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A Comprehensive Study on the Effectiveness of Office-based TDM Policies
by
Md Sami Hasnine
A thesis submitted in conformity with the requirements for the degree of Masters of Applied Science
Department of Civil Engineering University of Toronto
©Copyright by Md Sami Hasnine 2015
ii
A Comprehensive Study on the Effectiveness of Office-based
TDM Policies
Md Sami Hasnine
Masters of Applied Science
Department of Civil Engineering University of Toronto
2015
Abstract
The objective of this thesis is to develop an employer-based Transportation Demand Management
(TDM) evaluation tool that can be used for evaluating various employer-based TDM policies. The
conventional method of evaluating TDM policies has typically been conducting expensive before
and after TDM policy implementation surveys. On the contrary, this research used a pre-policy
deployment joint Revealed Preference and Stated Preference (RP-SP) survey, where the data were
collected to develop a TDM policy sensitive mode choice model, which is packaged into a software
system for TDM investment decision support. The evaluation tool (named Off-TET) developed by
integrating the mode choice model predicts changes in modal share by integrating all possible
effects of single or multiple TDM policies implemented in isolation or combined. While the tool
presented in this thesis was developed for the region of Peel, there exist opportunities for the
application of this type of analysis across Canada.
iii
Acknowledgments
Sometimes you may stand in front of your lifelong dream! Yes, this is that moment. The roadway
that I passed was not smooth. Failure was the part of my life. I faced failure at every nanosecond
of the last 24 years. However, I never gave up; I kept trying. All the credit goes to almighty Allah
who showed me the right path, who gave me countless blessings in my life and to whom I am
always liable for my actions.
Despite my failure, four people were always with me in my most hard times: my Mom (Laila
Ferdouse), Dad (Engr. Md Shafiqul Islam), Professor Dr. Khandker Nurul Habib and Professor
Dr. Md. Shamsul Hoque. The last 18 months I have learnt a lot from my supervisor Professor
Habib. He taught me how to work hard. The most interesting part is, I always feel lucky that have
such an awesome supervisor in my graduate studies.
Many thanks to Professor Amer Shalaby for spending his valuable time reading my thesis and
providing constructive feedback as a second reviewer of my thesis.
I want to express my cordial gratitude to my mentors for their concern about me: Rifat Kamrul,
Tamer El-Azzony, Samiha, Tamer Abdul-Azim, and Mohamed Mahmoud. I want to thank Elli
and Mehdi for motivating me all the time. I want to thank Graeme, Adam and Luke for helping
me to edit my thesis. Thanks my little sister, Sinthy, for inspiring me all the time.
I want to cordially thank Chamak madam and her family, Engr. Saadullah and his family,
Mohammad Hossain and his family for their awesome foods over the years. In graduate life, food
is the most important motivational tool. I believe there is no boxe at their home right now as I
brought all of those along with cooked foods.
I want to thank Faysal, Sajal, Majbah, Sudip, Tanvir, Najmus, Yafee, Rasheed Sir, Mokhles sir,
for their help during my higher studies. I also want to thank my old friends Faysal, Tanmoy, Sazid,
Urmi, Rafi, Rubel, Plabon, Sharif , Javed, Tomal, Sujhon for all of their sacrifice as a friend of
mine.
iv
I want to thank my project co-worker Adam Weiss, Sabrina Khan, Chan Wayne and Chris Miller
for their support during my project. I want to say thanks to my roommates Joy and Mahtab for
giving me such an awesome time.
Md Sami Hasnine
April 2015
v
Table of Contents
Acknowledgments.......................................................................................................................... iii
Table of Contents .............................................................................................................................v
List of Tables ............................................................................................................................... viii
List of Figures ................................................................................................................................ ix
Glossary ......................................................................................................................................... xi
1 Introduction .................................................................................................................................1
1.1 Objectives ............................................................................................................................2
1.2 Methodology ........................................................................................................................3
1.3 Thesis Outline ......................................................................................................................4
1.4 Chapter Summary ................................................................................................................4
2 Literature Review ........................................................................................................................5
2.1 Transportation Demand Management (TDM) Policy ..........................................................5
2.2 Classification of TDM Policies ............................................................................................5
2.3 TDM Policy Evaluation Methods ........................................................................................6
2.4 Data based Evaluation..........................................................................................................7
2.5 Model based Evaluation .......................................................................................................7
2.6 Mathematical Models for TDM Policy Evaluation Tools ...................................................8
2.6.1 COMMUTER ..........................................................................................................8
2.6.2 MSAT ......................................................................................................................8
2.6.3 TRIMMS ..................................................................................................................8
2.7 Shortcomings in existing Surveys to Develop TDM Evaluation Tool for the Region of Peel .......................................................................................................................................9
2.8 Chapter Summary ................................................................................................................9
3 Background of the Study Area ..................................................................................................10
vi
3.1 Study Area .........................................................................................................................10
3.2 Sampling Unit, Method and Distribution...........................................................................13
3.3 Sample Size Estimation .....................................................................................................15
3.4 Efficient Design Sample Size Estimation ..........................................................................17
3.5 Chapter Summary ..............................................................................................................17
4 Survey Design ...........................................................................................................................18
4.1 RP Survey ..........................................................................................................................19
4.2 Detailed Personal Information ...........................................................................................19
4.3 Detailed Household Information........................................................................................21
4.4 Activity Schedule Information ...........................................................................................22
4.5 SP Survey ...........................................................................................................................24
4.6 Mode Availabilities in SP Survey ......................................................................................25
4.7 Level of Service Attributes of SP Survey ..........................................................................26
4.8 TDM Policy Attributes of SP Survey ................................................................................28
4.9 SP Experimental Design aimed for Pilot Survey ...............................................................30
4.10 Pilot Survey Result ............................................................................................................30
4.11 Ngene Code Redesign ........................................................................................................31
4.12 SP Survey ...........................................................................................................................32
4.13 Chapter Summary ..............................................................................................................35
5 OFF-SET Survey Results ..........................................................................................................36
5.1 Survey Implementation Summary .....................................................................................36
5.2 RP Sample Descriptive Statistics of Household Attributes ...............................................36
5.3 RP Descriptive Statistics of Individual Attributes .............................................................39
5.4 Mode Choice Comparison between RP, SP and TTS data ................................................42
5.5 Telecommuting and Flexible work Hour ...........................................................................45
5.6 Chapter Summary ..............................................................................................................47
vii
6 Mode Choice Modelling ...........................................................................................................48
6.1 Data for Empirical Investigation and Generating LOS Attributes.....................................48
6.2 Econometric Model Formulation for Separate RP and SP Model .....................................49
6.3 Empirical RP Mode Choice Model ....................................................................................50
6.4 Empirical SP Mode Choice Model ....................................................................................52
6.5 Econometric Model Formulation for Joint RP/SP Mode Choice Model ...........................54
6.6 Joint RP/SP Mode Choice Model ......................................................................................57
6.7 Chapter Summary ..............................................................................................................59
7 Office based TDM Evaluation Tool (OFF-TET) Development ...............................................60
7.1 Tool Development Process ................................................................................................60
7.1.1 Forecasting with Joint RP/SP model .....................................................................60
7.1.2 Model Calibration ..................................................................................................61
7.1.3 Application of OFF-TET .......................................................................................62
7.2 Software Interface ..............................................................................................................63
7.3 Example Application .........................................................................................................66
7.4 Chapter Summary ..............................................................................................................68
8 Conclusions ...............................................................................................................................69
8.1 Research Contributions ......................................................................................................69
8.2 Future Research .................................................................................................................70
8.3 Chapter Summary ..............................................................................................................71
References ......................................................................................................................................72
Appendix A : OFF-SET( A Customized Individual Specific Web based Survey Software) ......74
Appendix B : Ngene Utility Equations .......................................................................................103
viii
List of Tables
Table 2-1 Basic TDM Policy Categories (Litman, 2003). .............................................................. 6
Table 3-1 Sample Distribution for the Survey .............................................................................. 14
Table 4-1 Level of Service Sources .............................................................................................. 27
Table 4-2 Experimental Design Choices, Attributes and Levels .................................................. 28
Table 4-3 Experimental Design Choices, Attributes and Levels .................................................. 29
Table 4-4 Comparison of MNL Efficiency Measures of Experimental Design ........................... 32
Table 4-5 One Typical Scenario of SP Questionnaire (Five Available Modes) ........................... 33
Table 4-6 One Typical Scenario of SP Questionnaire (Seven Available Modes) ........................ 34
Table 4-7 Confidence Rating Question with each of SP Scenario ............................................... 35
Table 5-1 Survey Respondents Summary ..................................................................................... 36
Table 5-2 Sample Descriptive Statistics of Household Attributes................................................ 37
Table 5-3 Sample Descriptive Statistics of Individual Attributes ................................................ 40
Table 5-4 Comparison between RP, TTS and SP Data ................................................................ 45
Table 6-1 RP Mode Choice Model ............................................................................................... 51
Table 6-2 SP Mode Choice Model................................................................................................ 53
Table 6-3 Joint RP/ SP Mode Choice Model ................................................................................ 57
Table 7-1 Start-up Page of the Tool .............................................................................................. 64
Table 7-2 Input Page of the Tool (Before Input) .......................................................................... 65
Table 7-3 Input Page of the Tool (After Input)............................................................................. 67
ix
List of Figures
Figure 3-1 Municipalities in the Greater Toronto Area ................................................................ 11
Figure 3-2 Region of Peel Boundaries .......................................................................................... 12
Figure 3-3 Sample Size Estimation............................................................................................... 17
Figure 4-1 RP data ........................................................................................................................ 18
Figure 4-2 SP data ......................................................................................................................... 19
Figure 4-3 Data Model for Detailed Personal Information ........................................................... 20
Figure 4-4 Data Model for Detailed Household Information ....................................................... 22
Figure 5-1 House Tenure Distribution .......................................................................................... 38
Figure 5-2 Total Household Income Distribution ......................................................................... 38
Figure 5-3 Gender Distribution ..................................................................................................... 39
Figure 5-4 Age Distribution .......................................................................................................... 41
Figure 5-5 Highest Level of Education ......................................................................................... 41
Figure 5-6 Employment Status ..................................................................................................... 42
Figure 5-7 RP Mode Share ........................................................................................................... 43
Figure 5-8 TTS Mode Share, 2011 ............................................................................................... 44
Figure 5-9 SP Mode Share ............................................................................................................ 44
Figure 5-10 Day of the Week Respondent’s willing to Telecommute ......................................... 46
Figure 5-11 Flexible Work Hour .................................................................................................. 46
Figure 7-1 Application of OFF-TET............................................................................................. 63
Figure 7-2 Base Case Mode Share (Before Testing any Policy) .................................................. 66
x
Figure 7-3 Mode Share Comparison (After Testing any Policies) ............................................... 68
xi
Glossary
DEFF Design Effect
GTA Greater Toronto Area
GTHA Greater Toronto and Hamilton Area
LOS Level of Service
MNL Multinomial Logit
MSAT Mode Shift Analysis Tool
OFF-TET Office based TDM Evaluation Tool
OFF-SET OFFice based Survey for Evaluation Tool development
RP Revealed Preference
RUM Random Utility Maximization
SP Stated Preference
TAZ Traffic Analysis Zone
TDM Transportation Demand Management
TDMAP Transportation Demand Management Assessment Procedure
TRIMMS Trip Reduction Impacts for Mobility Management Strategies
TTC Toronto Transit Commission
TTS Transportation Tomorrow Survey
1
1 Introduction
Traffic congestion is one of the most common challenges faced by every city in the world. Since
traffic congestion is result of many interacting processes including transportation and land use
systems, there is no quick fix. If a new roadway infrastructure is built (i.e. expressway, overpass,
rapid transit) congestion may be temporarily alleviated. Unfortunately, the addition of roadway
infrastructures often increases latent demand for travel, ultimately returning the transportation
network back to the congested state. The phenomenon of latent demand is exasperated by the
increase in developments surrounding the newly-built infrastructure. These create further demand
for travel within the region. Therefore, for long-term congestion mitigation, it is imperative to find
an alternative solution that will reduce the demand for travel rather than simply adding more
capacity. A complementary solution to these is transportation demand management (TDM)
policies. TDM policies aim at changing people’s current transport mode choice by encouraging
them to move away from single occupancy vehicle (SOV) travel. Beyond the advantage of
reducing congestion, TDM policies have other benefit, including increased safety and emission
reduction (Litman, 2003).
