Matthew J. Roorda

Relative Accessibility Deprivation Indicators for Urban Settings: Definitions and Application to Food Deserts in Montreal

Accessibility research, within the context of the social exclusion dimensions of transport, has provided valuable tools to understand the potential of people to reach daily life activity locations. In this paper, model-based estimates of distance travelled are used to calculate a cumulative opportunities measure of accessibility. Multivariate, spatially expanded models produce estimates of distance travelled that are specific to both geographical location and type of individual. Opportunity landscapes obtained based on these estimates are used for comparative accessibility analysis by means of what are termed relative accessibility deprivation indicators. The indicators proposed are demonstrated with a case study of food deserts in the city of Montreal, Canada. The results of the analysis illustrate the variations in accessibility between individuals in low-income households and the reference group, and the effect of vehicle ownership for accessibility to food services, thus highlighting the social exclusion implications of these factors.

Accessibility to health care facilities in Montreal Island: an application of relative accessibility indicators from the perspective of senior and non-senior residents

BackgroundGeographical access to health care facilities is known to influence health services usage. As societies age, accessibility to health care becomes an increasingly acute public health concern. It is known that seniors tend to have lower mobility levels, and it is possible that this may negatively affect their ability to reach facilities and services. Therefore, it becomes important to examine the mobility situation of seniors vis-a-vis the spatial distribution of health care facilities, to identify areas where accessibility is low and interventions may be required.MethodsAccessibility is implemented using a cumulative opportunities measure. Instead of assuming a fixed bandwidth (i.e. a distance threshold) for measuring accessibility, in this paper the bandwidth is defined using model-based estimates of average trip length. Average trip length is an all-purpose indicator of individual mobility and geographical reach. Adoption of a spatial modelling approach allows us to tailor these estimates of travel behaviour to specific locations and person profiles. Replacing a fixed bandwidth with these estimates permits us to calculate customized location- and person-based accessibility measures that allow inter-personal as well as geographical comparisons.DataThe case study is Montreal Island. Geo-coded travel behaviour data, specifically average trip length, and relevant travellers attributes are obtained from the Montreal Household Travel Survey. These data are complemented with information from the Census. Health care facilities, also geo-coded, are extracted from a comprehensive business point database. Health care facilities are selected based on Standard Industrial Classification codes 8011-21 (Medical Doctors and Dentists).ResultsModel-based estimates of average trip length show that travel behaviour varies widely across space. With the exception of seniors in the downtown area, older residents of Montreal Island tend to be significantly less mobile than people of other age cohorts. The combination of average trip length estimates with the spatial distribution of health care facilities indicates that despite being more mobile, suburban residents tend to have lower levels of accessibility compared to central city residents. The effect is more marked for seniors. Furthermore, the results indicate that accessibility calculated using a fixed bandwidth would produce patterns of exposure to health care facilities that would be difficult to achieve for suburban seniors given actual mobility patterns.ConclusionsThe analysis shows large disparities in accessibility between seniors and non-seniors, between urban and suburban seniors, and between vehicle owning and non-owning seniors. This research was concerned with potential accessibility levels. Follow up research could consider the results reported here to select case studies of actual access and usage of health care facilities, and related health outcomes.

Built Environment and School Travel Mode Choice in Toronto, Canada

Walking to or from school may provide a regular source of physical activity for children and youth. To improve walking practices among this younger population, urban planners emphasize the importance of built environment interventions. Empirical understanding of the potential relationship between the built environment and active school transportation (e.g., walking) is therefore essential to the development of effective planning interventions. In the nexus of empiricism and policy, place-based differences in school transport policy and urbanization processes, which may associate with mode choice, provide the rationale for conducting local research to support local policy development. This study examines the association between the built environment and the likelihood of walking or being driven to or from school. The research also addresses differences in mode choice behavior across morning and afternoon school trips. Binomial logit models were specified to study the school travel outcomes of children aged 11 to 13 years in the city of Toronto, Canada. Distance between the residence and school had the strongest correlation with mode choice; other built environment measures had moderate associations with walking. Importantly, the built environment around a childs residence had a stronger association with mode choice than did the built environment around the school. Furthermore, the effect of the built environment was more apparent for home-to-school trips. This research provides evidence that the built environment may influence school travel mode choice, but planners and community-based organizations should exercise caution when the nature of interventions required to encourage walking among children is determined.

