Greater Toronto And Hamilton Area Research Articles

On the factors affecting the choice of regional transit for commuting in Greater Toronto and Hamilton Area: Application of an advanced RP-SP choice model

This paper presents a jointly estimated Revealed Preference – Stated Preference (RP-SP) choice model to explore mode choice behavior of commuters in a multimodal regional transportation system in the Greater Toronto and Hamilton Area (GTHA). The paper focuses on the commuting trips that are long enough to be served by more than one transit services (local and regional transit services) in the GTHA (hence denoted by cross-regional commuting). This type of trips represents a unique travel market for which multimodal transportation services compete with each other. The study uses a dataset collected by a purposely designed SP pivoted on RP choices of cross-regional commuters in the GTHA. An advanced RP-SP model structure is specified to enhance parameter estimation from the SP choices by explicitly capturing the correlations between the SP choices and corresponding elicited confidence ratings. The econometric model also accounts for serial correlations between SP choices of the same respondents as well as inertia effects between RP and SP choices. The estimated choice model is used to predict the effectiveness of different strategies on commuting mode choice behaviour of cross-regional commuters. The results of the empirical investigation revealed many behavioural details such as the effect of eliminating co-fare between local and regional transit services and providing Wi-Fi on regional transit vehicles on increasing the share of regional transit modes. It is found that monetizing park and ride at transit stations will deter some individuals from using transit, however, this strategy can be considered as a way to manage the increasing parking demand at regional transit stations and to improve the access time of park and ride users.

‘There is always this feeling of otherness’: exploring the lived experiences of visually impaired immigrant women in Canada

This paper explores the experiences of immigrant women with visual impairment in the Greater Toronto and Hamilton Area (GTHA) of Canada. Conceptually, the paper draws from feminist disability theory and critical scholarship on blindness to consider the discursive and material processes shaping women’s experiences across different socio-cultural contexts. Empirically, the paper draws from in-depth interviews with seven women. The analysis is organized around three themes. The first explores women’s understandings of visual impairment. The second examines the ways in which women’s experiences of visual impairment are shaped by meanings and attitudes that circulate within their cultural communities, and the extent to which immigration might challenge or reinforce these meanings. The third examines the shifting barriers and opportunities women face with respect to social and economic participation. We close by identifying the conceptual implications of our research.

Spatio-temporal analysis of particulate matter intake fractions for vehicular emissions: Hourly variation by micro-environments in the Greater Toronto and Hamilton Area, Canada

Previous investigations have reported intake fraction (iF) for different environments, which include ambient concentrations (outdoor exposure) and microenvironments (indoor exposure). However, little is known about iF variations due to space-time factors, especially in microenvironments. In this paper, we performed a spatio-temporal analysis of particulate matter (PM2.5) intake fractions for vehicular emissions. Specifically, we investigated hourly variation (12:00am–11:00pm) by micro-environments (residences and workplaces) in the Greater Toronto and Hamilton Area (GTHA), Canada. We used GIS modeling to estimate air pollution data (ambient concentration, and traffic emission) and population data in each microenvironment. Our estimates showed that the total iF at residences and workplaces accounts for 85% and 15%, respectively. Workplaces presented the highest 24h average iF (1.06ppm), which accounted for 25% higher than residences. Observing the iF by hour at residences, our estimates showed the highest average iF at 2:00am (iF=3.72ppm). These estimates indicate that approximately 4g of PM2.5 emitted from motor vehicles are inhaled for every million grams of PM2.5 emitted. For the workplaces, the highest exposure was observed at 10:00am, with average iF equal to 2.04ppm. The period of the day with the lower average iF for residences was at 8:00am (average iF=0.11ppm), while for the workplaces was at 4:00am (average iF=0.47ppm). Our approach provides a new perspective on human exposure to air pollution. Our results showed significant hourly variation in iF across the GTHA. Our findings can be incorporated in future investigations to advance environmental health effects research and human health risk assessment.

