Shared Bicycle Riding Path Recognition and Dynamic Speed Setting for Electric Vehicles Based on 3D Stacking Models

Abstract

Amid the rapid development of the “Internet Plus” sharing economy, shared mobility grapples with challenges like imprecise ride path detection and unreasonable speed settings. Using over 280,000 September 2025 Citi Bike ride records in New York City, this study establishes a three-dimensional stacked recognition model (integrating hotspot identification, vectorization, terrain modeling and network analysis) via MiniBatchKMeans clustering and other terrain feature techniques to identify major cycling paths and determine scientific e-bike speeds. The model locates key terrain areas, constructs a main cycling network covering over 60% of core traffic, and reveals NYC cycling’s multi-centered, corridor-based pattern linked to transport and commercial zones; it further proposes a zoned dynamic speed limit scheme (15 km/h in rush hours/ central areas, 20 km/h on main routes, 25 km/h in valleys/other regions), providing data and methodological support for optimizing urban slow traffic planning, regulating shared mobility and promoting coordinated integration with urban transport systems.