑒𝑑𝐶𝑟𝑎𝑠ℎ𝑒𝑠(𝑃𝑒𝑟5𝑌𝑒𝑎𝑟𝑠) = [𝑇𝑟𝑎𝑛𝑠𝑖𝑡 − 𝑖𝑛𝑣𝑜𝑙𝑣𝑒𝑑𝐶𝑜𝑛𝑓𝑙𝑖𝑐𝑡𝑠(𝐻𝑜𝑢𝑟𝑙𝑦)]0.5482 ∗ 𝑒−0.2476

Equation 13

58

As a result of the model validation efforts, Equations 9 to 13 were used as the basis for assessing

safety performance for the following scenario tests.

59

6 Scenario Tests

Following model validation, the established SPFs were applied to assess the safety impacts of

various hypothetical transit infrastructure modifications. As was introduced in the Methodology

section, nine scenarios were designed, with each scenario testing the effect of one individual

infrastructure change. This chapter discusses the results of the scenario tests and the implications

from these findings. For each scenario, the total predicted crashes in the status quo condition, the

absolute change in total predicted crashes relative to status quo, and the percentage change in total

predicted crashes relative to status quo, are presented. Since a reduction in predicted crashes is in

the more favourable direction, a reduction in predicted crashes is coloured in green. Conversely

an increase in predicted crashes is coloured in red.

Each network was simulated 20 times, respectively having seed numbers from 100 to 119. The

networks from the status quo case were also simulated again, using the same 20 seed numbers.

Note that even though the same seed numbers were used for both before and after the infrastructure

modification, this practice did not discourage the number of simulation runs. In fact, the number

of runs was increased from 10, which was used in model calibration and validation, to 20 runs.

This increase in simulation runs was to allow for a more exhaustive representation of how vehicles

arrive at the intersection in reality, which in turn would make the comparison more justifiable.

6.1 Effects of TSP

Out of the 100 constructed micro-simulation networks, 20 intersections were serviced with TSP.

All 20 of these TSP-serviced intersections had green extension while only 10 had red truncation.

Due to the relatively small subgroup size, all qualified intersections were selected for investigation.

The results of the three scenarios for investigating the effects of TSP are presented in the Table

6-1 below:

60

Table 6-1 Predicted Results of TSP Treatments

Scenarios 1 2 3

Transit Infrastructure Treatments

Removal

of Green

Extension

Removal

of Red

Truncation

Removal

of TSP

Number of Networks Studied 20 10 20

Total Predicted Crashes in Status Quo

All Impact

Types

2002.3 885.0 2002.3

Change in Total Predicted Crashes

relative to Status Quo -22.75 -14.11 -23.93

Percentage Change in Total Predicted

Crashes relative to Status Quo -1.14% -1.59% -1.20%

Total Predicted Crashes in Status Quo

Angle

Impact

Types

753.1 343.5 753.1

Change in Total Predicted Crashes

relative to Status Quo 3.62 -9.77 -1.85

Percentage Change in Total Predicted

Crashes relative to Status Quo 0.48% -2.85% -0.25%

Total Predicted Crashes in Status Quo

Rear-End

Impact

Types

722.0 309.5 722.0

Change in Total Predicted Crashes

relative to Status Quo -13.62 -5.39 -13.76

Percentage Change in Total Predicted

Crashes relative to Status Quo -1.89% -1.74% -1.91%

Total Predicted Crashes in Status Quo Side-

Swipe

Impact

Types

292.5 124.5 292.5

Change in Total Predicted Crashes

relative to Status Quo -5.75 -2.65 -2.46

Percentage Change in Total Predicted

Crashes relative to Status Quo -1.97% -2.13% -0.84%

Total Predicted Crashes in Status Quo All

Transit-

Involved

Crashes

93.8 41.2 93.8

Change in Total Predicted Crashes

relative to Status Quo 0.79 -0.04 1.51

Percentage Change in Total Predicted

Crashes relative to Status Quo 0.84% -0.09% 1.61%

The resulting predicted crashes suggested that the status quo case, which had TSP operation, was

more inferior in terms of safety performance. In scenario 1, in which green extensions were

removed amongst all the studied micros-simulation networks, the overall predicted crashes were

decreased by 1.14%. Except for the angle type crashes which were increased by 0.48%, rear-end

61

and side-swipe crashes were reduced by 1.89% and 1.97% respectively. The predicted crashes that

involved transit vehicles were increased by 0.84%, which is expected given that the green

extension no longer facilitated transit movement in this scenario. In scenario 2, in which red

truncations were removed, the predicted crashes for all impact types were reduced. Surprisingly,

the transit-involved crashes were predicted to have a very minor reduction of 0.09%, suggesting

marginal improvement for transit safety. Thus this suggested that red truncation could be inferior

to green extension since it did not improve safety performance in any of the studied aspects. In

scenario 3, the predicted crash were predicted to decrease by 0.25%, 1.91%, and 0.84%

respectively for angle, rear-end, and side-swipe crash types; the total crashes were predicted to

decrease by 1.20%. However, the transit-involved crashes were predicted to increase by 1.61%,

which was similar to scenario 1. Again, this increase in transit-involved crashes was intuitive since

TSP is no longer facilitating transit movements in scenario 3.

The overall negative association between TSP and safety performance was also observed by

Shahla et al. (2009). However in that literature, a conventional CPM was developed without the

use of conflicts from micro-simulation, but with traffic volumes and other variables, including a

TSP indicator variable that suggested that the implementation of TSP technology could increase

total crashes by as much as 28.4% at transit-serviced intersections.

However, it is worth mentioning that, although the overall change in predicted crashes was

negative, the individual changes for every networks greatly differed amongst themselves (see

Appendix B for individual changes). In fact, some particular studied intersections were

demonstrated to have increased crash frequency had the TSP schemes been removed. This was

likely due to the fact that not all TSP schemes were exactly identical amongst the studied

intersections, although some resemble similar TSP parameters. One of the key TSP parameters

62

that usually differed amongst intersections was the maximum extension/truncation time. In

addition, the direction and the transit type for which the TSP was servicing, were also influential

towards the individual safety performance. Thus, this motivated the endeavour to investigate

further into the individual characteristics of each studied intersection and if any particular pattern

could be observed. The individual characteristics of these studied intersections are also provided

in Appendix B. Upon investigation, it appeared that most of the characteristics did not demonstrate

an observable pattern in relation to the change in predicted crashes. However it was observed that,

the transit type for which the TSP is servicing, has a noteworthy influence. Taking scenario 3 as a

demonstration, the micro-simulated networks appear to respond more negatively when the transit

routes serviced by the TSP are streetcars. More specifically, when a TSP that was servicing

streetcars is removed, there was more reduction in predicted crashes relative to when a TSP that

was servicing buses was removed. Figure 6-1 below depicts the composition of the change in

predicted crashes in scenario 3:

63

Figure 6-1 Absolute Change in Predicted Crash frequency as a Result of Removing TSP

[by Transit Types]

Figure 6-1 suggests the TSPs that service streetcars and buses had different safety impacts. For

convenience, the networks that had their TSPs servicing streetcars are referred to as the “TSP-

streetcar” networks in this chapter and likewise the networks that had their TSPs servicing buses

are referred to as the “TSP-bus” networks. From Figure 6-2, it can be seen that the majority of the

changes in predicted crashes were from the TSP-streetcar networks. This phenomenon is also

observable when each impact type was investigated individually. In fact, for angle and side-swipe

impact types, the predicted crashes were even predicted to increase when the TSPs that service

buses are removed.

Out of the 20 investigated networks which had a total of 2002 predicted crashes, 12 networks had

their TSPs service streetcars; these 12 networks contributed to a total of 1021 predicted crashes,

which were approximately half of the total predicted crashes among the 20 networks. Using the

-30

-25

-20

-15

-10

-5

0

5

All Crashes Angle Rear-End Side-Swipe Transit-Involved

Change in Predicted Crash Frequency as a result of Removing TSP[by Transit Types]

Networks in which TSPs Service Streetcars Networks in which TSPs Service Buses

64

absolute changes shown in Figure 6-1 and the total predicted crashes of the respective two factions

of networks, the percentage changes are presented in Figure 6-2 below:

Figure 6-2 Percentage Change in Predicted Crash Frequency as a Result of Removing TSP

[by Transit Types]

Figure 6-2 confirms that the total predicted crashes were experiencing more reduction for the TSP-

streetcar networks relative to the TSP-bus networks. Again, the breakdown by impact types

suggests that the changes in predicted crash for the TSP-streetcar networks were one-sided in the

negative direction; however for the TSP-bus networks, the change in predicted crash was more

volatile. Again, the result suggests that if the TSPs in the TSP-bus networks were removed, the

angle and side-swipe crashes were predicted to increase. The transit-involved crashes for both

TSP-streetcar and TSP-bus networks were expected to increase as a result of the TSP removal,

which is intuitive since the transits no longer benefited from the signal priority.