Modeling spatial patterns of link-based PM2.5 emissions and subsequent human exposure in a large canadian metropolitan area

Understanding the relationship between mobile source emissions and subsequent human exposure is crucial for emissions control. Determining this relationship over space is fundamental to improve the accuracy and precision of public policies. In this study, we evaluated the spatial patterns of link-based PM2.5 emissions and subsequent human exposure in a large Canadian metropolitan area - the Greater Toronto and Hamilton Area (GTHA). This study was performed in three stages. First, we estimated vehicle emissions using transportation models and emission simulators. Then we evaluated human exposure to PM2.5 emissions using the Intake fraction (iF) approach. Finally, we applied geostatistical methods to assess spatial patterns of vehicle emissions and subsequent human exposure based on three prospective goals: i) classification of emissions (Global Moran's I test), ii) level of emission exposure (Getis-Ord General G test), and; iii) location of emissions (Anselin Local Moran's I). Our results showed that passenger vehicles accounted for the highest total amount of PM2.5 emissions, representing 57% emissions from all vehicles. Examining only the emissions from passenger vehicles, on average, each person in the GTHA inhales 2.58 × 10−3 ppm per day. Accounting the emissions from buses and trucks, on average each person inhales 0.12 × 10−3 and 1.91 × 10−3 ppm per day, respectively. For both PM2.5 emissions and human exposure using iF approach, our analysis showed Moran's Index greater than 0 for all vehicle categories, suggesting the presence of significant clusters (p-value <0.01) in the region. Our study indicates that air pollution control policy must be developed for the whole region, because of the spatial distribution of housing and businesses centers and inter-connectivity of transportation networks across the region, where a policy cannot simply be based on a municipal or other boundaries.

Examining the difference between park and ride and kiss and ride station choices using a spatially weighted error correlation (SWEC) discrete choice model

This paper presents a novel discrete choice model formulation: the spatially weighted error correlation (SWEC) logit model for spatial location choices. This model captures both the correlation between spatially distinct alternatives based on the relative distance between them and the heteroskedasticity of the errors of the alternative as a function of their relative distance to the decision maker. The SWEC model is applied for the estimation of models of transit station choice for P-and-R as well as kiss and ride (dropped off at transit station) transit commuters in the Greater Toronto and Hamilton Area (GTHA). The kiss and ride model has particular relevance due to its ability to capture household tradeoffs made by both the driver and the passenger. The tradeoffs are captured by utilizing the subsequent trip made by the driver in the utility function specification. The proposed model structure provides additional insights into how station choice occurs for such complex trips. Finally, the application of the SWEC model for both choice contexts provides a fundamental improvement over conventional approaches, as it is able to capture non-proportional substitution patterns and heteroskedasticity inherent with spatial choices.

Testing Uncertainty in ILUTE - An Integrated Land Use -Transportation Micro-simulation Model of Demographic Updating

In microsimulation models, the behaviours of agents as intelligent objects are modeled through various behavioral modeling techniques, which give rise to uncertainties in the modeling workflow and predicted outputs. ILUTE (Integrated Land Use Transportation Environment model) is an agent-object-based microsimulation model system designed to simulate different activities in a city, in which many agents (persons, families, households, firms, etc.) intelligently and/ or randomly act and interact in a complex way. A 150,000 (150K) synthetic base year (1986) household sample of the Greater Toronto and Hamilton Area (GTHA) population is used as the starting point for undertaking twenty-year historical simulations (1986-2006). In order to analyze the uncertainty associated with stochastic behaviours in the ILUTE model system, a 1000 independent runs using different initial random number seeds are generated and tested through parallel programming and using high performance cloud computing (HPC). In this paper, the behaviour of the demographic updating module is tested by examining the variability and the distribution of its outputs including births, deaths, families, and households across simulation runs. The test results show that all the simulated outputs’ distributions are generally normally, or very close to normally, distributed. In general, ILUTE can simulate demographic updating process with considerable reliability and confidence.

The design of electric vehicle charging network

The promotion of Electric Vehicles (EVs) has become a key measure of the governments in their attempt to reduce greenhouse gas emissions. However, range anxiety is a big barrier for drivers to choose EVs over traditional vehicles. Installing more charging stations in appropriate locations can relieve EV drivers’ range anxiety. To determine the locations of public charging stations, we propose two optimization models for two different charging modes - fast and slow charging, which aim at minimizing the total cost while satisfying certain coverage goal. Instead of using discrete points, we use geometric objects to represent charging demands. Importantly, to resolve the partial coverage problem (PCP) for networks, we extend the polygon overlay method to split the demands on the road network. After applying the models to Greater Toronto and Hamilton Area (GTHA) and to Downtown Toronto, we show that the proposed models are practical and effective in determining the locations of charging stations. Moreover, they can eliminate PCP and provide much more accurate results than the complementary partial coverage method (CP).

