Abstract: Public transport,especially bus transport,can reduce the private car usage and fuel consumption,and alleviate the condition of traffic congestion and environmental pullution.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 buses may drive away many 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 reduce the waiting time of passengers.However,there are three major challenges for predicting the passenger demand on bus services:inhomogeneous,seaso-nal bursty periods and periodicities.To overcome the challenges,this paper proposed three predictive models and further took a data stream ensemble framework to predict the number of passengers.This paper developed an experiment over a 22-weekperiod.The evaluation results suggest that the proposed method achieves outstanding prediction accuracy among 86411 passenger demands on bus services,more than 78% of them are accurately forecasted.

Key words: Bus transport, Passenger demand prediction, Predictive models, Traffic congestion