COMPUTERIZED HOUSEHOLD ACTIVITY-SCHEDULING SURVEY FOR TORONTO, CANADA, AREA: DESIGN AND ASSESSMENT

Traditional activity-travel diary surveys have for some time served as the primary source of data for understanding and modeling travel behavior. Recent changes in policy and forecasting needs have led to the development of an emerging class of activity-scheduling process surveys that focus on the underlying behavioral mechanisms that give rise to travel and condition future change. Many of these surveys involve the use of computers for data entry over multiday periods. These changes pose new challenges and opportunities for quality assessment. At this early stage it is more important than ever to document closely the quantity and quality of data provided by such surveys as well as the associated burden and experience of respondents. This study reviews existing quality standards and seeks to develop several new data-quality measures suitable to this emerging class of surveys. Data are used from a recent household activity-scheduling survey of 271 households in Toronto, Ontario, Canada. A detailed description of the survey instrument is provided, along with an in-depth examination of key results that shed light on data quality. Included are results from a separate survey of 31 respondents concerning their experiences and perceptions of the survey. Overall, although the survey was generally successful in tracking both observed patterns and underlying decision processes over a multiday period within a household, it did come at a price in terms of respondent burden. On the basis of these results several new data-quality guidelines are suggested that incorporate use of activity-trip rates, scheduling-step rates, planning-time horizons, and log-in durations. Further specific suggestions for reducing respondent burden are suggested.

Fusing a Bluetooth Traffic Monitoring System With Loop Detector Data for Improved Freeway Traffic Speed Estimation

Anonymous probe vehicle monitoring systems are being developed to measure travel times on highways and arterials based on wireless signals available from technologies such as Bluetooth. Probe vehicle data can provide accurate measurements of current traffic speeds and travel times due to their excellent spatial coverage. However, presently probe vehicles are only a small portion of the vehicles that make up all of the traffic in the network. Alternatively, data from conventional loop detectors cover almost all the vehicles that have traveled along a road section, resulting in excellent temporal coverage. Unfortunately, loop detector measurements can be imprecise; their spatial sampling depends on the loop detector spacing, and they typically only represent traffic speed at the location of the detector and not over the entire road segment. With this complementarity in mind, this article explores several data fusion techniques for fusing data from these sources together. All methods are implemented and compared in terms of their ability to fuse data from loop detectors and probe vehicles to accurately estimate freeway traffic speeds. Data from a Bluetooth traffic monitoring system are fused with corresponding loop detector data and compared against GPS collected probe vehicle data on a stretch of Highway 401 in Toronto, Canada. The analysis shows that through data fusion, even a few probe vehicle measurements from a Bluetooth traffic monitoring system can improve the accuracy of traffic speed estimates traditionally obtained from loop detectors.

Incorporating Within-Household Interactions into Mode Choice Model with Genetic Algorithm for Parameter Estimation

The procedure for estimating a household model of mode choice is described. The tour-based mode choice model incorporates interpersonal interactions within the household explicitly in an agent-based random utility modeling framework. Household interactions include vehicle allocation, ridesharing to joint activities, and drop-off and pickup. Because of the complex nature of the model decision structure, choice probabilities are simulated from direct generation of random utilities rather than through an analytical probability expression. The computational requirements for the simulation are large. Therefore a grid of computers is used in parallel to perform the necessary calculations and a genetic algorithm is used for parameter estimation. A brief description of the model, the full model results, and a discussion of the computational techniques used in parameter estimation are presented.