Non-stop equity: Assessing daily intersections between transit accessibility and social disparity across the Greater Toronto and Hamilton Area (GTHA)

Public transportation systems generate economic benefits that can potentially reduce social disparities between populations when such benefits are distributed evenly within a region. However, the achievement of equity in the allocation of public resources is not easy to accomplish for land use and transportation planning agencies. This research seeks to determine whether people residing in socially disadvantaged areas in the Greater Toronto and Hamilton Area (GTHA), Canada, experience the same levels of transit accessibility as those living in other areas over the course of a day. Comparisons are presented in terms of regional accessibility, trends by social decile, spatial distribution of accessibility during the day, and travel time impacts. Findings suggest that residents in socially disadvantaged areas have equitable if not better transit accessibility to jobs than socially advantaged groups, and this is reflected in shorter travel times. However, the degree and impact of this advantage varies over the course of the day. Findings from this research can be of interest to transportation planners, engineers, and policy makers as it highlights deficiencies with current equity assessment practices that do not take into account variation in transit services over a 24-h time period.

Evolution of latent modal captivity and mode choice patterns for commuting trips: A longitudinal analysis using repeated cross-sectional datasets

This paper presents an investigation of the temporal evolution of commuting mode choice preference structures. It contributes to two specific modelling issues: latent modal captivity and working with multiple repeated crossectional datasets. In this paper latent modal captivity refers to captive reliance on a specific mode rather than all feasible modes. Three household travel survey datasets collected in the Greater Toronto and Hamilton Area (GTHA) over a ten-year time period are used for empirical modelling. Datasets collected in different years are pooled and separate year-specific scale parameters and coefficients of key variables are estimated for different years. The empirical model clearly explains that there have been significant changes in latent modal captivity and the mode choice preference structures for commuting in the GTHA. Changes have occurred in the unexplained component of latent captivities, in transportation cost perceptions, and in the scales of commuting mode choice preferences. The empirical model also demonstrates that pooling multiple repeated cross-sectional datasets is an efficient way of capturing behavioural changes over time. Application of the proposed mode choice model for practical policy analysis and forecasting will ensure accurate forecasting and an enhanced understanding of policy impacts.

Predictors of driving among families living within 2 km from school: Exploring the role of the built environment

Rates of active school travel have declined over time in line with decreasing levels of physical activity among children. Understanding why some children are driven to school when walking is an option is important for future intervention. The purpose of this study was to evaluate demographic characteristics, parent/family level factors, attitudinal and psychosocial factors, route features, access to vehicles, and environmental factors which appear to influence the decision to drive children who live within a walkable distance from school in the Greater Toronto and Hamilton Area (GTHA), Canada. Computer-aided telephone interviews were conducted in 2009 with 1001 parents of children attending elementary school in the GTHA, Canada׳s largest urbanized region. Hierarchical logistic regression analyses were conducted to examine the household demographics, parent/family factors, attitudinal and psychosocial factors, route features, access to vehicles, and environmental factors (qualities of urban design) that predict driving among households less than 2km away from school (n=529). In the GTHA, attitudes and psychosocial factors have a stronger role to play than the built environment in influencing driving behavior within close proximity to school. The introduction of built environment characteristics in the final regression model only explained an additional 4% of the variance in driving to school. To promote AST among drivers living within a walkable distance from school, non-infrastructure programs that provide adult supervision, such as Walking School Bus Schemes, may be a more powerful strategy to alleviate safety concerns while potentially reducing the parental time costs of escorting the child, than capital projects targeting changes to the built environment.