According to the TDM study report of Transport Canada (2011), a number of Canadian
municipalities have been testing various TDM policies in the past decade. However, the evaluation
process of the effectiveness of these policies is still unclear. In order to assess performances of a
TDM policy, it is necessary to conduct a before and after survey to compare transportation demand
changes due to the execution of the policy. Various government and private organizations have
been conducting regional and local surveys to measure TDM policies. For example, the regional
transportation authority for the Greater Toronto Area (GTA), Metrolinx, has conducted baseline
and follow-up surveys in the Greater Toronto and Hamilton Area (GTHA) to determine the return
on investment, or impact, of the Smart Commute programming at the worksites (Transport Canada,
2011). Metrolinx has also conducted household level surveys to measure the performance of
school based TDM programs. In 2002, the City of Ottawa conducted a survey to understand the
travel behaviour of commuters. Their survey identified triggering factors that encourage
respondents to choose non-auto-centric transportation modes (public transportation, biking,
walking) instead of single occupancy vehicles. Similarly, the Central Okanagan Region and Vélo
Québec developed surveys to measure some specific TDM policies in 2004 and 2005 (Transport
2
Canada, 2011). In light of the above discussion it becomes clear that surveys are essential for
evaluating and understanding the effectiveness of any TDM policy. However, before and after
survey approach is not cost effective as it is necessary to implement the policy initially and then
maintain the particular TDM policy for an extended period prior to undertaking the performance
measurements. Many such policies selected for implementation may not be appropriate or optimal
for the specific context of application and the result may be wastage of resources. Furthermore,
other land use or transportation network changes may skew the results of the evaluation of any
TDM policy implementation. Moreover, sometimes, extrapolating the results of such performance
measurements of a TDM policy from a regional scale to a local scale can be challenging. So, it is
clear that there is a lack of tools or techniques that can accurately measure the performance of
various TDM policies prior to implementation.
The main objective of this thesis is to develop a tool that can evaluate employer-based TDM
policies before implementation. The tool is particularly developed for Region of Peel to select the
most effective TDM policies at the regional (region-wide) as well as local (at city level) scale and
for various penetration levels. This tool can measure the individual and combined effects of various
employer-based TDM policies. For example, the joint effect of an office based bike sharing
program and a car sharing program can be tested. Alternatively, the effects of adding bike access
facilities, showers, and locker facilities and the subsequent mode shift can be tested (individually
and combined) before implementing a bike share program at work locations.
1.1 Objectives
The objectives of this thesis are as followings:
1. Developing a comprehensive Revealed Preference (RP) and Stated Preference (SP) web-
based survey tool that can capture commuter’s responses to various TDM policies in terms
of commuting mode choice changes.
2. Implement the survey to collect data from the Region of Peel: considering only the
commuters who live in or outside Peel, but within the Peel Region.
3. To develop a stand-alone evaluation tool that can compare changes in aggregate
commuting mode shares in response to implementation of one or multiple TDM policies
at various level of penetration (percentage of the employers implementing the policies).
3
1.2 Methodology
The methodology of this thesis can be divided into four stages: 1) an RP-SP web-based survey tool
design; 2) organizing and conducting the survey; 3) developing mathematical specification for the
microscopic policy sensitive commuting mode choice model; 4) developing a TDM evaluation
tool. These four stages will be discussed in the following paragraphs:
The first stage is designing an RP-SP survey. A web based survey tool was developed by using
JavaScript and jQuery programming applications (Resig, 2009). The RP portion consists of four
parts: 1) detailed person information; 2) detailed household information; 3) activity schedule
information consisting of a household travel diary; and 4) socio-demographic information. The RP
section was built following the existing Transportation Tomorrow Survey (TTS) in order to keep
the potential data fusion option. For SP portion an experimental design approach was used. There
are several experimental design methods available: full factorial design, orthogonal design, and
efficient design. The efficient design technique was used in this study as it is the most appropriate
for data necessary to develop discrete choice models.
The second stage of the methodology is conducting the survey. The respondents were selected
using a probability-sampling method from a pool of marketing research panel members. The
survey is web based, and invitations to participate were sent through email.
The third stage is to develop mathematical specification for policy sensitive commuting mode
choice model. The model is developed using a joint RP-SP specification to account for the
systematic bias associated in SP data. Codes were written in GAUSS to estimate the discrete choice
models
The fourth and final stage involves incorporating the mathematical model developed in stage three
into a comprehensive TDM evaluation tool. The tool prototype was designed as a spreadsheet
based software package that provides the user with detailed information regarding changes to
aggregate modal share in context of the implementations of various TDM policies. The tool utilizes
simulation-based approaches to predict impact of different policy penetration levels (percentage
of employers implementing the policies).
4
1.3 Thesis Outline
The thesis consists of eight chapters. Chapter 1 shows the motivation, objectives and
methodologies of the thesis. Chapter 2 presents a brief discussion of the previous literature and
critical analysis of previous research works. Chapter 3 provides the detail description of the study
area and sampling unit, method and distribution. Chapter 4 reveals each component of the survey
elaborately including revealed preference (RP) and stated preference (SP) design component.
Chapter 5 describes the descriptive analysis of RP and SP data. Chapter 6 shows RP model, SP
model and joint RP/SP model results. Chapter 7 reveals detail description of Office based TDM
Evaluation Tool (OFF-TET) and how to use this tool. Chapter 8 summarizes the outcome of this
thesis and it discusses about its limitations and the scope for the future work.
1.4 Chapter Summary
This chapter provides a synopsis of the full thesis. Objectives and a brief methodology are
described here, which will help to understand the total framework of the thesis. Then, the
methodology section is followed by thesis outline where some main features of each chapter are
listed. Next chapter, Chapter 2, will show a critical review of previous literatures.
5
2 Literature Review
2.1 Transportation Demand Management (TDM) Policy
TDM refers to the strategies that change passenger travel behaviours have several outcomes such
as improved road congestion, increased safety as well as reduced emission. The main target of
TDM is to reduce single occupant vehicle usages and promote the use of public transit and active
modes, e.g., biking, and walking. TDM policies tend to reduce the total travel demand and
redistribute demand across different modes over time-of-day. TDM policies mainly work as tools
for mobility management which work effectively when policies are implemented
comprehensively.
2.2 Classification of TDM Policies
There are a number of TDM policies which have been used all over the world. These policies can
be divided into three basic categories: employer-based, regional, and system wide. Employer based
TDM policies are generally work location based policies and are implemented by the employers.
Regional TDM policies are a combination of land use policies and home location based TDM
polices. System wide transportation policies target the whole or parts of the transportation system.
Some examples of these three categories are tabulated in Table 2.1.
6
Table 2-1 Basic TDM Policy Categories (Litman, 2003).
Employer Based Regional System Wide
Alternative Work Schedules
Mixed Land-Use Transit Priority
Carpool Matching Urban Densification
Parking Restriction
Vanpool Service
Transit-Oriented
Development
HOV –HOT Lanes
Parking Management
Complete Neighborhood
Rapid Transit
Monetary Incentives (Transit
Fare Subsidy)
Education and Social
Marketing
Road pricing
Telecommuting Parking Restrictions
Improved Way Finding and
Travel Information
Dissemination
Education and Promotion Traffic Calming
2.3 TDM Policy Evaluation Methods
Evidence of model development for TDM policy evaluations is sporadic in literature. Generally,
models are often presented for isolated policy evaluations. However, for doing effective evaluation
comprehensively combined (suits of policies) TDM policy evaluation is essential. Although,
comprehensive and joint effects of various TDM policies have been evaluated, model development
process and underlying assumptions are often hidden or absent in literature. Some noteworthy
examples of comprehensive model-based TDM evaluation tools are COMMUTER, TRIMMS and
SAT. Almost all of these use ad-hoc modelling techniques. Generally, mode choice model
components of such tools are not estimated by using appropriate data. Moreover, transferability of
such tools from one place to another place for application is questionable.
Though existing literature comprises a number of studies on TDM policies, there is a distinct lack
of research on TDM evaluation tools, more specifically, model based TDM evaluation methods.
For example, a great number of research focused on investigations of telecommunication, bike
7
sharing demand and related travel behaviour. However, evaluation method of certain policy or
combined effect of some policies is not clearly revealed in any research. USA Environmental
Protection Agency (EPA) developed software called ‘COMMUTER’ for evaluating TDM policies.
However, there is a lack of adequate literature regarding the application and effectiveness of such
type of software. We can divide the TDM evaluation methods discussed in various transportation
literatures into two categories:
1. Data based evaluations: Based on before and after survey.
2. Model based evaluation: Mathematical models estimated to forecast the impacts of TDM
policies.
2.4 Data based Evaluation
In this method survey is performed before and after TDM implementations. In the next step,
transport planners assess the performance of that particular policy. The planners use the before and
after data and compare the effects of TDM policies. This method is particularly suitable for pilot
testing. However, sometimes the evaluations based on observed data (e.g. vehicle kilometers
travelled, trip rates) from before and after data collection programs may be misleading due to
inappropriate focus on particular factors. The change in performance may happen also for some
other reasons those are not considered in the before and after survey.
2.5 Model based Evaluation
Model based evaluation is performed based on mathematical models to forecast the impact of
TDM policies. Usually, revealed preference (RP) and stated preference (SP) surveys are conducted
to collect required data. Commuting mode choice models are specified depending on data. These
models are based on the random utility maximization theory (RUM) which belongs to the family
of discrete choice modelling.
The pressing question is which evaluation method works better. Data based evaluation, which
basically depends on before and after survey, has several drawbacks. It often evaluates the policies
individually. However, the impacts of TDM policies may overlap which may result double
counting of the mode switching behaviour. Therefore, model based evaluations help to consider
a combination of different TDM policies. Such technique allows evidence-based planning and
systematic investigation. As a result, it allows investigating both individual and compound impact
8
of different TDM policies. Therefore, in most of the cases performance of model based evaluation
works better than the data based evaluations.
2.6 Mathematical Models for TDM Policy Evaluation Tools
2.6.1 COMMUTER
COMMUTER is a TDM Evaluation Tool developed by U.S. EPA. They used Logit model to
capture mode switching behaviour in response to employer-based TDM policies. Resulting mode
switching effects are quantified as VKT savings and emission reductions. COMMUTER did not
collect extensive local data. It is required to collect RP data to estimate those parameters.
However, parameters of the Logit mode choice models, used in COMMUTER, are imputed or
taken from other studies. In different areas, those parameters have been updated on an ad-hoc
basis. In COMMUTER, different set of parameter values are recommended for different states in
USA. In reality those values should be estimated by appropriate econometric mathematical models
(Herzog et al., 2002).
2.6.2 MSAT
Mode Shift Analysis Tool (MSAT) is a TDM evaluation tool used in Pasadena, California. This
tool is primarily based on the COMMUTER software. It uses pivot point Logit formulation
whereas COMMUTER simply uses Logit models. Therefore, MSAT is mainly an update of
COMMUTER. MSAT extrapolates mode shift based on base year modal shares rather than using
an econometric model-based approach. This tool is developed as an add-on to TransCAD
assignment software, to capture updated Level of Service (LOS) values. This tool has some
inherent limitations. It is limited in terms of potential policies for testing, e.g., commute trip
reduction programs, transit service improvements, and parking policy/pricing.
2.6.3 TRIMMS
Trip Reduction Impacts for Mobility Management Strategies (TRIMMS) is a tool developed by
Washington State DOT. They use a Transportation Demand Management Assessment Procedure
(TDMAP) which uses TRIMMS. TRIMMS, similar to MSAT, acts as an add-on to a regions four
stage model. TRIMMS uses zone based level-of-service values and mode shares and thereby
updates mode split based on TDM polices. However, TRIMMS only allows for a single employer
or small set of employers to test polices. TDMAP integrates with TRIMMS allows for region wide
9
planning approach which connects with a four stage modelling approach. The mode split updating
follows a more robust econometric modelling approach than those of MSAT and COMMUTER
V.2. It captures more complex development which considers elasticity and cross elasticity of
modal demand and therefore, results in a more complete picture of travel behaviour. However, this
tool has some constraints. It can forecast short and medium-term (3-5 years) effect, but lacks
accuracy in long-term forecasting (Concas and Winters, 2009).
