Modeling E-Bike Route Choice in Washington, DC: A Path Size Logit Approach

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.