Using spatial, temporal, and external factors to enhance prediction of shared-transport users

Shared transportation, which allows commuters to share vehicles, either through riding in the same vehicle (i.e., ride-sharing) or using the same vehicle at different times (i.e., car-sharing or bike-sharing) has become increasingly popular. Car-sharing and bike-sharing require efficient allocation of vehicle resources to sharing stations. Scholars have used temporal or spatial information to predict the number of users at each station. However, external factors, such as special events or rain, can affect this number. This paper proposes a framework to improve the prediction of shared-transport users based on both temporal and spatial factors as well as the external factors of the surrounding environment of the station, the weather, and relevant online activity. The proposed approach was verified through the application to the real-world case of bicycle-sharing in Taipei, Taiwan.