A GEV model with scale heterogeneity for investigating the role of mobility tool ownership in peak period non-work travel mode choices

The paper presents results of investigation on mode choice behaviour for peak period non-work trips (trips that are made during the peak period and are not linked to any other trips). Pure non-work trips within the peak period represent a significant portion of peak period traffic. In the case of the Greater Toronto and Hamilton Area (GTHA), the study area of this investigation, around 11 percent of peak period trips are pure non-work trips and auto driving is the dominant mode. Also more peak period non-work trips are made using the auto passenger mode than all transit modes combined. This heavy auto dependency for pure non-work trips in the peak period, when transit service is at its highest throughout the day, requires an improved understanding of such mode choice behaviour. This paper uses data from a household travel survey collected in the GTHA to investigate peak period pure non-work trip mode choice in the context of household mobility tool ownership (auto and transit pass ownership). The paper also proposes an advanced econometric modelling approach for capturing preference heterogeneity as well as scale heterogeneity (heteroskedasticity). The model involves systematic parameterization of the scale parameter of a GEV model. Empirical models highlight the superiority of the proposed model over a homoskedastic model. Empirical model also explains the influence of household mobility tool ownership on peak period pure non-work trip mode choices in terms of explaining both preference heterogeneity and scale heterogeneity.

Modeling Mobility Tool Ownership of Youth in Toronto, Ontario, Canada

This paper presents the findings of the modeling of mobility tool ownership of the youth population in the greater Toronto and Hamilton area (GTHA) in Ontario, Canada. Research on mobility tool ownership is limited. The key contribution of the research is to enhance the understanding of how neighborhood characteristics and accessibility affect mobility tool ownership among GTHA youth. The study exclusively considered youth between the ages of 17 and 19 years; at this age the subjects passed through a transitional stage in the GTHA because of opportunities for ownership of types of mobility tools. This study used data from the 2006 Transportation Tomorrow Survey, a household-based travel survey of the GTHA. The study used a latent class choice modeling approach to account for unobserved heterogeneity, which was often ignored in traditional choice modeling. The results suggest that the latent class logit model outperforms the conventional multinomial logit model according to model fit and its ability to evaluate various parameters across latent classes. Several sociodemographic characteristics, trip attributes, accessibility measures, and neighborhood characteristics were found to explain different types of mobility tool ownership of the youth population. Finally, the results revealed that latent heterogeneity existed in the sampled population. The research offers important behavioral insights into the formation of travel habits of youth that could be useful in shaping the travel behavior of Toronto's young adults.

Park-and-Ride Access Station Choice Model for Cross-Regional Commuting

The paper presents an investigation of park-and-ride access station choices of cross-regional commuters in the Greater Toronto and Hamilton area (GTHA). Data from a household travel survey conducted in 2006 in the GTHA were used for this empirical investigation. The household travel survey data were supplemented by data from transit service operators on park-and-ride station locations, parking lot capacities, parking costs, surrounding land use, and station amenities. Three groups of park-and-ride users were defined: (a) individuals for whom only local transit Toronto Transit Commission (TTC) subway stations were within reasonable reach, (b) individuals for whom only regional transit (GO Train) stations were within reach, and (c) individuals for whom both GO Train and TTC subway stations were within reach. Different model structures and specifications were tested, and three discrete choice models were estimated. Empirical models revealed that access distance and the relative station direction (toward the workplace) were the primary factors that affected transit station choice for park-and-ride options. However, for station distance and relative station direction, commuters were more sensitive to changes in station access distance than to changes in the relative station direction from their households. In addition, the empirical models revealed that local transit park-and-ride users were less sensitive to access distance than were regional transit park-and-ride users. The results of this investigation could be useful in future transit station design projects to attract more commuters to use park-and-ride facilities.

Spatiotemporal analysis of urban environment based on the vegetation–impervious surface–soil model

This study explores a spatiotemporal comparative analysis of urban agglomeration, comparing the Greater Toronto and Hamilton Area (GTHA) of Canada and the city of Tianjin in China. The vegetation–impervious surface–soil (V–I–S) model is used to quantify the ecological composition of urban/peri-urban environments with multitemporal Landsat images (3 stages, 18 scenes) and LULC data from 1985 to 2005. The support vector machine algorithm and several knowledge-based methods are applied to get the V–I–S component fractions at high accuracies. The statistical results show that the urban expansion in the GTHA occurred mainly between 1985 and 1999, and only two districts revealed increasing trends for impervious surfaces for the period from 1999 to 2005. In contrast, Tianjin has been experiencing rapid urban sprawl at all stages and this has been accelerating since 1999. The urban growth patterns in the GTHA evolved from a monocentric and dispersed pattern to a polycentric and aggregated pattern, while in Tianjin it changed from monocentric to polycentric. Central Tianjin has become more centralized, while most other municipal areas have developed dispersed patterns. The GTHA also has a higher level of greenery and a more balanced ecological environment than Tianjin. These differences in the two areas may play an important role in urban planning and decision-making in developing countries.