2.7 Shortcomings in existing Surveys to Develop TDM Evaluation Tool for the Region of Peel
There are various surveys available in Greater Toronto Area (GTA), e.g., Transportation
Tomorrow Survey (TTS). However, none of these are sufficient to develop policy sensitive mode
choice model and TDM evaluation tool for the Region of Peel, especially for policies that are not
implemented yet. Metrolinx’s smart commute dataset has opinion based data on TDM policies.
However, to develop a robust evaluation tool experimental design based data are necessary.
Opinion based survey cannot properly capture travelers’ stated responses as alternative level of
service attributes (travel time and costs) along with many other related factors resulting from
implementations of TDM policies are not presented to the respondents.
2.8 Chapter Summary
In this chapter various past studies have been discussed briefly. Also, the limitations of many
existing modelling formulations are presented here. Currently, most of the regional municipalities
are depending on before and after survey for TDM policy evaluation. The required data type and
data limitation is also discussed in this Chapter. In the next chapter detail study area, sampling unit
and sampling distribution will be discussed.
10
3 Background of the Study Area
Region of Peel consists of Mississauga, Brampton, and Caledon and serves 1.3 million residents
and approximately 88,000 businesses (Municipality of Region of Peel, 2004). There is a rapid
growth in population and employment over the last three decades in this region and has resulted
in increasing traffic congestion in Region of Peel. Moreover, a continued projected growth will
further exasperate congestion. These concerns cannot be managed solely by considering
infrastructure investment. Therefore, Peel region has identified TDM as a potential means of
addressing these concerns in a number of planning documents. However, presently there is no
evaluation tool available that can evaluate the combined and individual effect of TDM policies.
Therefore, Region of Peel felt a need to develop such a tool to measure the relative effectiveness
of its policies prior to implementation. To develop such tool it is necessary to do econometric
modelling and to do econometric modelling it is required revealed preference (RP) and stated
preference (SP) data. Therefore, to collect this data a web-based survey tool was developed. The
name of the survey is OFF-SET (OFFice based Survey for Evaluation Tool development)
3.1 Study Area
The study area is the Region of Peel: Mississauga, Brampton, and Caledon. The target population
is those who work in Region of Peel. As per the place of residences of the commuters, there are
two types of commuters. First type is those who live and work in Peel; the other type is those who
live elsewhere, but work in Peel. As such, to get a representative sample it is necessary to include
all the areas from where the commuters come to the Region of Peel. In this project the study area
is not limited to only Region of Peel. For getting the data of the commuters, who live outside Peel,
it was necessary to do household level survey from some other places who are commuting to Peel
including: Greater Toronto Area, Region of Durham, Halton, York, Niagara, Waterloo, City of
Hamilton, Guelph, Wellington, Barrie, Kawartha Lakes, Orillia, Dufferin, Brantford, Brant,
County of Simcoe, County of Peterborough, Town of Orangeville and, Peterborough. The GTA
consists of the City of Toronto (Etobicoke, York, Downtown, Toronto, East York and
Scarborough), and the regions of Peel, Durham, Halton and York (Figure 3-1). The region of Peel
Boundaries are shown in the Figure 3-2.
11
Figure 3-1 Municipalities in the Greater Toronto Area1
1 http://en.wikipedia.org/wiki/List_of_municipalities_in_the_Greater_Toronto_Area
12
Figure 3-2 Region of Peel Boundaries2
2 http://opendata.peelregion.ca/data-categories/regional-geography/municipal-boundaries.aspx
13
3.2 Sampling Unit, Method and Distribution
The survey designed in this study is a household level survey, and as such the sampling unit of this
survey is the “Household”. According to Richardson et al. (1995), there are some criteria to
categorize a sampling method as random: sample from each unit should be collected individually
and each household in the target population should have equal probability of being selected. In
this survey a probability based sampling technique is used. Since all of the samples have been
collected by random, sampling method and all the modes have non-zero probability of selection,
the sampling method refers as probability sampling. There are 6 types of probability sampling
techniques. Since simple random sampling method is the simplest method and this method fits
with this survey, simple random sampling method is used for OFF-SET (Richardson et al. 1995).
Several pieces of prior information are collected, e.g., total number of commuting trips to Peel
from each municipality within the study area and the population of the municipality. Such
information is used to calculate the hit ratio (percentage of the population for a given region who
work in peel) and the percentage of the total commuting trips to Peel from each origin. In Table 3-
1 detail data and calculation have been shown (Richardson et al. 1995).
As mentioned earlier, the simple random sampling technique is used in this study; after collecting
all the samples the distribution is compared with the percentage of the total commuting trips to
Peel from each origin (Table 3-1). This similar distributions show the random sampling nature of
the survey data collection.
14
Table 3-1 Sample Distribution for the Survey
From Total
Number of
Commuting
Trips to Peel
from
Municipality
Number of
Municipality
Residents
Percentage
of the
Population
for a
Given
Region
who Work
in Peel
(Hit Ratio)
Total
Percent of
GTA
Population
Percentage
of the total
Commuting
Trips to
Peel from
Each
Origin
City of Toronto 69700 2,616,800 2.66% 0.82% 14.22%
Region of Durham 5900 608,200 0.97% 0.07% 1.20%
Region of York 27800 1,032,700 2.69% 0.33% 5.67%
Region of Peel 300500 1,297,600 23.16% 3.53% 61.33%
Region of Halton 49600 502,000 9.88% 0.58% 10.12%
City of Hamilton 8700 519,800 1.67% 0.10% 1.78%
Region of Niagara 1700 431,500 0.39% 0.02% 0.35%
Region of Waterloo 4800 507,500 0.95% 0.06% 0.98%
City of Guelph 2400 121,700 1.97% 0.03% 0.49%
City of Wellington 2800 56,800 4.93% 0.03% 0.57%
Town of Orangeville 4600 28,000 16.43% 0.05% 0.94%
City of Barrie 2000 135,800 1.47% 0.02% 0.41%
County of Simcoe 5200 278,000 1.87% 0.06% 1.06%
City of Kawartha
Lakes
300 73,300 0.41% 0.00% 0.06%
City of Peterborough 100 78,800 0.13% 0.00% 0.02%
County of
Peterborough
100 43,000 0.23% 0.00% 0.02%
City of Orillia 0 30,700 0.00% 0.00% 0.00%
City of Dufferin 3000 28,800 10.42% 0.04% 0.61%
City of Brantford 600 93,600 0.64% 0.01% 0.12%
City of Brant 200 35,600 0.56% 0.00% 0.04%
Total 490000 8,520,200
15
3.3 Sample Size Estimation
Sample size estimation is the most important step prior to starting the survey. It is essential to
balance between sampling error and sample size. Sample size and sampling error are inversely
proportional. If sufficiently larger sample size is collected it may increase sampling accuracy.
Despite the use of a larger sample size, the data quality may not be good enough due to problematic
data collection techniques. On the other hand, using a small sample size may bring significant
variability in the data. Since, data collection is an expensive venture, and it is essential to seek a
balance between the sample size and accuracy. There are significant differences between
estimation of sample size for random sampling method and any other method. Estimation of
sample size for random sampling method is straightforward; however it is based on some
assumptions; these assumptions will be discussed in the following paragraphs. (Richardson et al.
1995).
Calculation of sample size is associated with three subsequent steps. The basic mathematical
equations are formed on the basis of central limit theorem. However, for discrete variables it will
be applicable for certain proportions. The general equation of standard error is given by:
�. �(�) = �� − �
�∗�(1 − �)
�(3 − 1)
Here, N means the total target population, n means the final sample size and p means the
proportion. It is necessary to assume the level of confidence or sampling error range prior to
calculating the standard error. For this survey 95% level of confidence was assumed which would
have a sampling error of less than 5 percentage points. More clearly, it has 95% probability that
the sampling error is will be less than 5% of sample mean. For example if 60% respondents choose
drive mode the actual choosing probability is 60%+/-5%.
From the definition of standard deviation (�) the mathematical formulation of standard score (z)
can be given by:
� =� − μ
�(3 − 2)
Here in equation 3-2 μ means mean and X is a score or value. By more simplifying equation 3-2
it can be written by:
16
�. �(�) =���������������
�(3 − 3)
�. �(�) =5
1.96= 2.551%(3 − 4)
Now the required sample size can be obtained by using equation 3.5. Here, population variability
of P=0.5 was assumed following the other similar types of studies (Idris 2013; Papaioannou 2014)
�� =�(1 − �)
�. �(�)�=. 5(1 − .5)
0.026�= 385(3 − 5)
Now it is required to adjust for the population.
�� = ���
� + ��= 385
8,520,200
8,520,200 + 385= 385
Then this sample size is required to adjust for the survey data collection method. It is required to
multiply it with “design effects”. Design effect is a factor which shows the relation between the
variance in a particular sample design with the variance of simple random sampling (Kish, 1965).
In this survey proportionate stratified sampling method was used. The Design Effect Factor
(DEFF) for a random sampling procedure is usually DEFF=1 (Idris 2013; Papaioannou 2014).
�� = ���� ∗ ��
�� = 1 ∗ 385 = 385
Therefore, the required sample size should be 385 for this survey. Since there is a possibility of
invalid data input and sampling error, the total number of target sample size should be larger than
385 so that after cleaning all data at least 385 respondent’s data can be compiled.
17
3.4 Efficient Design Sample Size Estimation
For the SP part of this survey an efficient design technique is used. In efficient design output S
estimate was found 779.62 (780). S estimate tells how many respondents are required to achieve
desired D-error. Therefore, sample size should be 780 to achieve required D-error (3.14) in
experimental design. Details of experimental design, S estimate, and D estimate will be discussed
in Chapter 4. Now, there is a decision to be made that which sample size estimation should be
considered from described two: using standard error and S-estimate. It is found that the S-estimate
in experimental design efficiency measurement output is greater than that from the standard error
approach. It is safe, therefore, to use greater one (780) which will lead to a good data quality.
Figure 3-3 Sample Size Estimation
3.5 Chapter Summary
This chapter provides a brief overview about the study area, Region of Peel. The sampling unit,
method and distribution are the most critical methodological decisions during the survey design
step. This chapter reveals details regarding sampling unit, method and distribution. Also, a
thorough calculation of sample size estimation is shown here. Then sample size is estimated by
using standard error and is compared with the s-estimate of experimental design. This chapter
provides a preliminary outline of the survey implementation, but more details will be shown in
Chapter 4, in which every component of RP and SP survey will be discussed elaborately.
18
4 Survey Design
The survey was conducted at the household level. However, the respondent should work in Region
of Peel (City of Mississauga or City of Brampton, or the Town of Caledon). Since the objective of
this study is the development of an employer-based TDM evaluation tool for Region of Peel, the
respondents also had to fill out a stated preference part of section regarding their personal commute
to work. The survey had two major components: a RP questionnaire and a SP questionnaire. In the
RP questionnaire, information was collected regarding detailed personal socio-demographic
information, detailed household information, and activity schedule information (household travel
diary). In the SP questionnaire, efficient experimental design method was applied. The SP has two
segments: Level of Service (LOS) and TDM policy based attributes. In that experimental design,
all level of service information, except transit fare, was kept constant among the scenarios, because
the level of service values were respondent-specific and it was difficult to predict the changes
unless the region has a defined policy set up to change the travel cost. The effect of providing
incentive for transit passes to the employees in the Region of Peel will be tested here. Hence transit
fare cost varied in the SP scenarios. Conversely, TDM policy based attributes change over the
scenarios according to experimental design. In Figure 3.1 and 3.2 overview of RP and SP data is
shown.
Figure 4-1 RP data
19
Figure 4-2 SP data
4.1 RP Survey
In the RP part of the survey, three categories of data were collected: detailed personal socio-
demographic information, detailed household information, activity schedule information. The
detail RP portion of the questionnaire is shown in the Appendix A.
4.2 Detailed Personal Information
In the beginning of the survey detailed personal information was collected. The following
information was collected from all household members whose age is over 12. The data model is
shown in Figure 3.3.
