Modeling the intermodality between public transport and bike-sharing using smartcard trip Chain data

Intermodality of public transit mitigates the negative impacts in an urban area, and many cities are opting for developing a more sustainable and integrated public transport system. This study develops models to examine the impacts of urban factors on the variability of the intermodality rate, defined as the number of transfers between bike-sharing and transit modes, including buses and subways. We employ a series of Bayesian conditional autoregressive (CAR) models to examine the impacts of four categories of factors on the variability of bike-sharing intermodality rates with a particular emphasis on the understanding of how such impacts vary between the intermodality with bus and subway. The findings suggest that intermodality behaviour varies considerably between buses and subways. The results show that efforts to improve the intermodality between buses and bike-sharing could yield results comparable to the intermodality with subways. It was also found that the results of public transport and bike-sharing intermodal models are not directly transferable among different transit modes. Transit travel time is the most important factor associated with the highest increase in the rates of bike-sharing and transit intermodal transfers. Other important factors to consider in the policy and planning for enhancing and increasing the intermodality of bike-sharing and transit are also discussed.