Exploring neighbourhood-level mobility inequity in Chicago using dynamic transportation mode choice profiles

Abstract

This paper develops a method to dynamically model urban passenger mode trade-offs at fine-grained spatial and temporal scales using data from OpenTripPlanner (OTP) and the City of Chicago’s Transportation Network Providers (TNP) dataset. This approach can be used to calculate dynamic modal cost-distance trade-offs for specific times, routes, and geographic areas of interest, providing a framework for creating aggregate mode choice profiles for individual cities and neighbourhoods that can be used to assess structural differences in transportation investment and mobility, as well as to test various assumptions about travel behaviour, observe temporal changes in modal trade-offs, and model the system-wide implications of changes to the transportation system to modal trade-offs. Using this dynamic mode choice framework, this paper explores the features underlying observed structural heterogeneity in the ratio of cost to distance (i.e., speed or potential mobility) for observed flows across the city for each mode. It finds that Census tracts with larger proportions of Black and Hispanic population tend to have significantly larger cost-distance ratios (i.e., slower speeds/lower potential mobility) for non-auto modes, while Census tracts with higher proportions of “creative class” employment and features of walkable built environments have significantly lower cost-distance ratios (i.e., faster speeds/higher potential mobility).

Keywords

• Mobility inequity
• Mode choice
• Open data
• Spatial interaction models
• Built environment
• Non-auto transportation