Total number of people who live in your household (including respondents)
Number of people live in your household over the age of 12 (including respondents)
Nickname of the household member (to refer in survey)
Age
Gender
Personal income
Highest level of education
20
Employment status
Driver’s license information
For fulltime, part-time or student:
o Work location (Google map is incorporated into the survey and a customized search
option was included.)
o Workplace or school daily parking cost
o Commuting mode choice
o Occupation
Figure 4-3 Data Model for Detailed Personal Information
21
4.3 Detailed Household Information
Detailed household information was collected from the respondent. The home location, dwelling
type and various ownership (auto, transit pass, bike) information was collected by this part of the
survey. Respondents provided the following information while filling out this section.
Home tenure status (own, rent, other)
Location of residence (Google map was incorporated into the survey and a customized
search button was included)
Dwelling type
Vehicle ownership information
o Total number of vehicle and for each vehicle:
Manufacturer
Model
Primary Driver
Transit pass ownership information
o Total number of transit passes
Transit agency
Primary user
Bike ownership information
o Total number of bikes
Reason for using bike
Primary user
22
Figure 4-4 Data Model for Detailed Household Information
4.4 Activity Schedule Information
A detailed activity schedule was collected in the survey for both commuters and non-commutes
for the last weekday workday. The travel diary includes activity purpose, location, and start time
duration, and joint activity with household member.
Number of trips each household member (over age 12) made on the last weekday
Trip origin
Origin activity purpose
Trip destination
Destination activity purpose
Joint activity information (trip made by other household member)
Departure time for this trip
23
Primary mode associated with this trip
o Auto-driving (information regarding the vehicle that was used for the trip was
collected)
o Carpool (information regarding the trip and driver information was collected)
o Auto-passenger (information regarding the vehicle that was used for the trip and
driver information was collected)
o Public transit: bus, streetcar, and subway (information type: agency, transit route,
transfer information, transfer agency and route)
o Subway park and ride (information type: details of the vehicle that was used for
the trip, boarding and alighting subway station information, connectivity with final
destination (agency, transit route, transfer information, transfer agency and route))
o Go train by walk access (information type: boarding and alighting subway/GO
train station, connectivity with final destination (agency, transfer and route
information , if public transit))
o Go train with public transit access (information type: for public transit: agency,
transit route, transfer information, transfer agency and route; for go train: boarding
and alighting station, connectivity with final destination (agency, transfer and route
information, if public transit))
o Go train park and ride (information regarding the vehicle that was used for the trip,
go train: boarding and alighting station, connectivity with final destination (agency,
transfer and route information, if public transit))
o Public transit (bus, streetcar, and subway) with bike access (information type:
agency, transit route, transfer information, transfer agency and route)
o Biking to the destination
o Walking to the destination
24
4.5 SP Survey
In the SP survey part, several hypothetical scenarios were presented to respondents. These
scenarios will help to evaluate the effect of various TDM policies. The SP scenarios consist of two
parts: level of service values (time, cost, and distance) and TDM policy attributes. The levels of
service attributes were individual specific. The following information was shown to each
individual.
Total drive time
Transit walk/ bike time
Transit wait time
Total time traveling in the transit vehicle
Travel cost
Travel distance (for walk and bike mode)
According to the TDM study report of Region Peel (2004) the regional municipality of Peel has
been testing various TDM policies for the past decade. As one of the main objectives of this thesis
is to evaluate TDM policies, it is necessary to include an exhaustive set of TDM policies so that
we can test the effect of those policies individually and jointly. An exhaustive set of TDM policies
is provided below.
Daily versus monthly parking
Daily parking cost
Indoor car parking facilities at workplace
Likelihood of finding a parking spot within 5 minutes’ walk to work place (due to parking
reductions)
Sheltered bike parking facilities at workplace
Showers and changing rooms at workplace
Employer owned bikes available to rent
25
Workplace with bike friendly access facilities (Ramps or other means for easy access with
a bike)
Employer provides incentive for Region of Peel transit passes (Miway or Brampton
Transit)
Emergency ride home program.
Car share program
4.6 Mode Availabilities in SP Survey
To present reliable and realistic SP scenarios is a challenging task. In this study, a total of seven
feasible modes were selected: auto driver (drove household vehicle to destination), auto passenger
(dropped off by household member), carpool, local transit walk access, transit bike on board, bike,
and walk. For considering the availability of all seven modes a set of rules have been defined the
survey. At the first part of the survey respondents detail household auto and bike ownership
information
Auto drive –if household owns a car and the respondent owns a driver’s license
Auto passenger - if household owns a car
Carpool – always available
Local transit walk access – depends on congested fare based transit network assignment
model result regarding the availability of local transit availability.
Transit bike on board –if household owns a bike and also depends on congested fare based
transit network assignment model result regarding the availability of local transit
availability
Bike –if commuting distance is less than 10 km and household owns a bike
Walk - if commuting distance is less than 3 km
26
4.7 Level of Service Attributes of SP Survey
LOS origin-destination matrices are produced by various assignment models and are used to
determine the expected travel time for a given origin-destination pair. A postal code to zone look
up table was used to determine the zones for both the origin (home location) and destination (work
location) of each respondent. For zone definition TTS (2011) zonal system was used. By using
second ordered linear approximation assignment with background transit assignment model auto
drive LOS values were generated. Link based probabilistic shortest path algorithm was used to
calculate drive cost. Fare based congested transit assignment model was used to generate transit
LOS values. The origin-destination LOS tables could then be applied for showing five attributes
over the six scenarios: total drive time, transit walk/ bike time, transit wait time, total time traveling
in the transit vehicle, travel cost. Over the six scenarios, however, the level of service was same
for the corresponding respondent except transit fare. Since employer of region of Peel may provide
some incentives to employees’ transit passes (Miway or Brampton Transit), transit fare is varied
across the six scenarios to capture the effect of changes. To improve the travel time and auto-cost
it requires massive infrastructural change that was not the aim of this study (or possible for
employers to implement). The aim is to evaluate TDM policies at the employer level and to observe
their effect. Therefore, the levels of TDM attributes were changed over the scenarios.
27
Table 4-1 Level of Service Sources
Attribute\Mode Drive Auto
Passenger Carpool Transit Transit
Bike Access (Bike on Board)
Bike Walk
Transit Walk/Bike Time
--- --- --- EMME 2011
(Walk time/3)and EMME 2011
--- ---
Total Drive Time
EMME 2011
EMME 2011
Drive time*1.2
__
__ __ __
Total in Vehicle Transit time
__ __ __ EMME 2011
EMME 2011
__ __
Travel Cost EMME 2011
(Drive Cost/2)
(Drive Cost/2)
Home Location: Toronto $5.70, Peel $2.80 (Over the scenarios, this values are reduced based on the incentive)
Home Location: Toronto $5.70, Peel $2.80 (Over the scenarios, this values are reduced based on the incentive)
Travel Distance
__ __ __ __ __ From Google maps (straight line distance in kilometers with one decimal space)
From Google maps (straight line distance in kilometers with one decimal space)
28
4.8 TDM Policy Attributes of SP Survey
Ten TDM policies will be tested in this study. In the experimental design appropriate levels are
assigned to each policy. Excluding parking cost, all levels are binary choices (yes and no). Parking
cost depends on the TDM policy of providing a daily vs. monthly parking payment structure. In
the experimental design, if daily parking shows up then parking cost will be either $2 or $4. On
the other hand, if monthly parking shows up then parking cost will be either 36.00 (daily rate:
$1.80) or $72.00 (daily rate: $3.60). Table 4.2 and 4.3 shows experimental design choices,
attributes and levels.
Table 4-2 Experimental Design Choices, Attributes and Levels
Attribute\Mode Drive Auto
Passen
ger
Carpool Transit Transit Bike
Access (bike on
board)
Bike Walk
Daily vs. monthly
parking cost
Monthly (1)/ daily (0) __ Monthly (1)/ daily
(0)
--- --- --- ---
Employer pays for
region of Peel (Miway or
Brampton Transit)
transit passes
Yes(1)/No(0) Yes(1)/No(0)
Parking cost (daily cost) Depend on row one.
For Daily either $2 or
$4.For Monthly
$36.00 (Daily rate:
$1.80) Monthly $72.00
(Daily rate: $3.60).
___ Depend on row
one. For Daily
either $1 or $2.For
Monthly $0(Daily
rate: $0)For
Monthly $36.00
(Daily rate: $1.80)
--- --- --- ---
29
Table 4-3 Experimental Design Choices, Attributes and Levels
Attribute\Mode Drive Auto
Passenger
Carpool Transit Transit
Bike
Access
(bike on
board)
Bike Walk
Indoor car parking Yes(1)/
No(0)
Yes(1)/No(0) --- --- --- ---
Sheltered bike parking at
your workplace
--- --- --- Yes(1)/
No(0)
Yes(1)/
No(0)
---
Showers and changing
rooms at your workplace
--- --- --- Yes(1)/
No(0)
Yes(1)/
No(0)
__
Employer owned bikes
available to rent (for
going out to lunch)
--- Yes(1)/No(0) Yes(1)/No(0)) Yes(1)/
No(0)
Yes(1)/
No(0)
Yes(1)/
No(0)
Yes(1)/
No(0)
Bike friendly building
access (ramps) at your
workplace
--- --- --- Yes(1)/
No(0)
Yes(1)/
No(0)
---
Likelihood of finding a
parking spot within 5
minutes’ walk to your
work place (due to
parking reductions)
100%
(1),
50%(0)
--- 100% (1),
50%(0)
--- --- --- ---
Emergency vehicle or ride
home program at your
workplace
--- Yes(1)/No(0) Yes(1)/No(0) Yes(1)/
No(0)
Yes(1)/
No(0)
Yes(1)/
No(0)
Yes(1)/
No(0)
Employee run car share
program at your
workplace (for business
related or short personal
trips)
--- Yes(1)/No(0) Yes(1)/No(0) Yes(1)/
No(0)
Yes(1)/
No(0)
Yes(1)/
No(0)
Yes(1)/
No(0)
30
4.9 SP Experimental Design aimed for Pilot Survey
In order to conduct efficient experimental design the Ngene software package was used (Choice
Metrics, 2014). Doing experimental design in Ngene requires writing the utility equations for each
alternative. It is required to use parameter values either from existing literature or from a small
scale survey. For this study, the Ngene codes were written based on the parameters values taken
from a small scale pilot survey. In the preliminary experimental design code, certain conditions
were set up to ensure that the choice experiment presented a realistic scenario to the respondent.
For example, carpool parking cost was constrained to be less than or equal to half the cost of
parking for a drive alone trip. This was done to ensure that a TDM policy did not explicitly
encourage driving over carpooling. Moreover, when car share was available, it was kept available
for all the four modes: carpool, auto passenger, local transit and park and ride. In the same way,
when car share was not available, it was made unavailable for all the four non-drive-alone modes:
carpool, auto passenger, local transit and park and ride. The same logic was used for bike share,
emergency ride home, shower, locker, fare, bike access, indoor parking and likelihood of finding
a parking spot within 5 minutes’ walk to work place. Finally, TDM policies were only included in
the utilities of those modes which would be affected by these TDM policies, including things like
parking cost, showers and bike storage facilities. The details Ngene code, including the utility
equations, are provided in Appendix B.
4.10 Pilot Survey Result
The Pilot survey was done in three stages. First, an internal pilot testing was tested among the
transportation graduate students of University of Toronto. Then, the pilot testing was performed
among the real respondents. At the end of the second pilot it was found that the survey completion
rate (completed responses/invited respondents) was only 7%. Therefore, another round of pilot
survey was done to get additional responses. At the end of third pilot it 282 people opened the link
leading to the screening question, 149 were qualified and lead to the actual survey and 20
completed the full survey. Therefore, for preliminary mode choice modelling we had 120 (20*6)
data point (6 scenarios for each response).
To revise the Ngene code it is required to estimate the parameters. For parameter estimation
Biogeme software was used (Bierlaire, 2003). The model framework was Multinomial Logit
Model (MNL Model). First, the full dataset was organized. In the dataset the Level of Service
31
(LOS) (travel time and cost) values were included according to their home and work locations.
Second, the rules discussed above were included for the availability of all seven alternative modes.
Then Biogeme code was written with the same utility function as was used in Ngene code. In
addition, LOS parameters and variables have been included in the utility function to check whether
the sign of travel time, cost as well as distance is negative.
Forward selection method was used during the variable selection process. In the preliminary model
it was found that the model with only LOS attributes was showing proper negative sign for time
and cost variables. As the pilot dataset is very small, it was hard to find the parameter estimates
that are statistically significant. Finally a model is selected where all parameters are showing
proper sign.
