Problem
Research questions and friction points this paper is trying to address.
e-bike route choice
cycling infrastructure
GPS trajectory data
street-level visual features
route choice modeling
Modeling E-Bike Route Choice in Washington, DC: A Path Size Logit Approach
🤖 AI Summary
This study addresses the empirical gap in understanding electric bicycle (e-bike) route choice behavior, which hinders the development of cycling infrastructure suited to the e-bike era. Leveraging GPS trajectory data from Washington, D.C.’s Capital Bikeshare system, the authors construct a mixed choice set combining observed and shortest paths and estimate a path-size logit model of user decisions. Innovatively integrating GIS-derived road attributes with visual streetscape features extracted from Street View imagery, the analysis reveals that road hierarchy moderates the effect of bike facilities and that longer trips exhibit stronger preferences for high-quality infrastructure. Results indicate that e-bike users significantly favor routes minimizing conflicts with motor vehicles and pedestrians and ensuring travel continuity; among greenery elements, only tree cover exerts a significant positive influence on route choice. Incorporating streetscape features substantially improves model performance, and standardized effect sizes help identify key behavioral drivers.
📝 Abstract
Understanding e-bike route choice is essential for developing effective cycling infrastructure, yet empirical evidence remains limited. This study investigates shared e-bike route choice in Washington, DC, using Global Positioning System (GPS) trajectory data from the Capital Bikeshare system. A Path Size Logit model is estimated using a hybrid choice set consisting of observed routes and corresponding shortest paths, integrating Geographic Information System (GIS)-based infrastructure variables with computer vision-derived street-level visual features extracted from Street View images (SVI). The results indicate that e-bike riders tend to choose routes that minimize conflicts with both motor vehicles and pedestrians while maintaining travel continuity. Roadway hierarchy substantially moderates the influence of bicycle facilities, with the presence of bicycle facilities having a much greater impact on route choice along major roads than along minor roads. Longer trips also exhibit stronger preferences for cycling infrastructure. Incorporating street-level visual features improves model performance, although their effects are generally smaller than those of road infrastructure, with trees being the only greenery component showing a consistently positive effect. Standardized effect sizes further identify the most behaviorally important route attributes. These findings provide practical evidence for cycling infrastructure planning in the e-bike era.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Path Size Logit
Street View imagery
computer vision
e-bike route choice
hybrid choice set
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