Investing in Ontario's Infrastructure for Economic Growth and Prosperity

This paper documents the potential employment and other value-added benefits of infrastructure investments in Ontario. In particular, it details the impact of infrastructure investment as a counter-cyclical fiscal policy tool. A review of the recent literature reveals that infrastructure investment serves both as a tool for job creation and as a stimulus for the economy as a whole. During recessionary times infrastructure investments have been able to boost the economy and have served as a primary job creation tool, especially when the private sector investments have dried up. At the same time, these investments have produced the infrastructure necessary to support future economic growth. Published research also revealed that the input-output models have been the preferred tool to capture the relationship between infrastructure investments and their impact on economy. Thus, the primary aim of this paper is to illustrate the impact of infrastructure investment on job creation, GDP, and tax revenue in the Province of Ontario using an input-output model. The paper documents the results of a simulation exercise, using an input-output model of the Ontario economy based on a 2008 industry structure that estimated the economic impact of a $12-billion public sector (government) investment in non-residential building and engineering construction in Ontario. This infrastructure investment is estimated to have a $38.4-billion impact on the province’s economy. The $12-billion investment would create an estimated 203,000 jobs (person-years of employment) in the provincial economy, generate an estimated $10 billion in employment income and increase the provincial GDP by an estimated $18.5 billion. This investment would also generate an estimated $668.7 million in corporate taxes and $161.2 in personal income taxes. When the impacts are normalized to a $1-billion stimulus, the IO simulations revealed that during the 2008 recessionary period in Ontario, every billion dollar invested in non-residential building and engineering construction would create almost 17,000 new jobs. Of those, the model estimated 3,050 jobs in direct impact, 2,850 jobs in indirect impact, and 11,000 jobs in induced impact. These benefits are in addition to the longer term direct benefits of infrastructure investments that would result from the use of new or rehabilitated infrastructure. For instance, a $12-billion investment in transportation infrastructure could result in a significant improvement in accessibility and mobility in the region, and related improvements in labour and business productivity. The above-mentioned monetized impacts of infrastructure investments, however, are above and beyond the positive impacts on accessibility and productivity. Finally, the paper reviews transportation infrastructure plans for the Greater Toronto and Hamilton Area (GTHA) and discusses the investment tools identified recently by various agencies. The paper concludes that public-private partnerships for infrastructure development will promote greater use of private capital in the construction and development of public infrastructure. User fees and those taxes that are likely to modify consumer behaviour are recommended as the preferred tools for revenue generation to support the construction of new infrastructure. We recommend that sufficient funds should be made available for infrastructure development and renewal in Canada to maintain the competitiveness of Canadian businesses. The opportunity cost of not spending now reduces the productive capacity of the economy in the future, especially when public capital investments in infrastructure have the potential to generate immediate and substantial productivity gains.

Joint modelling of propensity and distance for walking-trip generation

The paper presents an investigation on walking-trip generation for commuting. It uses advanced econometric models to investigate the importance of considering walking distance jointly with walking propensity in the walking-trip-generation models. Empirical models are estimated by using large-scale household travel survey data collected in the Greater Toronto and Hamilton Area (GTHA) in 1996, 2001 and 2006. Empirical models clearly validate the proposal that for walking, travel distance should be considered jointly with the propensity. The relationship between auto-ownership and walking-trip generation is proved to be very strong, referring that high auto-dependent household and neighbourhoods discourage walking. The empirical models reveal that the baseline walking propensity and distance remain unchanged over the years despite significant efforts to encourage active transportation through mixed land-use policies in the GTHA. This implies that, in addition to land-use policies, more rigorous applications of public education and social marketing are necessary.