4.11 Ngene Code Redesign
During the pilot, in the SP Survey part, the parameters were taken from the small scale survey.
After developing commuting mode choice model using the pilot data the old parameters will be
replaced with the new parameters and the efficient design will be repeated. There are number of
factors should be considered during the experimental design. MNL D-error should be low. Also,
S-estimate should be reasonable. The S estimate is a rough estimate of sample size that means
number of respondents is needed to get desired efficiency (Rose and Bliemer, 2013). B-estimate
is a measure of the utility balance. Its standard value is 70%-90% (Ngene 1.1.2 user manual and
reference guide, 2014).
After replacing the coefficients the S-estimate was found too high (454,998). S-estimate will be
reasonable only the prior values are accurate. A wide variation of prior values makes the S-estimate
very large (Rose and Bliemer, 2013). Given the small sample size used in the pilot model
development the parameter estimates were not significant and thus are not necessarily accurate.
To compensate for this the Ngene utility equation were altered from the pilot model utilities. In
the pilot model LOS variables were included. This may be the reason behind this high S estimate.
To account for this, an alternate pilot model was developed using only the TDM policies, keeping
the same utility equation as Ngene. Using these parameter estimates the S estimate was found more
reasonable (780). Table 4-3 reveals the summary output of the efficient experimental design.
32
Table 4-4 Comparison of MNL Efficiency Measures of Experimental Design
MNL Efficiency Measures
Final Survey Pilot Survey
D error 3.14 6.85
A error 4.27 18
B estimate 23.06 86.26
S estimate 779.62 199048
From Table 4-3, it is clear that the D error is sufficiently low. Also, it is lower than initial
experimental design. Before the pilot the D-error was 6.85. Moreover, S estimate is also
reasonable. The S estimate is also close to the sample size that was calculated by hand. B estimate
is little bit low than standard (70-80%). There is no level of service attribute in the Ngene code
and the pilot data is fully dominant with drive mode. These are the possible reasons behind this B
estimate value (Choice Metrics, 2014). However, it is clear that a significant improvement of
MNL efficiency is found after replacing the pilot prior values in the Ngene code. The S-estimate
is found 780, which is quite reasonable.
4.12 SP Survey
Six scenarios are shown to the respondents. In all the scenarios all the LOS values were kept the
same except the transit fare. Since the main objective of this thesis is to evaluate TDM policies,
the levels’ of TDM policies are varied over the scenarios. Two sample SP scenarios are shown in
Table 4-5 and 4-6. These two scenarios are taken from two different respondent’s survey. The
main reason of showing two separate scenario is to show how number of modes change depending
on the household attributes, home, and work locations. Since it is tough for a respondent to process
all of the information in the table, a confidence-rating question is shown after each scenario (Table
4-7). It is possible to analyze only those data where respondents were highly confident.
33
Table 4-5 One Typical Scenario of SP Questionnaire (Five Available Modes)
34
Table 4-6 One Typical Scenario of SP Questionnaire (Seven Available Modes)
35
Table 4-7 Confidence Rating Question with each of SP Scenario
4.13 Chapter Summary
In this chapter a detail discussion on RP and SP survey is presented. Also, the mode availability in
SP surveys, level of service generating methods are described here. Finally, pilot survey
implementation and Ngene code redesign procedure have been discussed here. In the next chapter
descriptive statistics of OFF-SET survey will be discussed.
36
5 OFF-SET Survey Results
This chapter presents the sample descriptive statistics of the OFF-SET survey. Several important
summaries of the descriptive statistics will be provided as well, such as the survey respondent’s
summary and the household descriptive statistics.
5.1 Survey Implementation Summary
To conduct the OFF-SET survey a market research company was hired. They collected data by
sending survey invitations to people in their survey panel database. Once their survey panel was
exhausted, they hired respondents over the phone. The total time frame for the pilot data collection
was from September 1, 2014 to September 30, 2014. Similarly, the total time frame for final data
collection was from December 1, 2015- January 31, 2015. Around 32,500 invitations were sent,
which resulted in 835 complete responses. A detailed summary of the survey implementation is
provided in Table 5-1. From 835 data, 635 respondent’s data was used for the analysis and
modelling purposes, and 200 data is hold for validation.
Table 5-1 Survey Respondents Summary
Survey Information Value
Total Invitations sent 32,500
Total number of people who opened the email 10,865
Total number of people who qualified (screening
question)
2,867
Total Complete Responses 835
Pilot Survey Time Frame 1 month
Final Survey Time Frame 2 month
5.2 RP Sample Descriptive Statistics of Household Attributes
Table 5-2 depicts the sample descriptive statistics of the household attributes. Table 5-2 reveals
that 71.97% respondents have their own households and 26% rent a home. In terms of the number
of vehicles, the preliminary analysis of the collected dataset shows that a majority (41.73%) of the
households have one vehicle; while 39.21% have two vehicles. Table 5-2 reveals that the
household size is uniformly distributed. Table 5-2 also shows that 26.93% of households are two
person households, while 23.62% of sampled respondents are living in a three person households
37
and 23.94% are living in a four person household. In terms of total household income, the data
shows that 39.1% of the households have a total income of between 50,000 and 100,000 dollars
per year (Figure 5-2). Furthermore, at 32.8%, 100,000-150,000 dollar per year income represents
the second highest percentage in the sample (Figure 5-2). The preliminary analysis also shows that
the average number of people in a household is 2.93 and average number of daily trips for each
respondent in the household is 2.32.
Table 5-2 Sample Descriptive Statistics of Household Attributes
Variable Value Frequency Percentage
House tenure Own the home 457 71.97%
Rent the home 168 26.46%
Other tenure 10 1.57%
Number of Vehicle 0 31 4.88%
1 265 41.73%
2 249 39.21%
3+ 90 14.17%
Household Size
1 96 15.12%
2 171 26.93%
3 150 23.62%
4 152 23.94%
5+ 66 10.39%
Total Household
Income (dollar per
year)
0-50,000 68 10.8%
50,000-100,000 247 39.1%
100,000-150,000 207 32.8%
150,000-200,000 92 14.6%
200,000 or more 18 2.8%
Household Average Person 2.93
Person aged over 12 2.46
Number of daily trips 2.32
38
Figure 5-1 House Tenure Distribution
Figure 5-2 Total Household Income Distribution
0%
10%
20%
30%
40%
50%
60%
70%
80%
Own the home Rent the home Other tenure
Perc
enta
ge (
%)
Home Dwelling Type
House Tenure
0%
10%
20%
30%
40%
50%
Perc
enta
ge (
%)
Total Household Income
Total Household Income Distribution
39
5.3 RP Descriptive Statistics of Individual Attributes
This section of the chapter summarizes the RP descriptive statistics of individual attributes. As
shown in Table 5-2, the sample shows nearly equal percentage of male and female, 49.61% and
50.39% respectively. Therefore, they have equal representation in terms of sample size (Figure
5.3). The preliminary analysis of the RP dataset portrays that the majority of the respondents fall
inside two age group categories: 21-30 (26.30%) and 41-50 (25.67%). Since this is an employee
based survey, this type of age distribution was expected beforehand (Figure 5.4). In terms of
driving license, Table 5-2 shows that the majority (95.28%) of the respondents have a driver’s
license. It is found that a very high percentage of people have their Bachelor's degree (43.15%).
Finally, Figure 5-6 shows fewer observations of part-time workers (14.20%) and student workers
(3.31%), while also showing that a significant portion (82.49%) of the respondents are full time
worker (Figure 5-6).
Figure 5-3 Gender Distribution
49.61%50.39%
Gender Distribution
Male Female
40
Table 5-3 Sample Descriptive Statistics of Individual Attributes
Variable Value Frequency Percentage
Gender Male 315 49.61%
Female 320 50.39%
Age 20 or less 8 1.26%
21-30 167 26.30%
31-40 163 25.67%
41-50 131 20.63%
51-60 135 21.26%
61-70 28 4.41%
70 or more 3 0.47%
Driving License Yes 605 95.28%
No 30 4.72%
Education Elementary School 1 0.16%
Junior High School 3 0.47%
High School 85 13.39%
College 177 27.87%
Bachelor's Degree 274 43.15%
Master's Degree or Above 95 14.96%
Employment
Status
Fulltime Worker 523 82.49%
Part-time Worker 90 14.20%
Worker & Student 21 3.31%
41
Figure 5-4 Age Distribution
Figure 5-5 Highest Level of Education
0%
5%
10%
15%
20%
25%
30%
20 or less 21-30 31-40 41-50 51-60 61-70 70 ormore
Perc
enta
ge (
%)
Age
Age
0%
10%
20%
30%
40%
50%
Perc
enta
ge (
%)
Highest Level of Education
Highest Level of Education
42
Figure 5-6 Employment Status
5.4 Mode Choice Comparison between RP, SP and TTS data
As an initial indication to heavily auto-oriented mode choice behaviour within the Region of Peel,
the preliminary RP data analysis shows 84.09% of the respondents use single occupancy vehicles
for their daily commute. Only 3.31% respondents reported that they use carpool as a commuting
mode and 4.41% reported auto passenger, and 6.30% reported transit. Bike on board, bike and
walking modes share a similar percentage of 0.63% (Figure 5-7). Now, if this mode share will
match with other studies, it will be conclusive that the survey is capturing travel behaviour in a
proper way. TTS data represents a 5% random selection of households in the Greater Toronto and
Hamilton Area (GTHA). Therefore, TTS (2011) am peak commuting mode share is compared with
OFF-SET survey’s RP mode choice. TTS does not differentiate between carpool and auto
passenger. However, the combined mode share of carpool and auto passenger (8.11%), of TTS
(2011), is similar to OFF-SET survey data (Figure 5-8) with a combined 7% passenger mode.
Auto drive, transit, bike on board, bike and walk mode shares are also quite similar to TTS (2011)
am peak commuting mode shares.
In the stated preference survey an exhaustive set of TDM policies have been tested. The average
mode share of six SP scenarios is shown in Figure 5-9. It is found that respondents have a strong
craving to shift their single occupancy vehicle (SOV) modes to sustainable modes. Only 41.01%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Fulltime Worker Parttime Worker Worker & Student
Perc
enta
ge (
%)
Employment Status
Employment Status
43
of respondent want to remain in auto drive mode. In this study it is found that, 28.30% of the
sample respondents choose carpool, 14.20% respondents choose auto passenger and 10.48%
choose transit. An increase mode share percentage is observed in the sustainable modes.
Respondents showed an increased desire towards three other modes, such as bike on board, bike,
and walk (Figure 5-9). A detail comparison between RP, TTS and SP data is tabulated in Table 5-
3.
Figure 5-7 RP Mode Share
Auto-Drive84%
Carpool3%
Auto-Passenger4%
Transit6%
Bike on Board0.63%
Bike0.63%
Walk0.63%
RP Mode Share
Auto-Drive Carpool Auto-Passenger Transit Bike on Board Bike Walk
44
Figure 5-8 TTS Mode Share, 2011
Figure 5-9 SP Mode Share
Auto-Drive86%
Passenger8%
Transit5%
Bike on Board0%
Bike0.27%
Walk1%
TTS Mode Share, 2011
Auto-Drive Passenger Transit Bike on Board Bike Walk
Auto-Drive41%
Carpool28%
Auto-Passenger14.20%
Transit10%
Bike on Board2% Bike
3%Walk2%
SP Mode Share
Auto-Drive Carpool Auto-Passenger Transit Bike on Board Bike Walk
45
Table 5-4 Comparison between RP, TTS and SP Data
Mode RP TTS (2011) SP
Auto Drive 84.09% 85.43% 41.01%
Carpool 3.31%
8.11%
28.30%
Auto Passenger 4.41% 14.20%
Transit 6.30% 4.94% 10.48%
Bike on Board 0.63% 0.00% 1.95%
Bike 0.63% 0.27% 2.53%
Walk 0.63% 1.25% 1.52%
5.5 Telecommuting and Flexible work Hour
In the second part of the survey, various TDM policies have been tested. Some TDM policies are
hard to capture through experimental design, such as telecommuting and a flexible work hour.