The above investigation suggests that existing TSPs are less compatible with streetcars than with

buses. The removal of TSPs that service streetcars would result in less crashes and thus in the safer

-4.0%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

All Crashes Angle Rear-End Side-Swipe Transit-Involved

Perchange Change in Predicted Crash Frequency as a result of Removing TSP[by Transit Types]

Networks in which TSPs Service Streetcars Networks in which TSPs Service Buses

65

direction. However the removal of TSPs that service buses would lead to more mixed results. The

exact reason of why TSPs were less compatible with streetcars than with buses remains unclear.

One hypothesis is that since general traffics are not allowed to pass streetcars when the streetcars

are stopped, the benefits from TSP to general traffics in the streetcars’ direction is inherently

diminished.

To summarize the findings from this subchapter in which the safety impacts of TSP were studied,

the above investigation implies that:

The existing TSP, whether it was in the form of green extension individually, red

truncation individually, or combined green extension and red truncation, did not contribute

to a safer environment for general traffics, albeit contributing to safer performance for

transit vehicles. In fact, the predicted crashes were expect to be reduced by approximately

2% had the TSP schemes be removed.

Solely from the safety perspective, the existing TSP was less compatible when it serviced

streetcars than when it serviced buses. When TSPs that serviced streetcars were removed,

a reduction in predicted crashes are expected for every impact types. In contrast, when

TSPs that serviced buses were removed, the resulting changes in predicted crashes were

more volatile and situational. In short, the existing TSPs that service streetcars were less

desirable for the safety of general traffics than those that service buses.

6.2 Effect of Transit Stop Positioning

Most of the micro-simulation networks had at least one pair of transit stops. This was not surprising

since the studied population was arterial roadway intersections. As a result, a great majority of the

micro-simulation networks qualified for scenarios 4 and 6, which investigated transit stop

66

positioning along the major direction at intersections without TSP service. 80 out of the 100 micro-

simulation networks did not have TSP and 72 out of these 80 intersections had near-sided stops

along their major direction; then 35 intersections were randomly selected from these 72 qualified

intersections to be investigated for scenario 4 and 6. For the network selection in scenario 5, 39

out of the 80 non-TSP serviced intersections had near-sided stops along their minor direction and

25 intersections out of these 39 were randomly selected to be investigated. For these three

scenarios, the number of studied intersections was smaller than the number of actually qualified

intersections in order to sustain a reasonable simulation and analysis time. For scenario 7, only 15

out of the 20 TSP-servicing intersections had near-sided stops along its TSP direction; thus all 15

networks were investigated in this scenario. The results of the four scenarios that study the effect

of transit stop positioning were presented by Table 6-2 below:

Table 6-2 Predicted Results of Transit Stop Positioning Treatments

Scenarios 4 5 6 7

Transit Infrastructure Treatments

Near-sided

to Far-

sided

[Major

Direction

Non-TSP]

Near-sided

to Far-

sided

[Minor

Direction

Non-TSP]

Near-sided

to No Stop

[Major

Direction

Non-TSP]

Near-

sided to

Far-sided

[TSP

servicing

Direction]

Number of Networks Studied 35 25 35 15

Total Predicted Crashes in

Status Quo

All

Impact

Types

4664.7 3201.7 4664.7 2002.3

Change in Total Predicted

Crashes relative to Status

Quo

97.89 77.39 -92.11 0.32

Percentage Change in Total

Predicted Crashes relative to

Status Quo

2.10% 2.42 % -1.97% 0.02%

Total Predicted Crashes in

Status Quo Angle

Impact

Types

1524.1 1048.0 1524.1 753.1

Change in Total Predicted

Crashes relative to Status

Quo

71.72 54.37 -25.03 13.24

67

Percentage Change in Total

Predicted Crashes relative to

Status Quo

4.71% 5.19% -1.64% 2.59%

Total Predicted Crashes in

Status Quo

Rear-

End

Impact

Types

1883.7 1286.3 1883.7 722.0

Change in Total Predicted

Crashes relative to Status

Quo

35.77 32.16 -46.99 -4.82

Percentage Change in Total

Predicted Crashes relative to

Status Quo

1.90% 2.50% -2.49% -1.05%

Total Predicted Crashes in

Status Quo

Side-

Swipe

Impact

Types

656.4 447.0 656.4 292.5

Change in Total Predicted

Crashes relative to Status

Quo

18.81 10.59 -13.64 2.76

Percentage Change in Total

Predicted Crashes relative to

Status Quo

2.87% 2.37% -2.08% 1.43%

Total Predicted Crashes in

Status Quo

All

Transit-

Involved

Crashes

170.2 119.3 170.2 93.8

Change in Total Predicted

Crashes relative to Status

Quo

25.66 9.59 -30.92 -1.96

Percentage Change in Total

Predicted Crashes relative to

Status Quo

15.08% 8.03% -18.17% -3.07%

The resulting predicted crashes suggest that near-sided stops were inferior to far-sided stops in

terms of safety performance. In both scenario 4 and 5, the predicted crashes were higher than status

quo, suggesting that far-sided stops would be less safe regardless of whether the transit was along

the direction of heavier or lighter traffic. It can be seen that the results from scenario 4 and 5 are

similar, both suggesting approximately 2% increase in predicted crashes had the near-side stops

be relocated to far-sided. In addition, not only was the total predicted crashes expected to increase,

it was noticed that the individual changes in predicted crashes among the studied networks were

also one-sided in the increasing direction. 31 out of 35 studied networks in scenario 4 and 20 out

of 25 studied networks in scenario 5 suggested increase in predicted crashes. This trend was also

68

observable in each of the angle, rear-end, and side-swipe impact type studies. Furthermore, the

transit-involved crashes were also expected to increase, suggesting that far-sided stops were

neither safer for general traffics nor for transits, when compared to near-sided stops. This

worsening in safety performance was likely due to the possibility that when traffic queue forms

upstream of a stopped transit at a far-sided stop, the queue may overextend into the perimeter of

the intersection itself, which is inherently a more volatile region of traffic conflicts; however at

near-sided stops, when traffic queue forms upstream of a stopped transit, they would not risk

queuing into the intersection.

In scenario 6 in which transit stops were removed entirely along the major direction, the predicted

crashes unsurprisingly decreased by approximately 2%. Again, this trend is also observable when

dissected by impact-types. The transit-involved crashes experienced a greater reduction of 18.2%,

which was intuitive since transits flows would be steadier without the stops. It is worth mentioning

that this scenario only suggests that the total predicted crashes would decrease at intersection-level,

which may not represent the global effect. It is likely that midblock crashes could have increased

as a result of the relocation of transit stops from intersection to midblock. However, the analysis

of midblock crashes would be beyond the scope of this study.

The result from scenario 7 was interesting and differentiated itself with the earlier scenarios. It can

be seen that in this scenario, the change in predicted crashes was only an increase of 0.02%,

suggesting neither large improvement nor deterioration. This differed from the 2.10% and 2.42%

that were observed respectively from scenario 4 and 5. In addition, despite having only 0.02% total

increase in this scenario, the changes in predicted crashes at each individual studied networks were

actually more volatile. Some networks were shown to have decreased predicted crashes as a result

of the relocation of transit stops while others were shown to have increased predicted crashes.

69

After dissecting the predicted crashes by their impact types, the rear-end crashes were predicted to

decrease, while the angle and side-swipe crashes were predicted to increase. The transit-involved

crashes were interestingly displaying a decrease, implying that far-sided stops were slightly safer

for transits in the presence of TSP.

As a summary, the above investigation suggests that:

When near-sided stops were relocated to far-sided at intersections without TSP, the

resulting safety performance deteriorated, regardless of whether the transit line was in the

direction of heavier or lighter volume

When near-sided stops were relocated to mid-block, the resulting safety performance

improved at the intersection-level; however the global effect was unexplored

When near-sided stops were relocated to far-sided along the TSP-servicing direction, the

resulting safety performance was uncertain and volatile for each individual intersection;

however the overall safety performance was expected to be neither an improvement nor a

deterioration

6.3 Effect of Transit Type

19 of the 100 micro-simulated intersections had at least one transit route serviced by streetcars.

