University of Toronto Transportation Research Institute (UTTRI)

Permanent URI for this collectionhttps://hdl.handle.net/1807/82686

UTTRI faculty specialize in systems analysis and policy evaluation, and are leaders in building and applying cutting-edge computer simulation models to the analysis and design of complex transportation systems. This collection presents but a small sample of their research.

Browse

Now showing 1 - 12 of 12

Autonomous Vehicle Literature Review
(University of Toronto Transportation Research Institute, 2015-09-30) Knowles, Alec; Budnyy, Anton; Miller, Eric J.; Farvolden, Judy
This literature review describes the safety, regulatory, financial, energy, environmental, security, and privacy implications of the introduction of Autonomous Vehicles (AVs), including Connected Vehicles (CVs). Specifically, this review is intended to provide background information and context to inform municipal policy- and decision-makers at the City of Toronto, ON, Canada, of ongoing developments and speculations related to AVs and CVs. Discussion of policy and regulatory frameworks introduced or under consideration in other jurisdictions is included where considered to be substantial and relevant. While this paper is intended primarily to inform municipal policy, regulation and legislation from all levels of government across Canada and around the world have been incorporated. The scientific and technological developments facilitating the introduction of these vehicles – i.e., how AVs are being introduced – are examined only as and where necessary to provide perspective regarding what municipalities should begin to prepare for. Beyond AVs, other forms of intelligent transportation equipment and services (such as drones and wireless communication) are discussed sparingly, and only where considered relevant to municipal services and regulation. This paper considers peer-reviewed journal articles, newspaper and website articles, academic simulation studies, theses, reports from think tanks, governmental policy analyses, and other diverse sources. As of the time of writing, many of the studies, projects, and developments described are in progress; others are in various planning stages, a handful have been completed, and many are merely speculative. The umbrella term “AV” is used to refer to any and all autonomous/driverless vehicles, of which connected vehicles are considered to be a subset. In cases where a distinction needs to be drawn between a standalone autonomous vehicle (standalone AV) versus a connected vehicle (CV), that distinction is made explicit.
Driving Changes: Automated Vehicles in Toronto. A Discussion Paper
(University of Toronto Transportation Research Institute, 2015-10) Ticoll, David
The purpose of this report is to equip City of Toronto decision makers with the information they need to identify and evaluate short and medium term policy, planning, and investment options that pertain to the onset of vehicle automation. The report provides independent analysis of statistical and qualitative information, drawing on literature reviews by the author and by UTTRI.
SmartTrack Ridership Analysis: Project Final Report
(University of Toronto Transportation Research Institute, 2016-06) Miller, Eric J.; Vaughan, James; Nasterska, Monika
On December 11, 2014, City Council directed the City Manager in consultation with the Province/Metrolinx to develop a work plan to undertake an accelerated review of the SmartTrack and RER plans. Council also directed the City Manager to retain the specialized services of the University of Toronto Transportation Research Institute (UTTRI) to support the planning analysis and required transit ridership modelling as a component of the overall review.1 On February 10, 2015, City Council considered the report EX2.2 SmartTrack Work Plan (2015- 2016), and approved the accelerated work plan for the review of SmartTrack.2 The UTTRI component of this work was to provide transit ridership estimates and other key network performance measures using the City’s new Regional Travel Demand Model (GTAModel Version 4.0) developed at the University of Toronto by UTTRI. As detailed in the final Terms of Reference for the UTTRI work, this work included: • Confirming the integrated RER and SmartTrack Service Concept to be modelled. • Completion and validation of a new travel demand model system to be used by the City of Toronto in this and similar studies of transit ridership and travel demand. • Development and review of forecasting assumptions that provide key inputs into the transit ridership forecasts. • Generating transit ridership forecasts for the identified range of future year networks and input scenarios. • Analysis and comparison of ridership forecast results. • Documentation and reporting of all work and results. This study did not deal with: • Detailed engineering design considerations of route alignments and stations. • Capital and operating costs of alternative network designs. • Financing mechanisms to pay for the construction and operation of network additions. Thus, this study focuses solely on the transit ridership levels and other system performance measures that are likely to occur if various transit network improvements are made. While the primary focus of this analysis is on options for the proposed SmartTrack line, this line cannot be considered in isolation of the overall Greater Toronto-Hamilton Area (GTHA) transit network and, in particular, other major transit infrastructure proposed investments, notably GO RER plans, Scarborough Subway Extension (SSE) options, and Relief Line (RL) options (formerly often referred to as the Downtown Relief Line). Similarly, the future is a very uncertain place, and so ranges of estimated ridership need to be generated across a variety of possible future year growth scenarios and other assumptions. Given this, a wide range of combinations of network investment and growth scenarios are generated in this study and results are compared in detail.