Therefore, respondents were asked to choose which day of the week they want to telecommute. It
is found that most of the respondents want to telecommute on Monday (63.15%) and Friday
(68.19%) (Figure 5-10). A flexible work hour was connected with the LOS attributes. Respondents
were shown LOS attributes in the SP scenarios based on their choice in this question. Most
individuals (61%) want to start early (7:30 am), end early (3:30 pm), whereas only (24%) want to
start at normal time and end at normal time (9:00 am to 5:00 pm) (Figure 5-11).
46
Figure 5-10 Day of the Week Respondent’s willing to Telecommute
Figure 5-11 Flexible Work Hour
0%
10%
20%
30%
40%
50%
60%
70%
80%
Mon Tue Wed Thu Fri
Perc
enta
ge (
%)
Day of the Week
Day of the Week-Willing to Telecommute
0% 10% 20% 30% 40% 50% 60% 70%
Start at Normal Time, End at Normal Time(9:00 to 5:00)
Start Late (10:30), End Late(7:30)
Start Early (7:30), End Early (3:30)
Percentage (%)
Tim
e
Flexible Work Hour
47
5.6 Chapter Summary
This chapter provides a detailed analysis results regarding the RP and SP parts of the survey. This
descriptive analysis is essential to understand the quality and applicability of the dataset. RP
descriptive statistics have been discussed from two views: individual attributes and household
attributes. Then RP, TTS and SP mode share comparison is discussed elaborately. Finally, some
TDM policies have been discussed. Chapter 6 will present various modelling structures and their
significance.
48
6 Mode Choice Modelling
Although the OFF-SET collected mode choice information as well as household level activity-
travel diaries, in this thesis I only focused on commuting mode choice behaviour for final analysis.
Therefore, household level activity-travel diaries were not used in this study. The following
sections summarize the effects of TDM policies on mode choice decisions. Three separate models
are presented in this chapter. Firstly, a Multinomial Logit Model (MNL) is estimated based on the
RP data. Alternative models (e.g. Nested Logit) are also tested, but were not identified as feasible
for the collected dataset. Secondly, a MNL model is estimated based on the SP data. Lastly, a joint
RP/SP model is estimated using two types of data (RP and SP). Here, SP dataset need to be scaled
here before pooling. Codes were written in GAUSS to estimate the final empirical models (Aptech
Systems, 2012).
6.1 Data for Empirical Investigation and Generating LOS Attributes
OFF-SET survey data have been used for empirical analysis. 635 respondents’ data are used to
develop the three models. All RP attributes have been kept in the dataset. Since for developing the
tool Monte-Carlo simulation technique is used, socio-demographic characteristics have not
included into the model. Various LOS values have been generated such as total drive time, transit
walk/ bike time, transit wait time, transit in vehicle travel time, travel cost. In the RP part of the
survey each respondent has provided their home and work location. By using second ordered linear
approximation assignment with background transit assignment model auto drive LOS values were
generated. Link based probabilistic shortest path algorithm was used to calculate drive cost. Fare
based congested transit assignment model was used to generate transit LOS values. TTS (2011)
zoning system is used for defining various zones.
49
6.2 Econometric Model Formulation for Separate RP and SP Model
Various modelling specification can be used for RP mode choice model. Though logit model
exhibits the independence from irrelevant alternatives (IIA), it is widely used to estimate different
mode choice models. If V� indicates systematic component, ε� , indicates random component of
the total utility of choosing mode m, then the utility of mode choice (���) for an individual (i) can
be written can be written as:
��� = ��� + ���,� = 1,2,3,… … … ,7 (6-1)
V�� (βX��) consists X�� andβ, where X�� is the vector of explanatory variables including
alternative m and β is the vector of parameters. In this investigation total seven modes have been
considered such as auto drive, carpool, auto passenger, transit, bike on board, bike, and walk. The
distribution of the random component is assumed as Independent and Identically Distributed (IID)
Extreme Value Type I distributed. This assumption should be considered to formulate probabilistic
choice model. According to Random Utility Maximization (RUM) theory the probability that
individual selects alternative m can be presented as Equation 6-2 (McFadden 1981).
��� =����
∑ ���������
(6-2)
MAXLIK component of Gauss was used for maximum likelihood estimation (Aptech Systems,
2012). For RP mode choice model Log likelihood function of total N individual with each m
alternatives can be written as follows:
��(β) = ∑ ∑ ��� ln(���)����
���� (6-3)
Here, ��� = 1 if person n choose mode i and it is zero if the person does not choose that mode.
For SP mode choice model with S scenarios Log likelihood function of total N individual with
each m alternatives can be written as follows:
��(β) = ∑ ∑ ∑ ���� ln(����)����
����
���� (6-4)
50
6.3 Empirical RP Mode Choice Model
MNL model is estimated to investigate RP commuting mode choice behaviour. After a series of
specification tests, the final RP commuting mode choice model is developed and reported in Table
6-1. Three level of service attributes have been kept in the final RP mode choice model: travel
time (in-vehicle travel time and access time), travel cost, and logarithm of distance. This model
shows the probability that a commuters will choose any given mode from a set of feasible
alternative.
As mentioned earlier, the objective of this model specification testing is to develop a policy
analysis tool. Monte-Carlo simulation technique is applied to this policy sensitive tool. Since
simulating socio-demographic variable is beyond the scope of this study, no socio-demographic
variable was tested as explanatory variable. A comprehensive list of future TDM policies is tested
in this study. However, RP data does not capture those hypothetical policies. Therefore, in RP
mode choice model no TDM policy is included as explanatory variable. Most of the variables are
accommodated into the final model based on their statistical significance and signs. Variables are
included in the final model if they are significant at the 0.05 level. Since some variables provide
considerable insight into the behavioural process, some variables are kept in the final model even
though they have low statistical significance.
51
Table 6-1 RP Mode Choice Model
Log Likelihood of Full Model -307.04
Log Likelihood of Null Model -961.22
Number of observations 635
Rho-square value against Null Model 0.68
Parameters Mode Estimates t-stats
Alternative Specific Coefficient Auto Drive 3.31 13.67
Carpool -0.65 -2.19
Auto
Passenger
0 -------
Transit 0.58 1.11
Bike on
board
-1.22 -1.94
Bike -0.61 -0.63
Walk -0.22 -0.37
Travel Time (In-Vehicle Travel Time +Access
Time)
All
motorized
mode
-0.01 -0.89
Travel Cost All
motorized
mode
-0.06 -1.30
logarithm of distance Walk and bike
-0.41 -0.81
As shown in the Table 6.1 the Rho-square value against null model is 0.68. Rho-square value
against null model means percentage increase in the log likelihood function above the value taken
at zero parameters (Train, 2009). This model has only LOS (time and cost) as explanatory
variables. If the time and cost of a particular mode increase, people will not use that particular
mode. The examination of the LOS related variables exhibits negative sign, which is expected.
Here, travel time consists of in-vehicle travel time and access time. Transit wait time usually
remains constant within a certain range. Therefore, sometimes including transit wait time
influences to show wrong sign of the travel time coefficient. Therefore, transit wait time is not
52
considered here. The subjective value of travel time (VOT) is 10 $/hr. There is no time and cost
variable for walk and bike mode. As a result, there are no measurement variables for walk and bike
such as time and cost. So, the logarithm of distance is used as an explanatory variable in the walk
and bike alternatives to capture the level of service. It is observed that the threshold distance for
walk and bike mode is 3 km and 10 km respectively, which means that people would like to walk
or bike if their commuting distances are within the thresholds. Also, the negative sign in the
logarithm of distance coefficient means that with increasing distances people are less inclined to
consider walk and bike as a mode.
6.4 Empirical SP Mode Choice Model
Similar to RP commuting mode choice model, MNL model is estimated to examine SP commuting
mode choice behaviour. After testing various variable combinations finally three level of service
attributes and eight TDM policies have been kept in the final SP mode choice model. Many
alternative structures are tested leading to final SP commuting mode choice model that is tabulated
in Table 6-2. This SP model shows the probability that a commuters will choose any given mode
from a set of feasible alternative with hypothetical TDM attributes.
Since Monte-Carlo simulation technique will be applied to develop Office based Evaluation Tool
(OFF-TET), no socio-demographic variable was included as explanatory variable. An exhaustive
set of future TDM policies is examined in SP commuting mode choice model. 10 policies have
been tested here. Two of them were found to be showing improper sign: shower facilities at office
and parking restriction around the office (likelihood of finding a parking spot within 5 minutes’
walk to your work place). Therefore, a total of eight TDM related variables are accommodated
into the final model. Variables are accommodated into the final model based on their statistical
significance and signs. Variables are included in the final model if they are significant at the 0.05
level. No variables were eliminated from the model on the basis of low statistical significance;
since all variables are anticipated to have some effect on transportation demand management, all
are maintained in the final SP commuting mode choice model.
53
Table 6-2 SP Mode Choice Model
As shown in the Table 6.2 Rho-square value against null model is 0.15. This model has LOS (time
and cost) and TDM policies as explanatory variables. The examination of the LOS related variables
exhibits negative sign, which is expected. Likely as RP commuting mode choice model, travel
time consists of in-vehicle travel time and access time. Cost variable is not showing proper sign;
therefore, cost/ logarithm of distance is used as a variable which is statistically significant as well
as showing proper sign. Cost/ logarithm of distance represents a unit cost over logarithm of
distance. If only the coefficient of cost parameter would be estimated, value of travel time can be
calculated. However, by using cost/ logarithm of distance variable value of travel time calculation
Log Likelihood of full model -4917.83
Log Likelihood of null model -5767.36
Rho-square value against null model 0.15
Number of Observations 635 Parameters Mode Estimates t-stats
Alternative Specific Coefficient Auto Drive 1.80 5.74
Carpool 0.47 2.20
Auto Passenger 0.00 --
Transit 0.66 6.17
Bike on board -0.66 -2.79
Bike 0.33 1.45
Walk -0.02 -0.14
Travel Time (In-Vehicle Travel Time +Access Time)
All motorized mode -0.02 -10.52
Cost/ logarithm of distance All motorized mode -0.02 -2.18
logarithm of distance for bike Bike -0.66 -9.69
logarithm of distance for walk Walk -0.93 -8.15
Monthly Parking Cost Auto Drive, Carpool -0.22 -2.58
Daily Parking Cost Auto Drive, Carpool -0.14 -2.19
Indoor Park Auto Drive, Carpool 0.34 2.82
Emergency Ride Home Carpool 0.42 2.84
Bike share Carpool, Auto Passenger, Local Transit
0.41 2.42
Car share Auto Passenger 0.15 1.01
Locker Bike and Bike on Board 0.24 0.86
Bike access Bike and Bike on Board 0.10 0.40
54
is sporadic in literature. Also, calculating VOT is not the main focus of the study. Unlikely RP
commuting mode choice model, logarithm of distance is used as alternative specific coefficient for
walk and drive mode. All three level-of-service variables, namely travel time, cost/ logarithm of
distance and logarithm of distance, are showing proper signs and are statistically significant at
95% confidence interval.
As presented in the SP commuting mode choice model, monthly parking cost has higher sensitivity
than the daily parking cost. The modelling result shows that the monthly parking cost coefficient
is around 2 times higher than the daily parking cost. As for the effect of increased parking facilities,
it is clearly shown that auto driver and carpool user have a positive perception for indoor parking
facilities. The result shows that carpool users are more likely to choose emergency ride home. As
expected, the model result shows that carpool, auto passenger, local transit users are more inclined
to use bike share program. Auto passengers are more persuaded to use car share program. Finally,
locker and bike accessible office (ramp) are complementary policy which promote bike share
program. Both of the coefficients are showing correct signs; though, they are not statistically
significant. However, to test the effect of these policies they are kept in the final model.
