Multi-Scale Spatio-Temporal Analysis of Online Car-Hailing with Different Relationships with Subway

Online car-hailing and subways are two vital modes of urban traffic, with differences in service scope. To optimize their utilization efficiency, it is crucial to understand the complex relationship between them. Online car-hailing offer higher flexibility in planning and management compared to subways. Thus, this study categorizes online car-hailing trips based on subway station data. Utilizing the MGWR model, we investigate the spatial and temporal variations in the influence of factors related to the built environment, transportation facilities, and socio-economic attributes on different types of online car-hailing trips. The results reveal a balanced distribution of passenger flows and dispersed travel hotspots on weekends, while weekdays exhibit distinct commuting patterns. Moreover, the main factors influencing online car-hailing trips are different: catering, subway passenger flow, and medical service density, each exhibiting spatial variations in their facilitative or inhibiting effects. This paper discusses the adequacy of forming multiple competition and cooperation relationship types and the reasons behind spatial differentiation of influencing factors based on these findings. This research contributes to optimizing the utilization of both online cars and subways, leading to more efficient and effective public transportation solutions in urban areas.