Simulating Autonomous Vehicles: A Discussion Paper and Research Proposal
(University of Toronto Transportation Research Institute, 2016-06) Miller, Eric J.
The purpose of this paper is, first, to enumerate a number of areas of current significant uncertainty concerning potential AV impacts on roadway operations, travel demand and urban form, and, second, to propose a coordinated research program to systematically investigate these issues. A fundamental assumption in the construction of the proposed research program is that advanced simulation modelling methods can provide a primary analysis tool to move beyond the qualitative assertions typical of current discussions towards a much more rigorous, quantitative exploration of key AV design and performance issues. In particular, simulation models can provide a “virtual laboratory” within which controlled “experiments” can be conducted to test alternative assumptions concerning AV operations and impacts within realistic, city-scale representations of the transportation system, the urban form within which this system is operating and the travel market that the system is intended to serve.
Canadian Ridership Trends Research Project, Final Report
(University of Toronto Transportation Research Institute, 2018-06-07) Miller, Eric J.; Shalaby, Amer; Diab, Ehab; Kasraian, Dena
This document presents the final report for the “Canadian Ridership Trends Research” project. The project’s overarching objective is “to conduct an in-depth study on current and future conventional ridership trends through research and consultation with transit systems” which is “to provide an understanding of the correlation between causal factors and ridership in Canada and provide explanation(s) of ridership decline at a transit system, Census Service Area (CSA), and national level.” This report brings together the deliverables for the Canadian Ridership Trends Research project organized into five parts.
Transportation Tomorrow Survey (TTS) 2.0 Final Report
(University of Toronto Transportation Research Institute, 2018-12-19) Srikukenthiran, Siva; Loa, Patrick; Hossain, Sanjana; Chung, Brittany; Habib, Khandker Nurul; Miller, Eric J.
TTS 2.0 was created with the aim of developing the next generation of travel survey methods for the region to overcome current and foreseeable challenges the TTS has or may face. TTS 2.0 was centred on the investigation of four primary survey modes: phone, web, smartphone and passive methods. Investigation into these modes proceeded first with a literature review report, providing information on the current state of the art in collection methodology for each mode, and providing recommendations for potential small-scale field tests that should be conducted in the next phase. This was followed by a series of smaller focussed research reports, the development of a web-survey platform (TRAISI) for use in later pilots, and a field test around the technical feasibility of smart phones and smartphone survey apps. The main field tests were conducted in the final year, encompassing both sampling and mode investigations. This report acts as a summary and synthesis of these in-depth reports. Key findings and recommendations for the 2021 TTS are presented that emerged from the literature review, pilots and field tests of the TTS 2.0 research programme.
Report 6: Analysis of Network Impacts: PTC Trip Chaining
(University of Toronto Transportation Research Institute, 2019-05) Calderón, Francisco; Miller, Eric J.
