An integrated spatio-temporal GIS toolkit for exploring intra-household interactions

This paper reports on the development of an integrated spatio-temporal GIS toolkit that facilitates the exploration of intra-household interactions. Two tools comprise the toolkit. The first tool, Space-Time Coincidence Analyst, identifies joint activity/travel episodes undertaken by household members. The core of this tool is a set of flexible criteria for classifying episodes as either joint or independent. The second tool, Space-Time Path Visualizer, not only displays space-time paths for household members, but also shows joint episodes undertaken by any two household members together. The toolkit can be applied to any household-based, activity/travel data set so long as required information is specified by the user. To demonstrate its usefulness for research, the toolkit is applied to the TAPS (Toronto Activity Panel Survey) 2002–03 data set. The results suggest that considerable variation exists in the number of joint activity/travel episodes identified using different classification criteria. Specifically, when using restrictive criteria (i.e., same timing, specific activity type/travel mode), only 2,265 joint activity/travel episodes are identified compared to 8,791 when using more flexible criteria. In turn, our results show that certain key attributes for independent and joint activity/travel episodes (i.e., frequency per household, starting time, ending time and duration) also vary under the different classification criteria.

Immigrant status and commute distance: an exploratory study based on the greater Golden Horseshoe

With 8.76 million residents in 2011, the population of Southern Ontario’s Greater Golden Horseshoe (GGH) has grown dramatically over the past decades, driven by net domestic in-migration and immigration. Corresponding to its growth in population, commuting distances and times within the region have grown as well. Yet, despite the number of immigrants that the region attracts on a yearly basis, there is comparatively little information on commute distances. Consequently, this paper examines commuting distance amongst immigrants in the GGH. Specifically, it evaluates commute distance by immigrant status (immigrants and native born), along with how commute distance differs by arrival cohort and ethnic and racial population groups. Results indicate that commute distance increases with increasing duration of residence, with differences by race and ethnicity.     

Impacts of commuting efficiency on congestion and emissions: case of the Hamilton CMA, Canada

This study uses IMULATE (Integrated Model of Urban LAnd use, Transportation, energy and Emissions) to examine the impacts of commuting efficiency on congestion and automobile emissions—specifically, non-methane hydrocarbons, carbon monoxide and nitrogen oxides—in the Hamilton Census Metropolitan Area. Estimates of these externalities are compared for two commuting scenarios: a base scenario of estimated commuting flows for 1991 and an optimal scenario in which the mean commuting time for all workers is minimized. The findings indicate that significant reductions in congestion and automobile emissions are possible by advocating policies that encourage greater commuting efficiency in the locational choices of workers. The analysis of jobs–housing balance as one such means suggests that a considerable proportion of commuting cannot be explained by geographical imbalances in the distributions of jobs and housing, and that workers consider many factors besides commuting costs in their locational choices. It is concluded that policies promoting jobs–housing balance as the principal strategy for facilitating more efficient commuting may not meet the expectations of policy-makers.     

Role of the built environment on trip-chaining behavior: an investigation of workers and non-workers in Halifax,Nova Scotia

Transportation - This study examines the influence of the built environment on trip-chaining behavior. Based on weekday travel in the Halifax Regional Municipality, we develop four separate models...