A Dual-View Approach for Multistation Short-Term Passenger Flow Prediction in Bus Transit Systems

Abstract

Timely and accurate prediction of bus passenger flow plays a crucial role in uncovering real-time traffic demand, presenting an essential and formidable challenge in the realm of bus scheduling and management. The extensive application of deep learning methods in transit passenger flow prediction can be attributed to their exceptional ability to effectively capture spatiotemporal features, resulting in superior performance. However, prevailing deep learning models in transit passenger flow prediction tend to ignore the data enhancement. Additionally, the predominant focus on a single station in the prediction task presents challenges in effectively capturing the spatiotemporal features of the entire network. A model named TSD-ST is proposed to better accomplish the task of predicting short-term transit passenger flow at multistation. The TSD-ST model leverages time series decomposition for data enhancement. Simultaneously, in addition to considering the adjacency graph, the similarity of all the stations of the entire transit network is also considered and uses multigraph convolution and graph fusion modules. This approach enables the TSD-ST model to effectively capture spatiotemporal dependencies. Experiments based on real-world bus transit datasets confirm that the TSD-ST model shows better performance in prediction tasks at 30-min, 60-min, and 90-min time scales, with an average improvement of 21.87%. The effectiveness of each component has been verified through ablation experiments.