This technical report presents the work undertaken by the University of Toronto Transportation Research Institute (UTTRI) to model trip chaining of vehicles operating for Private Transportation Companies (PTCs) in support of the City of Toronto’s Vehicle for Hire Bylaw Review. The main purpose of this component of the project is to assess network impacts of PTC operations. The data provided to study these impacts consists of the entirety of trips reported by ridehailing service providers from the period of September 1, 2016 to December 31, 2018. Even though these data are comprehensive and extremely useful for analysis, they do not encompass the entirety of ridehailing vehicular movements. Ridehailing vehicles further occupy the network while being en- route to pick-up passengers, as well as when idling or waiting to be paired with the next passenger. Hence, identifying and accounting for these additional vehicle “states” becomes a critical task towards a comprehensive assessment of PTC network impacts. To align this report ́s terminology with its counterpart in the Vehicles-For-Hire Bylaw (City of Toronto, 2016), the mentioned vehicle “states” are defined by three periods. Namely: • Period 1 (idling): “total time a PTC driver had activated or was logged into a PTC Platform and available to receive or accept requests to provide passenger transportation service”. • Period 2 (en-route): “total time elapsing between the time a passenger request for transportation is accepted by a PTC driver and the arrival of the PTC driver at the passenger's pick up location”. • Period 3 (in-service): “total time elapsing between the time that a PTC driver picks up a passenger(s) until the time the passenger(s) has arrived at their destination(s)”. To quantify the amount of time spent and distance driven by ridehailing vehicles while being in Periods 2 and 3, Project Task 4.2 aims to identify vehicle trip chains, which would provide information related to the periods when vehicles are not transporting passengers. It must be noted that trip chaining is normally not an operational task explicitly performed by service providers, but it rather is the outcome of the dynamics of within-day service provision processes. A brief background on ridehailing operations is presented in Section 2 below to further develop this argument. As acknowledged in the Project Charter document, undertaking a trip chaining analysis (Task 4.2) involves a high degree of uncertainty and complexity, being contingent upon the availability of various data sets, particularly unique driver identifiers and full path "GPS breadcrumbs" of each PTC VFH trip – the latter categorized by its different periods (1,2, or 3). Given the absence of these data, a prototype model has been developed, built upon existing data (observed demand for ridehailing trips) and an extensive conceptual understanding of ridehailing fundamental operational tasks. Furthermore, it must be acknowledged that the prototype model circumvents the lack of data by endogenizing ridehailing operational processes and vehicles (and its drivers) as agents. In this context, calibration and validation as envisioned in Task 4.3 become practically unfeasible since the prototype model can only provide approximate estimates. Nonetheless, a strategic modelling decision consisted of leaving out wait time variables in the dataset to be used for assessment of the quality of the results product of the modelling efforts undertaken in this report, through comparison of simulated versus observed wait time distributions. The rest of the report is organized as follows. Section 2 provides background on ridehailing operations. Section 3 describes and analyzes key variables from a modelling perspective. Section 4 documents the development of a prototype model for ridehailing service provision and operations. Section 5 presents and discusses model results. Section 6 concludes the report with discussion on the applicability and limitations of the model and the insights it can provide for the Vehicle for Hire Bylaw Review.
Report 5: The Relationship Between PTC and Public Transit: Descriptive Analysis
(University of Toronto Transportation Research Institute, 2019-04) Li, Wenting; Shalaby, Amer; Habib, Khandker Nurul
This report is a technical support document for the City of Toronto’s Vehicle for Hire Bylaw Review, prepared by the University of Toronto Transportation Research Institute (UTTRI). It aims to investigate the relationship between Private Transportation Companies (PTC) and public transit in the City of Toronto. The analysis includes a comparison of the travel patterns of PTC and public transit users for different trip markets over time of day, impacts of subway service disruption on PTC usage and comparison of the travel attributes (e.g. travel time and a number of transfers) of PTC and public transit for equivalent trips. The investigation was based on multiple datasets including 17,837,489 records of PTC trips made in the City of Toronto from Sept 2016 to Apr 2017, 2016 Transportation Tomorrow Survey (TTS) data on weekday public transit ridership in Toronto, TTC subway disruption log data from Sept 2016 to Apr 2017, and the estimated transit travel attributes of the fastest transit alternative of the PTC trips using the OpenTripPlanner API. The rest of the report is organized as follows. Section 2 presents the literature review of previous studies on the topic of PTC and public transit. Sections 3 to 5 illustrate the investigation of the relationship between PTC and public transit. Specifically, Section 3 presents the comparison of travel patterns of the riders of the two modes for different trip markets across a typical 24-hour weekday. Section 4 investigates the impacts of subway disruptions on the demand for PTC usage. Section 5 explores the differences in transportation service of PTC and public transit in terms of total travel time, in-vehicle travel time, out-of-vehicle travel time and walking distance. Moreover, the section incorporates temporal and spatial analyses which investigate the temporal and spatial variance in the travel attributes difference. Section 6 provides a summary of the key findings of the descriptive analysis completed for this report.