TORONTO AREA CAR OWNERSHIP STUDY: A RETROSPECTIVE INTERVIEW AND ITS APPLICATIONS

Recent work in the area of comprehensive transportation modeling systems in a microsimulation framework, more specifically auto ownership modeling, has recognized the need for increased experimentation with dynamic models. Implicitly, dynamic models require longitudinal data. A Toronto area car ownership study was conducted to design and administer a longitudinal survey to fulfill the data requirements for such a dynamic model, to validate the survey results, and to conduct preliminary analysis on those results. An in-depth retrospective telephone survey was conducted with the help of a computer aid in Toronto, Canada. Simple univariate analyses were conducted on the data to determine the relationship between characteristics of the household and the occurrence of vehicle transactions, the choice of vehicle type, the duration a vehicle is held, and the degree of consumer loyalty to different types of vehicles.

Stated Adaptation Survey of Activity Rescheduling: Empirical and Preliminary Model Results

This paper describes an analysis of activity rescheduling responses to an unexpected 1-h delay in getting to an activity. To focus on this rescheduling scenario, a stated adaptation survey technique was developed in which the hypothetical scenario of an unexpected 1-h delay was introduced to a randomly chosen activity from the observed 2-day executed activity schedules of respondents. This paper describes the survey technique used to elicit stated adaptation responses to this conflict, an exploratory univariate analysis of the explanatory factors, and the results of a simple multinomial logistic regression model that attempts to explain the combined effects of a variety of explanatory variables. Finally, a comparison is made with other revealed preference data from an interactive scheduling process survey to shed light on the differences in rescheduling strategies for activity conflicts without forewarning. Attributes of the activity are the major influences behind the type of rescheduling actions taken. The only person and household attributes found to influence the response significantly are the involvement of children in the activity and the student status of the individual for whom the delay occurs. Schedule attributes are not found to have a significant effect. Some new evidence suggests that when conflicts occur with little forewarning, people are less likely to skip or change the day of the activity and more likely to shorten it, where possible, or shift it to another part of the day.

Developing A Multi-Scale Multi-Region Input-Output Model

Many efforts have recently been devoted to developing global multi-region input–output (GMRIO) models. Unfortunately, the scales of GMRIO models do not allow them to capture the heterogeneity of regions within a single country. Multi-scale models can provide more comprehensive analyses capable of capturing the interdependencies of the global economy while preserving regional differences. The primary objective of this research is to develop methods for integrating multi-region input–output data sets from multiple spatial scales into multi-scale multi-region input–output (MSMRIO) models. These methods result in models that may have unusual features such as non-square trade coefficient matrices and a mix of industry-by-industry and commodity-by-commodity technical coefficients. To demonstrate the feasibility of MSMRIO modelling, a Canada-centric model was developed. This model includes 47 countries and Canadas 13 subnational regions. A MSMRIO model provides a tool to analyse global issues with a more spatially detailed focus.

Implications of driving patterns on well-to-wheel performance of plug-in hybrid electric vehicles.

This study examines how driving patterns (distance and conditions) and the electricity generation supply interact to impact well-to-wheel (WTW) energy use and greenhouse gas (GHG) emissions of plug-in hybrid electric vehicles (PHEVs). The WTW performance of a PHEV is compared with that of a similar (nonplug-in) gasoline hybrid electric vehicle and internal combustion engine vehicle (ICEV). Driving PHEVs for short distances between recharging generally results in lower WTW total and fossil energy use and GHG emissions per kilometer compared to driving long distances, but the extent of the reductions depends on the electricity supply. For example, the shortest driving pattern in this study with hydroelectricity uses 81% less fossil energy than the longest driving pattern. However, the shortest driving pattern with coal-based electricity uses only 28% less fossil energy. Similar trends are observed in reductions relative to the nonplug-in vehicles. Irrespective of the electricity supply, PHEVs result in greater reductions in WTW energy use and GHG emissions relative to ICEVs for city than highway driving conditions. PHEVs charging from coal facilities only reduce WTW energy use and GHG emissions relative to ICEVs for certain favorable driving conditions. The study results have implications for environmentally beneficial PHEV adoption and usage patterns.