Then, 15 out of these 20 had their streetcars moving in the through direction and in mixed right-

of-way. Therefore all 15 networks were investigated in scenario 8 and 9, in which these streetcars

were replaced with buses. In scenario 8, the replacement ratio was 1:1 such that streetcars were

replaced by buses following the same service schedule. In scenario 9, the original service headway

of the streetcar was shortened by a factor of m and then used as the new service headway of the

bus, to ensure equal transit capacity relative to status quo. The factor m was obtained from the

70

simple inversion of the ratio of the streetcar’s peak hour passenger counts to the bus’s peak hour

passenger counts, which are published by the Toronto Transit Commissions (Toronto Transit

Commission, 2015). This factor m turned out to be 51/74, or 0.69. Thus if the original service

headway for the streetcar was for example 10 minutes, the new service headway for the

hypothetical buses would be 6 minutes 54 seconds. The results for this exercise is shown by Table

6-3 below:

Table 6-3 Predicted Results of Transit Type Treatments

Scenarios 8 9

Transit Infrastructure Treatment Streetcars to Buses

(1:1 replacement)

Streetcars to Buses

(1:m replacement for

same operation

capacity)

Number of Networks Studied 15 15

Total Predicted Crashes in

Status Quo

All Impact

Types

1135.5 1135.5

Change in Total Predicted

Crashes relative to Status

Quo

19.04 36.55

Percentage Change in Total

Predicted Crashes relative to

Status Quo

1.68% 3.22%

Total Predicted Crashes in

Status Quo

Angle

Impact

Types

432.7 432.7

Change in Total Predicted

Crashes relative to Status

Quo

10.97 15.66

Percentage Change in Total

Predicted Crashes relative to

Status Quo

2.54% 3.62%

Total Predicted Crashes in

Status Quo

Rear-End

Impact

Types

358.0 358.0

Change in Total Predicted

Crashes relative to Status

Quo

6.20 15.27

Percentage Change in Total

Predicted Crashes relative to

Status Quo

1.73% 4.26%

71

Total Predicted Crashes in

Status Quo

Side-

Swipe

Impact

Types

190.3 190.3

Change in Total Predicted

Crashes relative to Status

Quo

3.77 5.34

Percentage Change in Total

Predicted Crashes relative to

Status Quo

1.98% 2.80%

Total Predicted Crashes in

Status Quo

All

Transit-

Involved

Crashes

60.0 60.0

Change in Total Predicted

Crashes relative to Status

Quo

14.75 22.45

Percentage Change in Total

Predicted Crashes relative to

Status Quo

24.57% 37.39%

The above results illustrate that by replacing the studied streetcars with buses, the predicted crashes

actually increased. This trend is observable for each of the three impact types and is more obvious

for transit-involved crashes. The overall increase was approximately 1.7% in scenario 8 and 3.2%

in scenario 9. The transit-involved crashes were predicted to increase by as much as 24.6% and

37.4% respectively in scenario 8 and 9. This implies that along the existing streetcar routes,

through-moving streetcars would not be made more safe had them been replaced by through-

moving buses. This might be because the existing infrastructures along the streetcar routes, such

as left-turn restrictions or the absence of left-turn signal, had already made the intersections more

favourable for streetcar operation. Thus the replacement of existing streetcars to buses would not

bring forth safety benefits.

It is worth emphasizing that the above results are unidirectional and do not provide any conclusion

for the other direction of replacement, i.e. if buses were being replaced by streetcars. Buses can

operate in a more flexible environment where the intersections have left-turn lanes and left-turn

signals, which would first have to be removed to even allow for a reasonable streetcar operation.

72

In addition, the above investigation was only comparing through-moving streetcars with through-

moving buses. Although rare, in reality streetcars occasionally make left or right turns along their

routes of operation, whether they are turning at intersection or turning into their terminal. Thus the

safety aspect of the turning movements of streetcars and buses remains unexplored. As a result, if

a streetcar make many turns along its servicing route, the strength of the above finding would

indisputably be hindered.

73

7 Conclusion and Future Work

Through the combinative use of micro-simulation and CPM, the safety performance at arterial

intersections have been investigated in this study. The methodology presented in this study

suggests a flexible approach of modelling not only the intersection-level safety performance, but

also the transit operation aspect and its influence on general traffics. Within the presented

methodology, the data acquisition, model calibration, and model validation were the key steps

undertaken to ensure the technical validity of the following analyses. From the scenario tests, the

safety impacts of three facets of transit operations, namely the TSP, positioning of transit stop, and

transit type were investigated. The results clearly suggest that transit operations have a certain

level of influence towards the overall safety performance at intersection-level. More specifically,

the results imply that:

1. The existing TSPs do not improve the safety performance, especially when the TSPs are

servicing streetcar routes. By removing the TSPs, crash frequency is expected to be

reduced slightly.

2. Along a direction that is not serviced by TSP, the existing near-sided stops have better

safety performance than their corresponding hypothetically placed far-sided stops.

However along a direction that is serviced by TSP, the overall safety performance is

expected to be similar for near-sided and far-sides stops, while the individual performance

is more volatile.

3. The existing streetcars have better safety performance than if they were to be replaced

with buses, possibly due to the fact that existing lane usages are already favouring streetcar

operation.

74

Despite the practical implications of the above findings, there were limitations within the applied

methodology that may somewhat challenge the strength of the findings. The first limitation was

due to the absence of two pieces of data, which were the transit dwell time and the on-street parking

usage. This absence of these data led to the assumption of transit dwell time and the omission of

on-street parking during the construction of the micro-simulation model. Had these data been

available, the micro-simulation models could be constructed to incorporate these elements and

arguably be more realistic. Nonetheless, careful considerations must be given to ensure these data

would fit the study period and the duration of the micro-simulation.

The second limitation was the exclusion of the pedestrian element in the micro-simulation, due to

the inability to identify pedestrian-vehicle conflicts. If pedestrians had been introduced, the

yielding priorities for turning movement would be significantly more complex due to pedestrian

crossing. As a result, pedestrian movements will generally act as moving barricades to the flow of

traffic, which at the moment have unclear impacts in safety performance. The inclusion of the

pedestrian elements, or even more preferably the integration of pedestrians as transit riders, are

definitely a worthwhile direction for future researches.

The last limitation is the separation of the investigated crash frequency and the less discussed crash

severity. Again, in reality, the overall safety performance is conceptually an end product of a

mixture of crash frequency and crash severity. However in this study, only the facet of crash

frequency has been discussed, which inherently assumed that a fatal crash is indifferent from a

minor-injury crash. If crash frequency and crash severity can be simultaneously modelled, for

example in the form of crash frequency weighted by severity, the resulting safety implications

would indisputably have more practical values. Thus clearly this is also a worthwhile direction for

future researchers to pursue.

75

With more advanced micro-simulation packages and perhaps improved SPF modelling structures

to be introduced in the future, the field of traffic safety modelling will definitely attract more

researchers. It is the author’s hope that, this thesis will not only provide planners directions for

safer transit infrastructure designs, but also strengthen the foundation of safety modelling for future

researchers.

76

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81

Appendix A List of Modelled Micro-simulation Networks

Scenarios Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6 Scenario 7 Scenario 8 Scenario 9

# Intersection

Name

Removal of Green Extension

Removal of Red

Truncation

Removal of TSP

Near-Sided to Far-sided

stops [Major

Directions without

TSP]

Near-Sided to Far-sided

stops [Minor

Directions without

TSP]

Near-Sided to No stops [Minor

Directions without

TSP]

Near-Sided to Far-sided

stops [TSP Directions]

Streetcar to Bus (1:1

replace-ment)

Streetcar to Bus (1:m

Replace-ment for

Same Capacity)