Report 4: Evaluating the Impacts of Private Transportation Companies on Travel Behaviour through a Stated Preference (SP) Survey
(University of Toronto Transportation Research Institute, 2019-05) Loa, Patrick; Hawkins, Jason; Habib, Khandker Nurul
This technical report presents the work undertaken in support of the City of Toronto’s Vehicle for Hire Bylaw Review by the University of Toronto Transportation Research Institute (UTTRI) and it presents the analysis of the factors that influence the residents’ (of the City of Toronto) choices of using or not using exclusive and shared ride-hailing services in the City. The investigation is based on data obtained through a specialized travel survey that uses a Stated Preference (SP) technique build on Revealed Preference (RP) information of daily travel. The survey, named the “Survey to Predict the Repercussions of the Introduction of Novel Transportation Network Services”: (SPRINT), collected information from a random sample of residents (selected from a market research panel) of the City of Toronto. Respondents were asked a series of questions pertaining to personal and household characteristics, information on the extent to which respondents use ride-hailing services, and their familiarity with and perceptions of ride-hailing services. In addition, respondents were asked to complete a series of real (revealed) and hypothetical (stated) preference questions, which were used to understand the trade-offs that people make when choosing a mode of travel in the City. The rest of this report is organized as follows. Section 2 summarizes the goals of and motivation for the survey and presents a discussion of the factors that influenced the design of each component of the survey. Section 3 provides an analysis of the data obtained through the SPRINT. The analysis of the results is comprised of three elements: descriptive statistics, usage, and perceptions of ride-hailing services, and model results. Section 4 summarizes the key findings of the report.
Report 3: Taxi Time-Series Analysis
(University of Toronto Transportation Research Institute, 2019-04) Ozonder, Gozde; Miller, Eric J
This technical report presents the work undertaken in support of the City of Toronto’s Vehicle for Hire Bylaw Review by the University of Toronto Transportation Research Institute (UTTRI) to analyze the patterns in taxi usage over a 20-year period as recorded in the five most recent Transportation Tomorrow Surveys (TTSs): 1996 TTS; 2001 TTS; 2006 TTS; 2011 TTS; and 2016 TTS. The trip records in the TTS data have socioeconomic attributes of trip-makers (e.g., age, sex, etc.), their household characteristics (household size, number of vehicles owned, etc.) and trip attributes attached (Data Management Group – Reports, n.d.). They therefore provide a statistically representative description of taxi-users and their reasons for travel along with the spatiotemporal attributes of the trips. Previous studies have shown that the profiles of Uber-users and taxi-users are considerably different (Habib, 2019; Ozonder and Miller, 2019). Thus, the purpose of this study as documented in this report is to identify changes or stabilities in the taxi-user group and their trip patterns by comparing various distributions through a longitudinal analysis. This report is one of a series of project reports by the UTTRI team. It complements Report No. 1 (which examined PTC usage as reported in the 2016 TTS) and Report No. 2 (which compared PTC usage as reported in the 2016 TTS with the VfH PTC data). The rest of the report is organized as follows. Section 2 reports the results of taxi time-series analysis in three parts: in the first part, it discusses the attributes of trip-makers; in the second part, it compares distributions in household attributes over the years; in the third part, it explains the analysis results of the trip patterns. Section 3 concludes the report with a summary of findings.
