A Novel Hierarchical Hybrid Model for Short-Term Bus Passenger Flow Forecasting

Abstract

For the increasing travel demands and public transport problems, dynamically adjusting timetable or bus scheduling is necessary based on accurate real-time passenger flow forecasting. In order to get more accurate passenger flow in future, this paper proposes a novel hierarchical hybrid model based on time series model, deep belief networks (DBNs), and improved incremental extreme learning machine (Im-ELM) to forecast short-term passenger flow. The proposed model is named HTSDBNE with two modelling steps. First, referring the idea of parallelization, the hybrid model, constructed by time series model, DBN, and Im-ELM, is used to forecast short-term passenger flow in different time scales hierarchically and parallel. Second, Im-ELM is utilized to analyse the relationship of forecasting results from the first step, and the weighted outputs of Im-ELM are as the final forecasting results. Comparing with single forecasting models and typical hybrid forecasting models, the testing results indicate that HTSDBNE has better performances. The mean absolute percent error of prediction results is around 10% and fully meets the application requirements of bus operation enterprise.