Modelling departure time choices by a Heteroskedastic Generalized Logit (Het-GenL) model: An investigation on home-based commuting trips in the Greater Toronto and Hamilton Area (GTHA)

The paper presents an econometric model for departure time choice modelling. The proposed model is a discrete choice model with latent choice sets. As per the formulation of the mode, the model falls in the general category of Generalized Extreme Value (GEV) models with choice set formation, which is also known as a Generalized Logit (GenL) model. However, the proposed modelling framework uses a scale parameterization approach to capture heteroskedasticity in departure time choices. Hence, the model presented in the paper is a Heteroskedastic Generalized Logit (Het-GenL) model in general or specifically a heteroskedastic Paired Combinatorial Logit Model (Het-PCL). Empirical models are developed for the departure time choices for home-based commuting trips in the Greater Toronto and Hamilton Area (GTHA). The datasets from the Transportation Tomorrow Survey, a 5 percent household based trip diary survey conducted in 2006 is used for empirical model estimation. Separate models are estimated for private car and transit users’ departure time choices. It becomes evident that transportation level-of-service attributes enter into the systematic utility function as well as the scale parameter function with significant coefficients. The proposed econometric approach captures the normalization effect of different variables in terms of simultaneously influencing systematic utility as well as the scale parameter and thereby correctly explains the elasticity of corresponding variables.

Transferability and enhancement of a microsimulation model for estimating urban commercial vehicle movements

This study examines the transferability of a microsimulation framework to estimate the movements of commercial vehicles in urban areas. We build on the pioneering efforts of Hunt and Stefan (2007) from Calgary, Alberta by considering three types of movements: tour-based; fleet allocator; and internal/external. Some sub-models from Calgary are transferred, while others are newly developed. The framework is implemented and validated for the Greater Toronto and Hamilton Area (GTHA) as a case study. A list of 185,790 records of individual business establishments in the GTHA was acquired from InfoCanada to conduct the microsimulations. The achieved results are promising with favorable correlations between observed and simulated commercial vehicle flows. We conclude that the modeling framework from Calgary can be transferred and used to depict the travel patterns of commercial vehicle movements in other urban areas. This can be beneficial when refined and costly data are unavailable to study urban commercial vehicle movements.

The school run: Exploring carpooling as an intervention option in the Greater Toronto and Hamilton Area (GTHA), Canada

The aims of this study were to identity the prevalence of carpooling as a school travel mode in the Greater Toronto and Hamilton Area (GTHA) and to examine attitudes toward automobile school travel and carpooling among adults who drive their children to school. Telephone interviews were conducted with 1,001 GTHA parents/guardians of elementary school-aged children. Analyses indicated that 1.7% of the sample used carpooling as the primary school travel mode in the a.m., while 33.8% of the sample drove their child to school in the a.m. One quarter (25%) of the total sample had participated in a carpool for school travel with neighbors or friends at times. The main reasons for automobile school travel were convenience and safety. Those drivers who indicated carpooling to be more convenient reported carpooling to be more appealing, to interfere less with their current household schedule, were more interested in carpooling, placed a greater importance on using an environment-friendly travel mode, and had a greater proportion of non-English speakers than drivers who indicated carpooling to be inconvenient. These findings confirm that carpooling is an under-utilized school travel mode, and that there may be some scope in intervening among parents/guardians who perceive carpooling to be potentially convenient.

Exploring and modeling the level of service of urban public transit: the case of the Greater Toronto and Hamilton Area, Canada

This study assesses the availability of public transit (i.e. supply) in the Greater Toronto and Hamilton Area (GTHA), Canada. We examine transit service intensity at the census tract level by assembling and analyzing a suitable GIS database for the study area. We make use of a version of the ‘Local Index of Transit Availability’ (LITA) to measure and explore service levels based on the coverage, capacity, and frequency of the transit system. Generated LITA measures are regressed against a number of locational covariates to establish a statistical relationship between the level of transit service and the characteristics of the site transit is servicing. We utilize a simultaneous auto-regressive (SAR) model to account for spatial effects and correct for the estimation bias that usually arises from the conventional Ordinary Least Squares (OLS) modeling approach. Results from exploring LITA indicate that the core areas of municipalities were not necessarily well serviced by public transit. Suburban peripheral tracts and those adjacent to the shoreline were characterized by average transit service at best, and areas adjacent to municipal borders indicated discontinuity in transit service. Furthermore, the multivariate analyses suggest that population density and income, as well as the presence of certain socio-economic groups in a tract (e.g. recent immigrants, young adults and elderly) can serve as key locational variables when examining transit availability.