1 Bloor St E and Jarvis St

2 Church St and Front St E

3 Church St and King St E

4 Church St and Gerrard St E

5 Yonge St and King St E

6 Yonge St and Wellesley St E

7 Yonge St and Eglinton Ave E

8 Bay St and Front St W

9 Bay St and Bloor St W

10 University Ave and King St W

82

11 Eglinton Ave W and Bathurst St

12 Lawrence Ave W and Avenue Rd

13 Avenue Rd and Wilson Ave

14 Kingston Rd and Dixon Ave

15 Church St and Park Rd

16 Dundas St W and Ossington Ave

17 Dundas St W and Dovercourt Rd

18 Eglinton Ave E and Mccowan Rd

19 Lake Shore Blvd E and Lower Jarvis St

20 Lake Shore Blvd W and Marine Parade Dr

21

Lake Shore Blvd W and Colonel Samuel Smith Park Dr

22 Parliament St and Gerrard St E

23 Sherbourne St and King St E

24 Sherbourne St and Dundas St E

83

25 King St W and Spadina Ave

26 Danforth Ave and Broadview Ave

27 Bathurst St and Harbord St

28 Bathurst St and Dupont St

29 Bathurst St and Glencairn Ave

30 Bloor St W and Dundas St W

31 Bloor St W and Keele St

32 Danforth Ave and Greenwood Ave

33 Danforth Ave and Main St

34 Gerrard St E and Carlaw Ave

35 Danforth Rd and Midland Ave

36 Mccowan Rd and Lawrence Ave E

37 Danforth Rd and St Clair Ave E

38 Lawrence Ave E and The Donway E

84

39 Lawrence Ave E and Pharmacy Ave

40 Lawrence Ave E and Midland Ave

41 Lawrence Ave E and Markham Rd

42 Lawrence Ave W and Marlee Ave

43 Dufferin St and Lawrence Ave W

44 Lawrence Ave W and Caledonia Rd

45 Weston Rd and Lawrence Ave W

46 Lawrence Ave W and Scarlett Rd

47 Dixon Rd and Royal York Rd

48 O Connor Dr and Pape Ave

49 O Connor Dr and Coxwell Ave

50 O Connor Dr and St Clair Ave E

51 Eglinton Ave W and Keele St

52 St Clair Ave E and Pharmacy Ave

85

53 St Clair Ave W and Old Weston Rd

54 St Clair Ave W and Runnymede Rd

55 Keele St and Gulliver Rd

56 Keele St and Wilson Ave

57 Dufferin St and Glencairn Ave

58 Bayview Ave and Finch Ave E

59 Coxwell Ave and Mortimer Ave

60 Midland Ave and Progress Ave

61 Kennedy Rd and Glamorgan Ave

62 Ellesmere Rd and Pharmacy Ave

63 Mccowan Rd and Ellesmere Rd

64 Ellesmere Rd and Morningside Ave

65 Albion Rd and Kipling Ave

66 Sheppard Ave E and Willowdale Ave

86

67 Kennedy Rd and Sheppard Ave E

68 Browns Line and Horner Ave

69 Martin Grove Rd and The Westway

70 Finch Ave W and Arrow Rd

71 College St and Dovercourt Rd

72 College St and Lansdowne Ave

73 Dupont St and Spadina Rd

74 Dupont St and Dovercourt Rd

75 Martin Grove Rd and Rathburn Rd

76 Ellesmere Rd and Helicon Gt

77 Finch Ave W and Islington Ave

78 Finch Ave W and Martin Grove Rd

79 Finch Ave W and Alness St

80 Finch Ave E and Victoria Park Ave

81 Finch Ave E and Kennedy Rd

82 Pharmacy Ave and Huntingwood Dr

87

83 Kingston Rd and Scarborough Golf Club Rd

84 Ellesmere Rd and Orton Park Rd

85 Sheppard Ave E and Morningside Ave

86 Kennedy Rd and Huntingwood Dr

87 Morningside Ave and Military Trl

88 Steeles Ave E and Pharmacy Ave

89 Steeles Ave E and Warden Ave

90 Steeles Ave E and Kennedy Rd

91 Steeles Ave W and Islington Ave

92 Steeles Ave W and Signet Dr

93 Victoria Park Ave and Gordon Baker Rd

94 Finch Ave E and Tapscott Rd

95 Warden Ave and Mcnicoll Ave

96 Carlingview Dr and

88

International Blvd

97 Brimley Rd and Mcnicoll Ave

98 Tapscott Rd and Mclevin Ave

99 Morningside Ave and Milner Ave

100 Morningside Ave and Sewells Rd

Total Selected Networks

20 10 20 35 25 35 15 15 15

89

Appendix B Detailed Scenario Test Results

This Appendix details the resulting predicted crashes rates in each of the scenario test. The notation

in each of the following tables is “∆ (B)”, where ∆ is the change in predicted crash frequency

relative to status-quo case and B is the predicted crash frequency for status-quo case. A positive ∆

indicates an increase in predicted crash frequency and vice versa. The summations amongst the

studied intersections are also provided at the bottom of each table. Since a reduction in predicted

crash is the more desirable direction, a negative total changes in predicted crashes is coloured in

green. Conversely, a positive total changes in predicted crashes is coloured in red.

Scenario 1 With Green Extension Without Green Extension

Intersections (20 in total) All Impact

Types Angle Rear-End

Side-Swipe

Transit-Involved

Church St and King St E 0.5 (78.2) 1.0 (35.3) -0.2 (23.8) 0.4 (12.4) -0.2 (6.4)

Dundas St W and Ossington Ave 1.4 (62.2) 0.5 (33.1) 0.4 (15.5) 0.5 (11.5) 0.1 (4.6)

Dundas St W and Dovercourt Rd -1.1 (43.4) -0.5 (15.9) -0.3 (11.2) -0.2 (9.0) -0.2 (1.4)

Parliament St and Gerrard St E -0.4 (90.0) 0.7 (32.5) -0.3 (31.1) -0.3 (13.4) 0.1 (6.1)

Sherbourne St and King St E 1.5 (59.1) 2.4 (23.9) 0.2 (18.0) -0.1 (7.7) -0.1 (3.7)

Sherbourne St and Dundas St E 0.1 (66.6) 0.2 (35.4) 0.1 (18.6) -0.3 (8.5) 0.5 (4.6)

Danforth Ave and Broadview Ave -6.1 (161.3) -1.5 (53.6) -3.0 (68.3) -1.6 (20.1) 0.0 (5.9)

Bathurst St and Glencairn Ave 0.8 (106.9) -1.4 (38.6) 0.6 (39.2) 0.5 (14.8) 0.1 (4.3)

Bloor St W and Dundas St W -6.2 (133.4) -0.5 (62.0) -2.9 (45.5) -2.0 (23.1) -0.1 (6.8)

Danforth Ave and Main St -2.8 (91.9) 0.4 (37.1) -1.6 (32.0) -0.5 (11.2) 0.2 (5.2)

Gerrard St E and Carlaw Ave -1.5 (69.7) -1.0 (36.9) -0.4 (18.9) -0.4 (11.0) -0.2 (3.5)

Dufferin St and Glencairn Ave 0.2 (82.0) 0.6 (31.5) -0.1 (27.5) 0.2 (11.5) 0.0 (3.8)

Mccowan Rd and Ellesmere Rd 1.1 (156.6) 1.1 (44.9) 0.3 (65.9) 0.5 (22.9) 0.2 (6.7)

Sheppard Ave E and Willowdale Ave

0.0 (93.5) 0.0 (27.2) 0.0 (35.3) 0.0 (11.0) 0.0 (2.3)

Finch Ave W and Arrow Rd 1.7 (128.3) 0.7 (53.5) 0.9 (48.9) 0.0 (14.3) -0.1 (4.7)

College St and Dovercourt Rd -0.4 (65.8) 1.0 (25.4) -0.3 (21.3) -0.4 (7.6) 0.2 (1.9)

College St and Lansdowne Ave -0.2 (99.8) 0.3 (26.5) -0.2 (32.9) 0.0 (23.6) 0.2 (4.9)

Finch Ave W and Islington Ave 0.7 (199.7) 0.0 (70.6) 1.0 (85.1) -0.8 (31.2) 0.0 (8.9)

Finch Ave W and Alness St -14.3 (146.4) -2.7 (43.8) -8.1 (61.4) -1.6 (19.2) -0.3 (4.3)

90

Finch Ave E and Tapscott Rd 2.2 (67.4) 2.2 (25.5) 0.5 (21.8) 0.5 (8.6) 0.4 (3.6)

Summation of Changes in Predicted Crashes

-22.75 3.62 -13.63 -5.75 0.79

Summation of Predicted Crashes in Status Quo Case 2002.3 753.1 722.0 292.5 93.8

Percentage Difference relative to Status Quo Case -1.14% 0.48% -1.89% -1.97% 0.84%

Scenario 2 (10 in total) With Red Truncation Without Red Truncation

Intersections All Impact

Types Angle Rear-End

Side-Swipe

Transit-Involved

Church St and King St E -1.0 (78.2) -1.2 (35.3) -0.3 (23.8) 0.0 (12.4) -0.4 (6.4)

