Yujie Hu

Reducing commuting from the actual to the minimum level is designed to prioritize environmental sustainability in order to reduce carbon emissions and alleviate traffic congestion, but does it also ensure social equity and sustainability? We study the social inequity of reducing commuting using commuting flow data defined over a combination of multiple housing and socio-demographic attributes in Chicago, Illinois. Social inequity may manifest itself as limited commute reduction benefits or commute increase liability for socially vulnerable groups compared to advantaged groups. We find that (1) 11.8% of Chicago’s workers face commute increase liability after optimization, (2) high poverty Black neighborhoods are the most spatially disadvantaged group after optimization because they have one of the highest commute increase liabilities and one of the lowest commute reduction benefits, (3) the highest reduction benefit and lowest increase liability accrues to higher income and Whites neighborhood groups, and (4) that the amount of commute increase liability is higher in neighborhoods with jobs-housing imbalance. Our analysis provides a method to help decision makers evaluate optimal commuting in terms of social inequity.

We introduce a waypoint-based flow capture location model (WbFCLM) for siting facilities on networks with the objective of maximally capturing flows. The advantages of the waypoint-based formulation are that (1) demand is modeled along observed trajectories rather than assumed travel paths, and (2) demand is allowed to vary across geographic space. We demonstrate the WbFCLM using a test dataset derived from vehicle tracking data. Though solving the WbFCLM can require considerable computing power, it can be used to site facilities on networks where both flows and demand vary spatially.

Commuting backbones represent the most essential connections within a commuting network-those significant flows that link major residential and employment areas and structurally support regional mobility systems. Identifying these backbones is critical for understanding large-scale travel demand, informing infrastructure investment, and supporting transportation planning at regional and national levels. Despite their importance, no publicly available datasets currently exist that capture these commuting backbones across the United States. This paper addresses this gap by generating and sharing two commuting backbone networks, focusing on ground-based commuting modes. Using the disparity filter algorithm, the study extracts significant commuter flows-forming the commuter connection backbone network-within core-based statistical areas (CBSAs). These connections are then mapped to the road network to determine shortest driving paths, and aggregated to highlight heavily used road segments, resulting in the backbone road network. These datasets offer a consistent, national framework for examining key commuting corridors, analyzing structural characteristics of commuting networks, and supporting applications in transportation planning, infrastructure prioritization, sustainability analysis, and regional policy development.