Report 2: Comparison of 2016 PTC and 2016 TTS
(University of Toronto Transportation Research Institute, 2019-04) Ozonder, Gozde; Miller, Eric J.
This technical report presents the work undertaken in support of the City of Toronto’s Vehicle for Hire Bylaw Review by the University of Toronto Transportation Research Institute (UTTRI) to analyze and compare the patterns in the usage of ride-sourcing services provided by the Private Transportation Companies (PTCs) as recorded by these companies and as recorded in the 2016 Transportation Tomorrow Survey (TTS). The 2016 TTS conducted in the fall (September-December) 2016 time period collected information on Uber trips as an explicit mode of travel (Ashby, 2018; Miller, et al., 2019). The trip records in the data have socioeconomic attributes of trip-makers (e.g., age, sex, income class, etc.), their household characteristics (household size, number of vehicles owned, etc.) and trip purposes attached (Data Management Group – Reports, n.d.). They, therefore, provide a statistically representative description of the users of the services provided by PTCs and their reasons for travel along with time and start/end locations of the trips. Despite the relatively modest market penetration of PTCs (only Uber at the time) in the City during the fall of 2016, TTS provides a considerably richer description of PTC travel and trip-makers than can be obtained from the PTC records alone, since PTC records do not have user-attributes or trip purposes attached (Miller, et al., 2019). Thus, if it can be shown that the trip records in the 2016 TTS data set can successfully represent the patterns observed in the actual usage of the services, statistical models can be built to impute further attributes associated with the trips recorded by the PTCs, such as the corresponding trip purposes, user attributes, etc. The purpose of this study as documented in this report is to identify the spatiotemporal patterns in both data sets and determine the similarities and/or discrepancies by examining the distributions of the trips mainly considering their time- and space-related attributes. This report is one of the deliverables by the UTTRI team and it complements Report No. 1 which focusses on the demographics of the trip-makers and their household characteristics as recorded in the 2016 TTS. The rest of the report is organized as follows. Section 2 reports the results of the comparative analyses. Section 3 concludes the report with a summary of findings.
Report 1: Analysis of PTC Usage as Recorded in the 2016 TTS
(University of Toronto Transportation Research Institute, 2019-04) Ozonder, Gozde; Miller, Eric J.
This technical report presents the work undertaken by the University of Toronto Transportation Research Institute (UTTRI) to analyze the usage of services provided by Private Transportation Companies (PTCs) as recorded in the 2016 Transportation Tomorrow Survey (TTS) in support of the City of Toronto’s Vehicle for Hire Bylaw Review. The 2016 TTS conducted in the fall (September-December) 2016 time period collected information on Uber trips as an explicit mode of travel (Ashby, 2018; Miller, et al., 2019). The trip records in the data have socioeconomic attributes of trip-makers (e.g., age, sex, income class, etc.), their household characteristics (household size, number of vehicles owned, etc.) and trip purposes attached (Data Management Group – Reports, n.d.). They, therefore, provide a statistically representative description of the users of the services provided by PTCs and their reasons for travel along with time and start/end locations of the trips. Despite the relatively modest market penetration of PTCs (only Uber at the time) in the City during the fall of 2016, TTS provides a considerably richer description of PTC travel and trip-makers than can be obtained from the PTC records alone, since PTC records do not have user-attributes or trip purposes attached (Miller, et al., 2019). This report is one of the deliverables by the UTTRI team, where the main focus is on the demographics of the trip-makers and their household characteristics. PTC trip records from the same September-December 2016 time period are compared to the 2016 TTS PTC trip records to investigate both spatial and temporal usage of the services provide by PTCs in a complementary report (Project Report No. 2), hence, trips are not discussed in detail in this report. The rest of the report is organized as follows. Section 2 reports the results of the analysis of descriptive statistics of PTC user attributes and their household characteristics, along with a concise overview on trips. Section 3 reviews three studies conducted using the 2016 TTS with a focus on PTC usage. Section 4 concludes the report with a summary of findings.

Browse

Recent Submissions