Dundas St W and Ossington Ave 0.7 (62.2) -0.9 (33.1) 0.5 (15.5) 0.4 (11.5) 0.4 (4.6)

Dundas St W and Dovercourt Rd 0.1 (43.4) -0.6 (15.9) 0.1 (11.2) 0.2 (9.0) 0.2 (1.4)

Sherbourne St and King St E -1.7 (59.1) -1.5 (23.9) -0.4 (18.0) -0.6 (7.7) -0.1 (3.7)

Danforth Ave and Broadview Ave -1.8 (161.3) -0.3 (53.6) -0.7 (68.3) -0.9 (20.1) -0.2 (5.9)

Bathurst St and Glencairn Ave -5.2 (106.9) -2.4 (38.6) -1.9 (39.2) -1.7 (14.8) 0.1 (4.3)

Danforth Ave and Main St -1.0 (91.9) -1.7 (37.1) -0.2 (32.0) 0.2 (11.2) -0.1 (5.2)

Gerrard St E and Carlaw Ave 0.1 (69.7) 0.0 (36.9) 0.2 (18.9) -0.3 (11.0) 0.0 (3.5)

College St and Dovercourt Rd 0.4 (65.8) 0.9 (25.4) 0.0 (21.3) -0.1 (7.6) 0.1 (1.9)

Finch Ave W and Alness St -4.7 (146.4) -2.0 (43.8) -2.8 (61.4) 0.2 (19.2) -0.3 (4.3)

Summation of Changes in Predicted Crashes -14.11 -9.77 -5.39 -2.65 -0.04

Summation of Predicted Crashes in Status Quo Case 885.0 343.5 309.5 124.5 41.2

Percentage Difference relative to Status Quo Case -1.59% -2.85% -1.74% -2.13% -0.09%

Scenario 3 (20 in total) With TSP (both Green Extension and truncation)

Without TSP

Intersections All Impact

Types Angle Rear-End

Side-Swipe

Transit-Involved

Church St and King St E -0.8 (78.2) -0.3 (35.3) -0.3 (23.8) -0.1 (12.4) -0.3 (6.4)

Dundas St W and Ossington Ave 0.3 (62.2) 0.6 (33.1) -0.4 (15.5) 0.7 (11.5) 0.3 (4.6)

Dundas St W and Dovercourt Rd 0.1 (43.4) -0.6 (15.9) 0.4 (11.2) -0.4 (9.0) 0.4 (1.4)

Parliament St and Gerrard St E -0.4 (90.0) 0.7 (32.5) -0.3 (31.1) -0.3 (13.4) 0.1 (6.1)

Sherbourne St and King St E -3 (59.1) -1.3 (23.9) -1.2 (18.0) -0.4 (7.7) -0.1 (3.7)

91

Sherbourne St and Dundas St E 0.1 (66.6) 0.2 (35.4) 0.1 (18.6) -0.3 (8.5) 0.5 (4.6)

Danforth Ave and Broadview Ave -6.6 (161.3) -3.1 (53.6) -3.2 (68.3) -1.0 (20.1) 0.0 (5.9)

Bathurst St and Glencairn Ave 4.3 (106.9) 0.7 (38.6) 2.2 (39.2) 0.6 (14.8) 0.2 (4.3)

Bloor St W and Dundas St W -6.2 (133.4) -0.5 (62.0) -2.9 (45.5) -2.0 (23.1) -0.1 (6.8)

Danforth Ave and Main St -3.7 (91.9) -0.2 (37.1) -1.9 (32.0) -0.7 (11.2) 0.3 (5.2)

Gerrard St E and Carlaw Ave -0.7 (69.7) -0.1 (36.9) -0.3 (18.9) -0.3 (11.0) -0.1 (3.5)

Dufferin St and Glencairn Ave 0.2 (82.0) 0.6 (31.5) -0.1 (27.5) 0.2 (11.5) 0.0 (3.8)

Mccowan Rd and Ellesmere Rd 1.1 (156.6) 1.1 (44.9) 0.3 (65.9) 0.5 (22.9) 0.2 (6.7)

Sheppard Ave E and Willowdale Ave 0.0 (93.5) 0.0 (27.2) 0.0 (35.3) 0.0 (11.0) 0.0 (2.3)

Finch Ave W and Arrow Rd 1.7 (128.3) 0.7 (53.5) 0.9 (48.9) 0.0 (14.3) -0.1 (4.7)

College St and Dovercourt Rd -1.5 (65.8) 0.1 (25.4) -1.0 (21.3) 0.4 (7.6) 0.2 (1.9)

College St and Lansdowne Ave -0.2 (99.8) 0.3 (26.5) -0.2 (32.9) 0.0 (23.6) 0.2 (4.9)

Finch Ave W and Islington Ave 0.7 (199.7) 0.0 (70.6) 1.0 (85.1) -0.8 (31.2) 0.0 (8.9)

Finch Ave W and Alness St -11.6 (146.4) -3.2 (43.8) -7.4 (61.4) 1.0 (19.2) -0.6 (4.3)

Finch Ave E and Tapscott Rd 2.2 (67.4) 2.3 (25.5) 0.4 (21.8) 0.5 (8.6) 0.4 (3.6)

Summation of Changes in Predicted Crashes

-23.93 -1.85 -13.76 -2.46 1.51

Summation of Predicted Crashes in Status Quo Case

2002.3 753.1 722.0 292.5 93.8

Percentage Difference relative to Status Quo Case

-1.20% -0.25% -1.91% -0.84% 1.61%

Scenario 4 (35 in total) Near-Sided Stops (Along Major Direction at non-TSP

intersections) Far-sided Stops

Intersections All Impact

Types Angle Rear-End

Side-Swipe

Transit-Involved

Yonge St and King St E 1.2 (51.3) 1.3 (13.6) 0.2 (15.1) 0.4 (10.4) 0.4 (3.0)

Bay St and Front St W 2.5 (72.0) 1.4 (39.2) 0.7 (19.4) 0.7 (11.2) 0.7 (3.8)

Bay St and Bloor St W 2.5 (95.2) 2.9 (37.5) 0.9 (33.3) -0.5 (12.4) -0.2 (3.4)

Avenue Rd and Wilson Ave -0.2 (190.8) 1.8 (70.9) -1.0 (81.5) 0.7 (25.8) 0.9 (5.6)

Eglinton Ave E and Mccowan Rd 1.7 (80.5) 3.7 (28.1) -0.1 (27.0) 0.4 (12.5) 1.0 (6.0)

Bathurst St and Dupont St 7.6 (109.5) 10.1 (50.2) 1.3 (37.3) 0.9 (15.5) 1.4 (3.8)

Mccowan Rd and Lawrence Ave E 5.4 (232.5) 3.4 (61.3) 2.1 (111.2) 2.3 (31.2) 0.9 (7.3)

Danforth Rd and St Clair Ave E -0.8 (139.1) 0.8 (30.9) -1.0 (60.4) 0.7 (16.2) 0.1 (4.4)

Lawrence Ave E and Pharmacy Ave 2.2 (175.0) 1.8 (54.5) 1.0 (77.3) 0.3 (20.1) 1.2 (4.5)

92

Lawrence Ave E and Markham Rd 4.3 (114.5) -1.9 (45.3) 2.7 (41.2) 1.1 (16.9) 0.5 (4.7)

Lawrence Ave W and Marlee Ave 1.3 (167.2) 2.8 (43.7) 0.3 (74.4) -0.1 (20.3) 1.0 (4.9)

Dufferin St and Lawrence Ave W 5.1 (176.5) 3.5 (49.3) 2.5 (77.2) 0.4 (25.4) 1.4 (6.8)

Weston Rd and Lawrence Ave W 1.3 (91.0) 2.3 (31.2) 0.3 (28.8) -0.1 (19.6) 0.4 (6.4)

Dixon Rd and Royal York Rd 9.4 (165.8) 7.7 (64.6) 4.0 (68.6) 0.8 (19.7) 0.1 (4.2)

O Connor Dr and Pape Ave 1.4 (64.5) 3.9 (30.2) -0.3 (18.3) -0.2 (10.1) 0.9 (6.3)

O Connor Dr and St Clair Ave E 1.3 (134.2) 0.8 (13.2) 0.9 (59.4) -0.5 (17.6) 0.0 (4.6)

Bayview Ave and Finch Ave E 5.2 (220.3) 2.1 (66.9) 3.0 (102.3) 0.6 (28.7) 1.6 (4.4)

