Trip Prediction in Bike Sharing Systems

Trip demand prediction plays a crucial role in bike-sharing systems. Predicting trip demand is a highly challenging problem because it is influenced by multiple factors, such as periodic changes, correlation between stations, weather and types of users. Although several recent studies successfully address some of these factors, no framework exists that can consider all of them simultaneously. To this end, we develop a novel form of the point process that jointly incorporates all the above factors to predict trip demand, i.e., predicting the number of pick-up and drop-off events in the future and when over-demand is likely to occur. Our extensive experiments on real-world bike sharing systems demonstrate the superiority of our trip demand prediction method over five existing methods.