6.5 Econometric Model Formulation for Joint RP/SP Mode Choice Model
For joint RP/SP model two types of data will be used: RP data and SP data. According to Elisabetta
and Juan (2006), joint RP/SP model uses two separate utility equations for RP and SP portion,
rather than combining RP and SP data. As mentioned earlier, if V� indicates systematic component
and ε� , indicates random component random component of the total utility of choosing mode m,
then the utility of mode choice (Um) for an individual (i) can be written as:
��� = ��� + ���,� = 1,2,3,… … … ,7(6 − 5)
Now, for joint RP/SP model, there should be two utility equations. One utility equation will
represent RP utility and another one will represent SP utility. There will be some generic
coefficients in both RP and SP utility. Similarly, there will be some attribute specific coefficients
such as alternative specific coefficients that will vary over these two type of utility equations. Now
according to the discussion above two utility equations can be presented as follows:
55
���� = ���
�� + ����� + ��
��(6 − 6)
���� = ���
�� + ����� + ��
��(6 − 7)
Here, ����,��
��=Corresponding utility of RP and SP data
�= Generic Vector Parameter
�,�= Alternative Specific Vector Parameter
����, ��
��= Generic Vector Attributes
����,��
��= Alternative Specific Vector Attributes
����,��
��=Random component associated to RP and SP utility
���� ≈ (0,���
� )
���� ≈ (0,���
� )
As SP and RP dataset is different from each other it is essential to scale them during the model
estimation. If �and� are scale parameter for RP and SP data respectively, according to
Elisabetta and Juan (2006) generic and alternative specific vector parameter can be written as
follows:
��� = ��
��� = ��
��� = ��
��� = ��
Due to different variance (���� ≠ ���
� ) of these two different dataset the scale parameter should not
be same. As a result of this unknown scale parameter, ��� should not be equal to���. Now it is
a pressing question that which dataset should be scaled. According to Brownstone et al. (2000)
and Elisabetta and Juan (2006) the most common practice is to scale SP utility. Therefore, only SP
56
data will be scaled (Ф) to achieve same variance in both cases. If Ф is the scale parameter, the
relation between RP utility and SP utility can be written as follows:
Ф =����
���� (6 − 8)
If joint estimation requirement Фfollows the condition Ф =���
��� , the final equation of RP scale
parameter can be written as (Train, 2000):
Ф =���
���=
�
√����
�
√����
= ���
���(6 − 9)
Now the SP utility equation should be changed due to the scale factor.
�′��� = Ф��
�� = Ф(����� + ���
�� + ����)
= ����� + ���
�� + ���� [Whereas,��
��≈ (0,���
� )]
= (����� + ���
��) + ����
= ����� + �′�
��
For total N individual with each m alternatives and for s stated preference scenarios joint log
likelihood function can be also written as follows:
��(β) = ∑ ∑ ��� ln(���) + ∑ ∑ ∑ ���� ln(���)����
����
���� (6 − 10)�
�������
Whereas, ��� = 1 if person n choose mode m and it is zero if the person does not choose that
mode.
57
6.6 Joint RP/SP Mode Choice Model
OFF-SET RP dataset reveals the existing travel behaviour of the respondents where TDM policies
could not be tested; whereas, OFF-SET SP dataset provides an opportunity to test a wide range of
TDM policies. Here, RP mode choice model cannot be used for policy analysis and SP commuting
mode choice model is based on hypothetical scenario testing and thus not appropriate for
forecasting (Louviere, 2000). Therefore, a joint RP/SP mode choice model is estimated which is
widely used to estimate various advanced model such as mixed logit models (Elisabetta & Juan,
2011). Using the data described in the previous sections, several joint RP/SP MNL commuting
mode choice models are estimated. Final model result is tabulated in Table 6-3.
Table 6-3 Joint RP/ SP Mode Choice Model
Log Likelihood of Full Model
-5226.41
Log Likelihood of Null Model
-6728.59
Rho-square value against Null Model
0.22
Number of Observations 635
Parameters Mode Estimates t-stats Estimates t-stats
RP Coefficient SP Coefficient
Alternative Specific Coefficient
Auto Drive 3.39 13.46 2.09 1.98
Carpool -0.52 -1.72 0.59 1.59
Auto
Passenger
0.00 -- 0.00 --
Transit 1.18 2.24 0.81 2.13
Bike on
board
-0.76 -1.20 -0.63 -1.50
Bike -0.69 -0.64 0.47 1.37
Walk -0.32 -0.41 0.03 0.14
Travel Time (In-Vehicle Travel Time +Access Time)
All motorized mode
-0.02 -2.18 -0.02 -2.18
Cost All motorized mode
-0.07 -1.58 -- --
logarithm of distance Walk and Bike
-0.40 -0.74 -- --
58
Parameters Mode Estimates t-stats Estimates t-stats
Cost/ logarithm of distance
All motorized mode
-- -- -0.02 -1.54
Monthly Parking Cost Auto Drive,
Carpool
-- -- -0.26 -1.64
Daily Parking Cost Auto Drive, Carpool
-- -- -0.16 -1.53
Indoor Park Auto Drive, Carpool
-- -- 0.38 1.66
Emergency Vehicle Home Carpool -- -- 0.46 1.67
Bike share Carpool, Auto
Passenger, Local Transit
-- -- 0.47 1.58
Car share Auto Passenger
-- -- 0.20 1.05
Locker Bike and Bike on Board
-- -- 0.23 0.69
Bike access Bike and Bike on Board
-- -- 0.09 0.30
logarithm of distance for Bike
Bike -- -- -0.76 -2.07
logarithm of distance for Walk
Walk -- -- -1.06 -2.04
Scale Parameter -- -- 0.89 2.11
As depicted in the Table 6.3 Rho-square value against null model is 0.22, which is slightly higher
than SP mode choice model but lower than RP mode choice model. This model has both LOS
attributes (time and cost) from RP data and TDM policies from SP data as explanatory variables.
Time, cost and distance related variables exhibit negative sign, which is expected.
Travel time (in-vehicle travel time and access time) is estimated as a generic coefficient for both
RP and SP. However, to be consistent with RP mode choice model (Table 6-1) and SP mode choice
model (Table 6-2) cost is estimated using RP data and cost/ logarithm of distance is estimated
using SP data. Similarly, generic coefficient is estimated for logarithm of distance using RP data
and alternative specific coefficient (walk and bike) is estimated for logarithm of distance using SP
data. Except generic logarithm of distance variable all other level of service attributes are
statistically significant on 95% confidence interval. It is found that the threshold distance for walk
59
and bike mode is 3 km and 10 km respectively, which indicates that people would like to walk or
bike if their commuting distances are within the threshold distances. Furthermore, the negative
sign in the logarithm of distance coefficient means that with increasing distances people are less
inclined to consider walk and bike as a mode.
Similar to SP commuting mode choice model, monthly parking cost has higher sensitivity than the
daily parking cost. Monthly parking cost coefficients are 1.6 times higher than the daily parking
cost. It is found that auto driver and carpool user have an upbeat insight for indoor parking
facilities. Similar to SP mode choice model, the result shows that carpool users are inclined to
choose emergency ride home. Bike share program has high influence on carpool, auto passenger,
and local transit users. According to the model result, auto passengers are more inclined to use car
share program. Locker and bike accessible office (ramp) variables are showing correct sign, but
they are showing low statistical significance. However, as they have significant impact into the
behavioural process, those policies are kept in the final model. The scale parameter is estimated as
a constant (0.89). As there are two types of data, SP data is needed to be scaled before pooling
(Elisabetta & Juan, 2011).
6.7 Chapter Summary
This chapter provides a synopsis about the full modelling framework. This chapter also provides
detailed insight on the RP mode choice model, the SP mode choice model and the joint RP/SP
mode choice model. Rationalization of all variable selection is broadly discussed here. Also,
each variable’s performance are compared among various models.
60
7 Office based TDM Evaluation Tool (OFF-TET) Development
In this chapter the development process of OFF-TET will be described. The development process
has two steps: joint RP/SP model insertion into the tool and simple application of the Monte
Carlo simulation method. These two steps, along with step-by-step instructions for using the tool
are described in this chapter
7.1 Tool Development Process
The OFF-TET interface is extensible to a number of different platforms, ranging from spreadsheets
to dedicated web based platform. Both the spreadsheet and web based approaches have strengths
and challenges associated with their deployment and maintenance. The spreadsheet-based tool is
a stand-alone tool that requires minimal maintenance and upkeep once developed. Unfortunately,
issues related to the distribution of the tool may be difficult and there is some privacy concerns
related to sensitive micro data contained within the spreadsheet. Conversely, a web-based tool
requires regular maintenance and upkeep associated with maintaining a server to host the tool,
however this approach may allow for the tool to reach a much larger audience and has fewer
privacy concerns associated with sensitive personal data. For this study, a spreadsheet based tool
is developed. Details of tool development process are described in the following sections.
7.1.1 Forecasting with Joint RP/SP model
Three models are estimated in this study: RP mode choice model, SP mode choice model and joint
RP/SP mode choice model. RP data should be used for forecasting. To develop the tool, it is
required to forecast various TDM policies; however, the TDM policies are captured through SP
data. Since SP data do not represent real world, it is not recommended to use for forecasting
(Louviere et al., 2000). Therefore, the best practice would be used use a “hybrid” model that will
use both RP and SP data (Cherchi & Ortúzar, 2006).
If all coefficients are generic in the RP and SP portions of joint RP/SP model, forecasting would
be straightforward by using generic RP and SP coefficients. However, in the joint RP/SP model
61
only travel time (in-vehicle travel time plus access time) is generic. All other parameters are
corresponding to either the SP environment or the RP environment. A specific methodology of
using parameters for forecasting, while parameters are not generic for SP and RP environment, is
scant in literature. Another practical problem is whether to use in forecasting the RP ASC or SP
ASC and whether the SP parameter should be scaled. According to Cherchi & Ortúzar (2006) and
Louviere et al. (2000) the following agreements can be written.
1. In this study, RP and SP alternatives are same. Hence, RP-ASC will be used for forecasting.
Also, RP-ASC will not be scaled.
2. Since RP and SP alternatives are the same, RP-ASC should be adjusted to match the base
year market share. So, calibration will be necessary to match the market share of the base
year.
3. In this study, only SP parameters will be used for forecasting. This SP parameters should
not be scaled as those parameters are already deflected by an unknown RP scale parameter.
7.1.2 Model Calibration
As mentioned in the previous section, calibration is required to match predicted mode share of an
alternative using discrete choice models with the market share of the base year. For forecasting
market share data from the specific area should be used to calibrate the ASC. Equation 7.1 is used
to calibrate ASC.
����� =Calibrated alternative specific constant for alternative m
����� = Estimated alternative specific constant for alternative m
��=Base year mode share of an alternative
��� =Predicted mode share of an alternative using discrete choice models
����� = ����
� + ln���
��� �(7 − 1)
62
The aggregate mode share is very close to the RP base case mode share (observed mode share in
the survey) and TTS mode share. The model is calibrated against RP base case mode share.
7.1.3 Application of OFF-TET
Individual or combined effect of TDM policies can be captured by using this tool. The joint RP/SP
model is developed by using whole regional level data. So, this tool can be applied for regional
level TDM policy testing. Changed VKT, CO2 savings and other environmental factors can be
calculated using the output of this tool. Also, this tool can be used in city levels such as Brampton,
Mississauga or Caledon. By calibrating ASC, any of this city can use this tool for TDM policy
testing. In this region, there are many offices with large number of employees. This tool can be
modified so that an employer can use this tool for policy testing at his/her own office.
Another research question is whether this tool can be used for partial implementation of any policy.
To solve this problem a naive application of Monte Carlo Simulation technique is applied to
capture the penetration rate. Sometimes it is impractical to implement a certain policy within the
whole region. In this case, the policy can be implemented within a sub-portion of the region. This
partial implementation effect can be captured by the penetration rate. For example, if an individual
wants to implement 30% bike share in the regional level, by using the penetration rate it can be
easily possible to test the effect of partial implementation of bike share policy.
63
Figure 7-1 Application of OFF-TET
7.2 Software Interface
The software interface for the tool has three main pages
1. Startup page with modal definitions.
2. Input page with all TDM policies’ name, description, input type and instruction.
3. Result page with base case, after TDM application case and differences.
The tool is very user friendly. Also, a user manual was added with OFF-TET. The input type is
only yes or no. For some cells user has to provide dollar amount of money and for other cells
user has to provide percentage of the penetration rate.
1. The definitions of the available modes are shown in the start-up page of OFF-TET (Table
7-1).
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Table 7-1 Start-up Page of the Tool
Mode Alternatives Description
Drive One passenger driving alone all-way
Carpool Sharing the car (either as driver or as a passenger) with someone who is not a own household member
Auto Passenger Having a household member driving car to drop at destination
Local Transit Taking public transit (bus, streetcar, etc.) to the destination
Bike on Board The respondent will access transit using their bicycle and take the local transit to work, bringing their bike on board
Bike Biking to the destination
Walk Walking to the destination
2. The next section will show the input interface for the tool (Table 7.2).
a. The first column contains a list of the possible TDM policies to be tested.
b. In the second column, there is the description for the policies.
c. In the third column, there is “input type and instruction” for the policies.
d. If a user wants to change any certain policy, he/she has to provide an input in the fourth
column. The input is either binary (yes/no and monthly/daily) or continuous numbers. For
example: “daily or monthly parking scheme for driving” has two options: monthly or daily.