Coxwell Ave and Mortimer Ave 1.2 (79.3) 0.8 (29.7) 0.4 (26.2) 0.3 (11.9) 0.8 (3.0)

Midland Ave and Progress Ave 1.5 (111.2) 2.3 (36.2) 0.1 (42.5) 0.5 (14.6) 1.3 (3.8)

Kennedy Rd and Glamorgan Ave -0.6 (110.5) -0.6 (38.5) 0.0 (41.1) -0.4 (15.6) 0.7 (3.3)

Ellesmere Rd and Morningside Ave 5.9 (137.5) 1.6 (46.7) 2.7 (56.3) 1.8 (15.2) 0.8 (5.8)

Albion Rd and Kipling Ave 2.6 (110.9) 0.8 (34.7) 0.9 (41.1) 1.0 (18.0) 0.9 (4.8)

Kennedy Rd and Sheppard Ave E 2.9 (183.9) 3.0 (53.9) 0.8 (80.1) 0.8 (27.7) 0.7 (5.7)

Browns Line and Horner Ave 2.4 (105.6) 1.2 (32.1) 0.9 (40.7) 0.7 (13.2) 1.1 (2.9)

Finch Ave W and Martin Grove Rd 1.8 (93.9) 0.5 (45.3) 0.6 (30.5) 1.0 (11.9) 0.2 (6.1)

Finch Ave E and Victoria Park Ave 5.2 (103.9) 1.5 (43.7) 2.5 (35.3) 0.6 (16.4) 1.8 (6.1)

Finch Ave E and Kennedy Rd 0.5 (176.4) -0.7 (49.0) -0.2 (74.8) 1.1 (29.9) 0.2 (5.6)

Ellesmere Rd and Orton Park Rd 8.4 (96.6) 3.2 (29.5) 3.9 (35.7) 0.9 (13.4) 1.6 (5.7)

Sheppard Ave E and Morningside Ave 4 (85.6) 0.9 (32.5) 1.7 (28.3) 1.0 (13.7) -0.6 (4.2)

Kennedy Rd and Huntingwood Dr 2.9 (149.3) 1.4 (43.4) 1.4 (64.5) 0.5 (16.0) 0.2 (2.9)

Steeles Ave E and Pharmacy Ave 2.5 (171.7) 4.2 (65.7) 0.1 (69.1) 0.8 (26.4) 1.1 (5.5)

Steeles Ave E and Warden Ave -0.5 (213.4) 2.5 (64.1) -0.9 (96.9) -0.1 (30.8) 0.7 (6.4)

Steeles Ave E and Kennedy Rd 1.3 (204.2) -1.0 (60.4) 1.4 (89.7) -0.3 (33.4) 0.1 (7.0)

Steeles Ave W and Islington Ave 2.9 (162.9) 1.1 (58.2) 1.3 (66.6) 0.5 (23.8) 1.5 (3.7)

Steeles Ave W and Signet Dr 1.5 (87.9) 0.3 (29.8) 0.7 (31.6) 0.3 (10.9) 0.1 (3.7)

Summation of Changes in Predicted Crashes 97.89 71.72 35.77 18.81 25.66

Summation of Predicted Crashes in Status Quo Case 4664.7 1524.1 1883.7 656.4 170.2

Percentage Difference relative to Status Quo Case 2.10% 4.71% 1.90% 2.87% 15.08%

Scenario 5 (20 in total) Near-Sided Stops (Along Minor Direction at non-TSP

intersections) Far-sided Stops

93

Intersections All Impact

Types Angle Rear-End

Side-Swipe

Transit-Involved

Lawrence Ave W and Marlee Ave -1.7 (104.4) 1.1 (20.3) -1.2 (41.9) -0.1 (14.0) 0.5 (1.4)

Dufferin St and Lawrence Ave W 1.7 (51.3) 1.0 (13.6) 0.6 (15.1) 0.1 (10.4) 0.5 (3.0)

Weston Rd and Lawrence Ave W 2.1 (72.4) 0.4 (26.0) 0.7 (22.6) 0.8 (12.9) 0.6 (3.8)

Dixon Rd and Royal York Rd 1.4 (109.5) 1.8 (50.2) 0.3 (37.3) 0.1 (15.5) 0.4 (3.8)

O Connor Dr and Pape Ave 1.9 (232.5) 0.3 (61.3) 1.2 (111.2) 0.4 (31.2) -1.3 (7.3)

O Connor Dr and St Clair Ave E -3.1 (175.0) 1.6 (54.5) -2.3 (77.3) -0.4 (20.1) 0.2 (4.5)

Bayview Ave and Finch Ave E 4.2 (114.5) 1.0 (45.3) 1.9 (41.2) 1.1 (16.9) 0.8 (4.7)

Coxwell Ave and Mortimer Ave 4.4 (176.5) 1.3 (49.3) 2.5 (77.2) 0.4 (25.4) 0.7 (6.8)

Midland Ave and Progress Ave 4.1 (168.9) 4.1 (49.8) 1.8 (75.0) 0.0 (18.3) 0.0 (5.8)

Kennedy Rd and Glamorgan Ave 1.7 (91.0) 2.9 (31.2) 0.3 (28.8) 0.0 (19.6) 0.1 (6.4)

Ellesmere Rd and Morningside Ave 33.6 (165.8) 26.1 (64.6) 14.5 (68.6) 2.7 (19.7) 0.7 (4.2)

Albion Rd and Kipling Ave 2.0 (64.5) 0.3 (30.2) 0.9 (18.3) 0.3 (10.1) 0.5 (6.3)

Kennedy Rd and Sheppard Ave E 0.4 (93.8) -0.1 (31.3) 0.1 (31.6) 0.4 (17.7) 0.2 (5.4)

Browns Line and Horner Ave -1.8 (220.3) -0.1 (66.9) -1.1 (102.3) -0.5 (28.7) 0.7 (4.4)

Finch Ave W and Martin Grove Rd 1.0 (79.3) 1.9 (29.7) 0.2 (26.2) -0.3 (11.9) 0.2 (3.0)

Finch Ave E and Victoria Park Ave -1.1 (111.2) -1.6 (36.2) -0.3 (42.5) 0.0 (14.6) 0.4 (3.8)

Finch Ave E and Kennedy Rd 5.2 (137.5) 0.9 (46.7) 2.7 (56.3) 1.2 (15.2) 0.6 (5.8)

Ellesmere Rd and Orton Park Rd 0.9 (110.9) 0.0 (34.7) 0.7 (41.1) -0.4 (18.0) 0.9 (4.8)

Sheppard Ave E and Morningside Ave 6.1 (183.9) 2.9 (53.9) 3.0 (80.1) 1.1 (27.7) 1.8 (5.7)

Kennedy Rd and Huntingwood Dr 0.7 (90.1) 0.7 (26.1) 0.0 (32.8) 0.6 (13.0) 0.4 (3.5)

Steeles Ave E and Pharmacy Ave 1.8 (93.9) 0.5 (45.3) 0.6 (30.5) 1.0 (11.9) 0.2 (6.1)

Steeles Ave E and Warden Ave 7.9 (103.9) 0.3 (43.7) 3.7 (35.3) 2.4 (16.4) 0.4 (6.1)

Steeles Ave E and Kennedy Rd -0.1 (149.3) 4.7 (43.4) -1.1 (64.5) 0.0 (16.0) 0.0 (2.9)

Steeles Ave W and Islington Ave 3.1 (213.4) 1.3 (64.1) 2.2 (96.9) -0.6 (30.8) -0.4 (6.4)

Steeles Ave W and Signet Dr 0.9 (87.9) 1.0 (29.8) 0.2 (31.6) 0.2 (10.9) 0.4 (3.7)

Summation of Changes in Predicted Crashes 77.39 54.37 32.16 10.59 9.59

Summation of Predicted Crashes in Status Quo Case 3201.7 1048.0 1286.3 447.0 119.3

Percentage Difference relative to Status Quo Case 2.42% 5.19% 2.50% 2.37% 8.03%

94

Scenario 6 (35 in total) Near-Sided Stops (Along Major Direction at non-TSP

intersections) No Transit Stops

Intersections All Impact

Types Angle Rear-End

Side-Swipe

Transit-Involved

Yonge St and King St E -0.3 (51.3) -0.6 (13.6) -0.1 (15.1) 0.1 (10.4) -0.3 (3.0)

Bay St and Front St W -5.0 (72.0) -1.5 (39.2) -2.2 (19.4) -0.8 (11.2) -2.1 (3.8)

