Citi Bike Station Behavioral Regime Model and Its Application in Rebalancing Operations

Past Citi Bike rebalancing research has relied on optimization and geospatial models but has treated spatial and temporal structures separately, leaving a gap in understanding stations as long-term behavioral entities. This study exploits the frequent spatiotemporal structure in Citi Bike daily trip data and treats the station’s bike net flow rate (NFR) time-series as the study object. Stations are grouped into regimes using time-series clustering, cluster stability, and the spatial context surrounding each station. Stations were assigned operational roles based on their hourly NFRs and potential contribution to the rebalancing truck. A priority-queue-based heuristic routing (PQHR) algorithm is introduced to design a single-vehicle route that accounts for stations’ regimes, roles, rebalancing urgency, and priority during rush hours. Therefore, this study formally introduces the Station Behavior Regime Model (SBRM) that defines station regimes, rebalancing roles, and routing. The result achieved a >90% reduction in the number of stations with extreme bike accumulation or unavailability and reduced the NFR of affected stations by >30% in busy areas. The spatial context derived from station behavior modes suggests new ways to define neighborhood boundaries. The methodologies provide new avenues for rebalancing operations and routing plans across a broad range of station-centric transportation network studies.