The Passenger Demand Prediction Model on Bus Networks

Public transport, especially the bus transport, can reduce the private car usage and fuel consumption, and alleviate traffic congestion. However, when traveling with buses, the travelers not only care about the waiting time, but also care about the crowdedness in the bus. Excessively overcrowded bus may drive away the anxious travelers and make them reluctant to take buses. So accurate, real-time and reliable passenger demand prediction becomes necessary, which can help determine the bus headway and help reduce the waiting time of passengers. There are three major challenges for predicting the passenger demand on bus services: inhomogeneous, seasonal bursty periods and periodicities. To overcome the challenges, we propose three predictive models and further take a data stream ensemble framework to predict the number of passengers. Our performance study based on a real dataset of five months' bus data demonstrates that our approach is quite effective: among 86,411 passenger demands on bus services, more than 78% of them are accurately forecasted.