The user can provide either monthly or daily as an input. Similarly, “bike share program”
has two options: yes or no. Here, the user can provide either yes or no as an input. On the
other hand, parking cost can be any continuous value from zero to one hundred dollars.
e. Sometimes, policies may not be implemented across the entire region. So, a certain policy
can be implemented within a certain percentage of the region. This percentage is defined
as the penetration rate. The penetration rate is incorporated within the tool using the Monte
Carlo simulation technique. Therefore, an equivalent percentage of commuters in the
region will be affected by the TDM policy.
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Table 7-2 Input Page of the Tool (Before Input)
TDM Policy Description Input type and instruction Input Here
Penetration Rate
Daily Parking Cost Driving at Workplace Provide any Dollar amount 0.78 Daily or Monthly Parking Scheme for Driving
Select from drop down menu Daily 100.00%
Daily parking cost carpooling workplace Provide any Dollar amount 0.78 Daily or Monthly Parking Scheme for Carpooling
Select from drop down menu Daily 100.00%
Employer provides incentive for Region of Peel transit passes
Employer pays for Region of Peel (Miway or Brampton Transit) transit passes
Select from drop down menu No
If Employer provides incentive for Region of Peel transit passes, then how much incentive they will provide? (provide percentage of incentive)
Provide an incentive percentage 0%
Indoor parking facilities at workplace for drive mode
Indoor Car Parking at Your Workplace
Select from drop down menu No 100.00%
Indoor parking facilities at workplace for carpool mode
Indoor Car Parking at Your Workplace
Select from drop down menu No 100.00%
Emergency ride home program.
Emergency Vehicle or Ride Home Program at Your Workplace
Select from drop down menu No 100.00%
Bike share Program
Employer Owned Bikes Available to Rent (For Going Out to Lunch)
Select from drop down menu No 100.00%
Car share program
Employee Run Car Share Program at Your Workplace (for business related or short personal trips)
Select from drop down menu No 100.00%
Sheltered bike parking at your workplace
Sheltered Bike Parking at Your Workplace
Select from drop down menu No 100.00%
Bike friendly building access (ramps) at your workplace
Bike Friendly Building Access (Ramps) at Your Workplace
Select from drop down menu No 100.00%
Ride matching
Employer provides a ride matching program at workplace
Provide an penetration percentage 1 100.00%
66
3. Figure 7-1 reveals preliminary the mode share before implementing any policies.
7.3 Example Application
A user can test various policies numerous times by using OFF-TET. Both combined and individual
effect of policies can be tested. A sample application is shown in Figure 7-3 to guide users where
to test combined effect of two policies. First, the user wants to increase the parking cost for only
driving mode, keeping carpool parking cost constant. The user, therefore, provides a 3 dollar input
at the “daily parking cost driving at workplace” cell. Then, the user wants to implement an
“emergency ride home program” at the workplace. Therefore, the “yes” option is selected in the
“emergency ride home program”. The input and output interfaces have been shown in Table 7-3
and Figure 7-2.
Figure 7-2 Base Case Mode Share (Before Testing any Policy)
67
Table 7-3 Input Page of the Tool (After Input)
TDM Policy Description Input type and instruction Input Here
Penetration Rate
Daily Parking Cost Driving at Workplace Provide any Dollar amount 3.00 Daily or Monthly Parking Scheme for Driving Select from drop down menu Daily 100.00% Daily parking cost carpooling workplace Provide any Dollar amount 0.78 Daily or Monthly Parking Scheme for Carpooling Select from drop down menu Daily 100.00%
Employer provides incentive for Region of Peel transit passes
Employer pays for region of Peel (Miway or Brampton Transit) transit passes Select from drop down menu No
If Employer provides incentive for Region of Peel transit passes, then how much incentive they will provide? (provide percentage of incentive)
Provide an incentive percentage 0%
Indoor parking facilities at workplace for drive mode
Indoor Car Parking at Your Workplace Select from drop down menu No 100.00%
Indoor parking facilities at workplace for carpool mode
Indoor Car Parking at Your Workplace Select from drop down menu No 100.00%
Emergency ride home program.
Emergency Vehicle or Ride Home Program at Your Workplace Select from drop down menu Yes 100.00%
Bike share Program
Employer Owned Bikes Available to Rent (For Going Out to Lunch) Select from drop down menu No 100.00%
Car share program
Employee Run Car Share Program at Your Workplace (for business related or short personal trips) Select from drop down menu No 100.00%
Sheltered bike parking at your workplace
Sheltered Bike Parking at Your Workplace Select from drop down menu No 100.00%
Bike friendly building access (ramps) at your workplace
Bike Friendly Building Access (Ramps) at Your Workplace Select from drop down menu No 100.00%
Ride matching
Employer provides a ride matching program at workplace
Provide an penetration percentage 1 100.00%
68
Figure 7-3 Mode Share Comparison (After Testing any Policies)
7.4 Chapter Summary
This chapter reveals the process of using the policy analysis tool (OFF-TET).
This chapter discusses about software interface and a sample application. A brief description is
provided regarding the description of the modes, OFF-TET’s input page, and output page. Finally
a sample application is shown here which will help users to use it properly. In the Chapter 8,
findings of the total study will be discussed, along with future works.
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8 Conclusions
This chapter presents the summary of the study conducted in this thesis. Also, it provides
recommendations for improving the OFF-SET survey as well as future works.
8.1 Research Contributions
This dissertation focuses on enhancing quantitative understandings of how employer-based TDM
policies influence commuting mode choice. Since existing surveys did not provide required
information to estimate a policy sensitive evaluation tool, a joint RP-SP survey (OFF-SET) was
designed by using state-of-the-art methodology of experimental design. OFF-SET collected both
revealed preference (RP) travel information and stated preference (SP) mode choice preferences
in context of one or multiple employer-based TDM policies. In the OFF-SET the RP survey is
followed by six SP scenarios. In the RP part, household, personal and socio-demographic
information were collected. In the SP part, a total of twelve employer-based TDM policies are
specified through respective attributes of commuting mode choice contexts and are captured by
using efficient design technique. The survey is implemented in Fall 2014 and Winter 2015 and a
total 835 randomly selected commuters were interviewed who work in the Region of Peel.
Data are used to estimate discrete choice models considering SP response biases through the
implementation of RP-SP data fusion technique. Ten out of twelve policies are found to have the
expected impacts on commuting mode choice behaviour of the commuters in the Region of Peel.
Two TDM policies (telecommuting and flexible work hour) are hard to capture by the stated
preference scenarios. Therefore, these policies are captured by linking into stated preference
scenarios. It is found that most of the commuters want to telecommute on Monday (63.15%) and
Friday (68.19%). Surprisingly, most of the individuals (61%) want to start early (7:30 am) and end
early (3:30 pm).
Three separate econometric model formulations are tested in this research: RP commuting mode
choice model, SP commuting mode choice model and joint RP/SP commuting mode choice model.
The joint RP/SP commuting mode choice model is used in final tool development to practice the
state-of-the-art modelling structure. In the joint RP/SP commuting mode choice model result, it is
found that shower facilities and changing room at office and parking restriction around the office
do not have significant effect on commuters’ mode choice behaviour. Total eight TDM policies
70
are found to have significant effect on commuters’ mode choice behaviour such as monthly
parking cost, daily parking cost, indoor park, emergency vehicle home, bike share, car share,
locker, and bike access. Monthly parking cost has higher sensitivity than the daily parking cost.
Result shows that auto driver and carpool users will increase, if indoor parking facilities are
provided. The result also shows that carpool users are prone to choose emergency ride home. Bike
share program has high influence on three motorized modes as follows: carpool, auto passenger,
and local transit users. Auto passengers are found to be more inclined to use car share program,
whereas locker and bike accessible office (ramp) variables are showing the influence bike and bike
on board users.
The main outcome of this research is the policy analysis tool (OFF-TET). It allows investigating
marginal as well as compounded (complementary or supplementary) effects of multiple employer-
based TDM policies. To evaluate reasonable strategies, in terms of allowing different percentages
of employers in the region implementing different types of policies alone or jointly, the concept
of penetration rate is introduced. In OFF-TET, such penetration rate is simulated by Monte Carlo
Simulation. This tool has a spreadsheet based interface which is very easy to use. No software
installation is required to use this tool and this tool is convertible to any platform.
8.2 Future Research
While this dissertation served its primary objectives by developing the TDM evaluation tool OFF-
TET, there is a scope of future research. The OFF-SET survey provided RP data which includes
their activity schedule. This activity scheduling information have not been used in this research.
So, this section represents some recommendations of future work that have a potential for future
research motivation.
1. Office based TDM policies mainly change people’s travel behaviour in AM peak. However,
implementation of those TDM policies may affect the trip chain in the later parts of the day. Also,
it may reduce congestion in AM peak, but at the same time it may increase congestion in the off
peak time or PM peak. Therefore, to get the holistic picture it is essential to focus on the activity
scheduling modelling. In the OFF-SET survey both commuters and non-commuters travel diaries
have been collected. By investigating that information a better understanding can be perceived
regarding commuters everyday travel behaviour and how TDM policies might influence them.
71
2. OFF-TET is basically an employer-based policy evaluation tool. A home-based TDM
evaluation tool can also be developed. The models presented in this thesis do not consider any
socio-economic variables. This has done purposely to avoid forecasting of socio-economic
variables within OFF-TET application. However, further investigation is necessary to find ways
to capture market segmentations based on socio-economics in TDM policy responses. Also, effects
of daily activity-travel scheduling are also overlooked although data on these are collected. So,
future research includes investigation of these also.
8.3 Chapter Summary
In this chapter a synopsis of the total research contribution is described briefly. It also provides
recommendations for future research. In the next sections references and appendix are provided.
72
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Appendix A : OFF-SET( A Customized Individual Specific Web based Survey Software)
Welcome Screen
75
RP Part
Household and Personal Information
76
77
Interactive Google Map to Allow Respondent to find his/her Work place
78
79
Interactive Google Map to Allow Respondent to find his/her Home Location
80
81
82
SP Part
Telecommuting and Flexible work Hour
83
Home based TDM Question
84
Instruction for SP Part
85
86
87
88
89
90
91
92
93
94
95
Activity Scheduling Part (Non-Commuters)
96
97
\
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Activity Scheduling Part (Commuters)
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100
101
102
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Appendix B : Ngene Utility Equations
U(D)= a1[1]+a8[-0.2]*D_PC[2,4] +a9[.01]*M_or_D_PC[0,1]+a10[-
0.2]*Add_5_Walk[0,1]+a11[0.1]*In_P[0,1]/
U(CP)= a2[-.1]+a8*CP_PC[0,1,2]+a9*M_or_D_PC[0,1]
+a10*Add_5_Walk[0,1]+a11*In_P[0,1]+a12[.1]*BikeShare[0,1]
+a17[0.1]*CarShare[0,1]+a24[0.1]*ERH[0,1]/
U(AP)= a13[.1]*BikeShare +a18[0.1]*CarShare +a25[0.1]*ERH/
U(LT)= a3[-0.1] +a8[-.2]*Fare[0,1] +a14[.1]*BikeShare +a19[0.1]*CarShare
+a26[0.1]*ERH/
U(PR)= a4[-0.1] +a8[-.2]*Fare +a15[.1]*BikeShare +a20[0.1]*CarShare
+a27[0.1]*ERH/
U(BOB)=a4[-.2] +a8[-.2]*Fare +a21[0.1]*CarShare
+a28[0.1]*ERH+a31[0.1]*Shower[0,1]+a32[0.1]*Locker[0,1]+a33[0.1]*BikeAccess[0
,1]/
U(B)= a6[-.2] +a22[0.1]*CarShare+ a29[0.1]*ERH
+a31[0.1]*Shower[0,1]+a32[0.1]*Locker[0,1]+a33[0.1]*BikeAccess[0,1]/
U(W)= a7[-0.3] +a16[.1]*BikeShare +a23[0.1]*CarShare
+a30[0.1]*ERH$