Bay St and Bloor St W -3.1 (95.2) 0.7 (37.5) -1.7 (33.3) -0.9 (12.4) -1.9 (3.4)

Avenue Rd and Wilson Ave -8.0 (190.8) -2.2 (70.9) -5.0 (81.5) 0.0 (25.8) -1.2 (5.6)

Eglinton Ave E and Mccowan Rd -9.1 (80.5) -2.8 (28.1) -3.8 (27.0) -1.7 (12.5) -3.1 (6.0)

Bathurst St and Dupont St -3.7 (109.5) -0.7 (50.2) -2.0 (37.3) -0.6 (15.5) -0.3 (3.8)

Mccowan Rd and Lawrence Ave E -3.2 (232.5) -2.0 (61.3) -1.5 (111.2) -0.7 (31.2) -0.8 (7.3)

Danforth Rd and St Clair Ave E -4.2 (139.1) -2.1 (30.9) -1.9 (60.4) -0.9 (16.2) -0.5 (4.4)

Lawrence Ave E and Pharmacy Ave -2.7 (175.0) -1.8 (54.5) -1.3 (77.3) -0.2 (20.1) -0.7 (4.5)

Lawrence Ave E and Markham Rd -2 (114.5) -2.6 (45.3) -0.7 (41.2) 0.3 (16.9) -0.7 (4.7)

Lawrence Ave W and Marlee Ave -5.1 (167.2) -1.0 (43.7) -2.7 (74.4) -1.2 (20.3) -2.0 (4.9)

Dufferin St and Lawrence Ave W -0.3 (176.5) -0.6 (49.3) 0.3 (77.2) -0.9 (25.4) 0.6 (6.8)

Weston Rd and Lawrence Ave W -3.5 (91.0) -0.8 (31.2) -1.8 (28.8) -0.3 (19.6) -0.3 (6.4)

Dixon Rd and Royal York Rd 7.3 (165.8) 4.5 (64.6) 3.3 (68.6) 1.0 (19.7) -1.3 (4.2)

O Connor Dr and Pape Ave -1.7 (64.5) 0.7 (30.2) -1.5 (18.3) 0.7 (10.1) -0.3 (6.3)

O Connor Dr and St Clair Ave E 0.1 (134.2) 0.2 (13.2) 0.3 (59.4) -0.6 (17.6) -0.3 (4.6)

Bayview Ave and Finch Ave E -3.6 (220.3) -0.3 (66.9) -2.1 (102.3) -0.9 (28.7) 0.4 (4.4)

Coxwell Ave and Mortimer Ave -0.8 (79.3) -0.1 (29.7) -0.3 (26.2) -0.1 (11.9) -0.4 (3.0)

Midland Ave and Progress Ave -2.1 (111.2) -2.0 (36.2) -0.8 (42.5) 0.0 (14.6) -0.9 (3.8)

Kennedy Rd and Glamorgan Ave -2.3 (110.5) -0.3 (38.5) -1.3 (41.1) -0.3 (15.6) -0.9 (3.3)

Ellesmere Rd and Morningside Ave -4 (137.5) -0.8 (46.7) -2.2 (56.3) -0.6 (15.2) -1.0 (5.8)

Albion Rd and Kipling Ave 0.6 (110.9) 1.1 (34.7) 0.1 (41.1) 0.0 (18.0) 0.1 (4.8)

Kennedy Rd and Sheppard Ave E -0.8 (183.9) 0.1 (53.9) -0.5 (80.1) -0.3 (27.7) -0.4 (5.7)

Browns Line and Horner Ave -1.9 (105.6) -1.6 (32.1) -0.9 (40.7) 0.2 (13.2) -0.6 (2.9)

Finch Ave W and Martin Grove Rd 0.5 (93.9) 0.5 (45.3) -0.1 (30.5) 0.5 (11.9) -0.5 (6.1)

Finch Ave E and Victoria Park Ave -4.3 (103.9) -1.6 (43.7) -2.0 (35.3) -0.5 (16.4) -2.2 (6.1)

Finch Ave E and Kennedy Rd -2.4 (176.4) -1.2 (49.0) -1.4 (74.8) -0.1 (29.9) -0.2 (5.6)

Ellesmere Rd and Orton Park Rd -8.4 (96.6) -1.7 (29.5) -3.7 (35.7) -2.2 (13.4) -2.4 (5.7)

Sheppard Ave E and Morningside Ave -0.3 (85.6) 0.4 (32.5) -0.5 (28.3) 0.6 (13.7) -0.7 (4.2)

95

Kennedy Rd and Huntingwood Dr -1.9 (149.3) 0.2 (43.4) -1.1 (64.5) -0.4 (16.0) -1.0 (2.9)

Steeles Ave E and Pharmacy Ave -2.5 (171.7) 0.9 (65.7) -2.0 (69.1) 0.0 (26.4) -2.1 (5.5)

Steeles Ave E and Warden Ave -3.6 (213.4) -1.6 (64.1) -1.7 (96.9) -1.1 (30.8) -0.8 (6.4)

Steeles Ave E and Kennedy Rd -5.5 (204.2) -1.7 (60.4) -2.6 (89.7) -1.7 (33.4) -0.5 (7.0)

Steeles Ave W and Islington Ave 0.1 (162.9) -0.2 (58.2) -0.2 (66.6) 0.2 (23.8) 0.5 (3.7)

Steeles Ave W and Signet Dr -4.1 (87.9) -2.5 (29.8) -1.5 (31.6) -0.6 (10.9) -2.3 (3.7)

Summation of Changes in Predicted Crashes -92.11 -25.03 -46.99 -13.64 -30.92

Summation of Predicted Crashes in Status Quo Case 4664.7 1524.1 1883.7 656.4 170.2

Percentage Difference relative to Status Quo Case -1.97% -1.64% -2.49% -2.08% -18.17%

Scenario 7 Near-Sided Stops (Along TSP-servicing Direction at TSP

intersections) Near-Sided Transit Stops

Intersections (15 in total) All Impact

Types Angle Rear-End

Side-Swipe

Transit-Involved

Church St and King St E 3.9 (78.2) 4.1 (35.3) 1.2 (23.8) -0.1 (12.4) 0.0 (6.4)

Dundas St W and Ossington Ave 0.1 (62.2) -1.0 (33.1) 0.3 (15.5) 0.2 (11.5) -0.2 (4.6)

Dundas St W and Dovercourt Rd 0.8 (43.4) 0.8 (15.9) 0.2 (11.2) 0.0 (9.0) -0.1 (1.4)

Parliament St and Gerrard St E 2.2 (90.0) 2.5 (32.5) 0.2 (31.1) 0.9 (13.4) 0.1 (6.1)

Sherbourne St and King St E -2.2 (59.1) 1.1 (23.9) -1.5 (18.0) 0.0 (7.7) -1.3 (3.7)

Sherbourne St and Dundas St E 1.5 (66.6) 1.6 (35.4) 0.4 (18.6) 0.0 (8.5) -0.2 (4.6)

Danforth Ave and Broadview Ave -4.4 (161.3) -0.6 (53.6) -2.4 (68.3) -0.8 (20.1) -0.2 (5.9)

Bathurst St and Glencairn Ave 2.9 (106.9) 2.6 (38.6) 0.8 (39.2) 0.5 (14.8) 0.1 (4.3)

Gerrard St E and Carlaw Ave -0.8 (69.7) -1.1 (36.9) -0.2 (18.9) 0.1 (11.0) -0.2 (3.5)

Dufferin St and Glencairn Ave -0.1 (82.0) 0.6 (31.5) -0.6 (27.5) 0.7 (11.5) -0.7 (3.8)

Finch Ave W and Arrow Rd -2.3 (128.3) 0.2 (53.5) -1.6 (48.9) 0.0 (14.3) 0.1 (4.7)

College St and Dovercourt Rd -3.9 (65.8) 0.6 (25.4) -2.2 (21.3) -0.3 (7.6) -0.4 (1.9)

College St and Lansdowne Ave -2.6 (99.8) -0.3 (26.5) -1.2 (32.9) -0.6 (23.6) 0.1 (4.9)

Finch Ave W and Alness St 1.0 (146.4) 0.3 (43.8) 0.4 (61.4) 0.3 (19.2) 0.2 (4.3)

Finch Ave E and Tapscott Rd 4.3 (67.4) 1.7 (25.5) 1.2 (21.8) 1.9 (8.6) 0.6 (3.6)

