Investigating the Role of POIs in the Relationship Between Bike‐Sharing and Public Transportation: Evidence From Beijing, China, Using Neural Networks and SHAP

ABSTRACT

This study investigates how points of interest (POI) shape the interaction between dockless bike‐sharing (DBS) and public transportation. Using Beijing, China, as a case study, we propose a data‐driven analytical framework that delineates public transportation service buffers by incorporating station‐level passenger inflow and outflow characteristics. Based on these adaptive buffers, DBS trips are classified into competition, cooperation‐first‐mile, cooperation‐last‐mile and complement types. Grid‐level DBS trip volumes are then predicted using fully connected neural networks, and Shapley Additive Explanations are employed to interpret the influence of different POI categories. The results show that model performance varies across DBS trip types and improves with deeper neural network architectures. Competition between DBS and public transportation during peak hours is primarily associated with short‐distance, lifestyle‐oriented travel around residential and dining areas, whereas cooperation is strongly linked to access and egress functions related to daily activity chains. Parking‐intensive residential areas emerge as high‐potential zones for promoting DBS‐public transportation integration, indicating that DBS can effectively mitigate first‐ and last‐mile barriers in car‐oriented neighbourhoods. These findings provide insights for transit‐oriented planning and DBS deployment, suggesting that reallocating limited urban space toward integrated DBS‐public transportation facilities can support more sustainable urban mobility.