How do commuters choose? An anonymous multimodal data approach to subway feeder mode competition

Efficient integration of subway networks with emerging feeder modes is essential for improving urban accessibility and sustainability. This study explores the growing role of bike sharing as a flexible and eco-friendly first- and last-mile solution, competing with traditional bus connections. We propose a framework that combines machine learning with interpretable analysis to model subway feeder mode choice. By examining factors such as travel attributes, land use, demographics, and station centrality, the framework provides insights into user preferences from spatial and temporal perspectives. Our findings indicate that the built environment and demographic factors around commuters’ residences or workplaces have a stronger impact on mode choice than the proximity to subway stations. Housing prices, residential density, and business density notably influence mode selection, with bike sharing particularly favored in urban centers. These insights offer guidance for urban planners seeking to optimize multimodal transport systems and promote sustainable mobility.