Summation of Changes in Predicted Crashes 0.32 13.24 -4.82 2.76 -1.96

Summation of Predicted Crashes in Status Quo Case 1327.2 511.3 458.2 193.2 63.8

Percentage Difference relative to Status Quo Case 0.02% 2.59% -1.05% 1.43% -3.07%

96

Scenario 8 Thru-moving Streetcars Thru-moving Buses

(1:1 replacement)

Intersections (15 in total) All Impact

Types Angle Rear-End

Side-Swipe

Transit-Involved

Church St and King St E 1.0 (78.2) 1.2 (35.3) 0.3 (23.8) -0.1 (12.4) 0.6 (6.4)

Yonge St and King St E 2.6 (51.3) -0.2 (13.6) 1.1 (15.1) 0.6 (10.4) 1.7 (3.0)

University Ave and King St W 3.6 (62.4) 1.6 (43.6) 1.7 (12.1) 0.1 (10.3) 1.8 (5.2)

Dundas St W and Ossington Ave 1.7 (62.2) 0.0 (33.1) 0.6 (15.4) 0.8 (11.5) 0.7 (4.6)

Dundas St W and Dovercourt Rd 2.2 (43.4) 0.8 (15.9) 0.7 (11.2) 0.5 (9.0) 1.3 (1.4)

Lake Shore Blvd W and Marine Parade Dr -3.5 (122.5) -0.9 (23.4) -1.6 (50.1) -0.8 (18.9) -0.3 (4.5)

Lake Shore Blvd W and Colonel Samuel Smith Park Dr 0.0 (72.4) 0.1 (26.0) -0.2 (22.6) 0.5 (12.9) -0.1 (3.8)

Sherbourne St and King St E 1.3 (59.1) 3.4 (23.9) -0.3 (18.0) 0.2 (7.7) 1.5 (3.7)

Sherbourne St and Dundas St E 0.9 (66.6) -5.6 (35.4) 2.0 (18.6) 0.6 (8.5) -0.7 (4.6)

King St W and Spadina Ave -0.5 (97.6) 1.4 (30.5) -0.8 (33.2) 0.2 (19.3) 0.4 (5.5)

Bathurst St and Harbord St 2.2 (94.4) 1.7 (31.9) 0.8 (35.3) 0.3 (9.5) 1.6 (2.3)

Gerrard St E and Carlaw Ave 1.8 (69.6) 0.0 (36.9) 1.0 (18.9) 0.2 (11.0) 1.2 (3.5)

St Clair Ave W and Old Weston Rd 4.0 (93.8) 5.0 (31.3) 1.0 (31.6) 0.5 (17.7) 2.5 (5.4)

College St and Dovercourt Rd 2.7 (62.1) 1.8 (25.4) 0.9 (19.3) 0.4 (7.6) 1.9 (1.2)

College St and Lansdowne Ave -1.1 (99.8) 0.6 (26.5) -0.8 (32.9) 0.0 (23.6) 0.7 (4.9)

Summation of Changes in Predicted Crashes 19.04 10.97 6.20 3.77 14.75

Summation of Predicted Crashes in Status Quo Case 1135.5 432.7 358.0 190.3 60.0

Percentage Difference relative to Status Quo Case 1.68% 2.54% 1.73% 1.98% 24.57%

Scenario 9 Thru-moving Streetcars Thru-moving Buses (1:m replacement for same operating capacity)

Intersections (15 in total) All Impact

Types Angle Rear-End

Side-Swipe

Transit-Involved

Church St and King St E 2.5 (78.2) 0.2 (35.3) 1.3 (23.8) 0.4 (12.4) 1.4 (6.4)

Yonge St and King St E 4.7 (51.3) 1 (13.6) 2.1 (15.1) 0.4 (10.4) 2.4 (3)

University Ave and King St W 4.6 (62.4) -2.1 (43.6) 3.3 (12.1) 1 (10.3) 2.4 (5.2)

Dundas St W and Ossington Ave 3.3 (62.2) 1.1 (33.1) 0.9 (15.4) 1.2 (11.5) 1 (4.6)

Dundas St W and Dovercourt Rd 2.7 (43.4) 0.9 (15.9) 1 (11.2) 0.4 (9) 1.6 (1.4)

Lake Shore Blvd W and Marine Parade Dr -1.4 (122.5) 0.7 (23.4) -0.6 (50.1) -0.7 (18.9) 0.8 (4.5)

97

Lake Shore Blvd W and Colonel Samuel Smith Park Dr 2.7 (72.4) 1.1 (26) 1.2 (22.6) 0.2 (12.9) 0.6 (3.8)

Sherbourne St and King St E 5.6 (59.1) 7.8 (23.9) 0.9 (18) 0 (7.7) 2.8 (3.7)

Sherbourne St and Dundas St E 1.4 (66.6) -5.8 (35.4) 2.4 (18.6) 0.5 (8.5) -0.2 (4.6)

King St W and Spadina Ave 1 (97.6) 3 (30.5) 0.1 (33.2) -0.3 (19.3) 1.2 (5.5)

Bathurst St and Harbord St 2.2 (94.4) 2.8 (31.9) 0.3 (35.3) 1 (9.5) 2.3 (2.3)

Gerrard St E and Carlaw Ave 1.3 (69.6) -1.8 (36.9) 1.1 (18.9) 0.4 (11) 0.9 (3.5)

St Clair Ave W and Old Weston Rd 3.3 (93.8) 3.3 (31.3) 0.8 (31.6) 0.6 (17.7) 2.6 (5.4)

College St and Dovercourt Rd 2.1 (62.1) 1.7 (25.4) 0.6 (19.3) 0.1 (7.6) 1.8 (1.2)

College St and Lansdowne Ave 0.7 (99.8) 1.6 (26.5) 0 (32.9) 0.2 (23.6) 1 (4.9)

Summation of Changes in Predicted Crashes 36.55 15.66 15.27 5.34 22.45

Summation of Predicted Crashes in Status Quo Case 1135.5 432.7 358.0 190.3 60.0

Percentage Difference relative to Status Quo Case 3.22% 3.62% 4.26% 2.80% 37.39%

98

Detailed Operational Characteristics for the 20 TSP-featured intersections in Scenario 3

Intersections

Maximum

Green

Extension

(seconds)

Maximum

Red

Truncation

(seconds)

Peak

Hourly

Volume

Percentage of

Peak Hourly

Volume in the

TSP-servicing

direction

Cycle

Time

(seconds)

Transit

Type

receiving

TSP

Joint

Headway in

the direction

receiving

TSP

(minutes)

Church St and King St E 30 9 2,504 51.3% 70 Streetcar 1.85

Dundas St W and Ossington Ave 30 8 2,033 50.4% 70 Streetcar 5.25

Dundas St W and Dovercourt Rd 30 6 1,692 67.4% 70 Streetcar 5.25

Parliament St and Gerrard St E 16 2,344 100% (All direction) 70 Streetcar/Bus

4.33 (E-W) 9.50 (N-S)

Sherbourne St and King St E 30 12 1,633 48.1% 70 Streetcar 1.71

Sherbourne St and Dundas St E 16 2,072 100% (All direction) 70 Streetcar

5.25 (E-W) 7.00 (N-S)

Danforth Ave and Broadview Ave 30 23 3,675 29.4% 98 Streetcar 2.27

Bathurst St and Glencairn Ave 30 5 2,983 79.2% 80 Bus 6.00

Bloor St W and Dundas St W 30 3,875 38.0% 80 Streetcar 2.27

Danforth Ave and Main St 16 15 2,862 33.3% 79 Streetcar/Bus 2.65

Gerrard St E and Carlaw Ave 30 2 2,263 54.9% 70 Streetcar 4.33

Dufferin St and Glencairn Ave 30 2,979 80.3% 80 Bus 6.25

Mccowan Rd and Ellesmere Rd 9 5,138 32.3% 118 Bus 5.13 Sheppard Ave E and Willowdale

Ave 16 3,342 52.7% 110 Bus 30.00

Finch Ave W and Arrow Rd 16 4,899 66.6% 110 Bus 5.00

College St and Dovercourt Rd 30 7 1,935 58.4% 70 Streetcar 4.33

College St and Lansdowne Ave 16 1,718 54.0% 80 Streetcar 6.00

Finch Ave W and Islington Ave 16 7,204 61.0% 108 Bus 6.50

Finch Ave W and Alness St 14 21 4,200 67.5% 108 Bus 6.50

Finch Ave E and Tapscott Rd 16 2,477 61.1% 98 Bus 2.80