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Aiming at the difficulty of utilizing spatiotemporal dependence relationship in bus passenger flow prediction effectively, a prediction model of passenger flow based on multiple information attention and dynamic adaptive adversarial graph convolutional network was proposed. Firstly, the time feature encoder was used to capture the similarity between passenger flows at different time periods, and point of interest(POI) information of bus stations was incorporated to enhance node feature extraction. Secondly, the dynamic modeling of spatiotemporal dependence was adopted to complete the modeling of non-Euclidean relationships, and the SimAM attention module was utilized to capture the overall differences in passenger flow data at different stations. The experimental results on real bus passenger flow data showed that compared with the best baseline model, the proposed model reduced the average MAE and RMSE of the next 12 time steps by 0.34 and 0.33, respectively, demonstrating its effectiveness and superiority in predicting bus passenger flow.
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Basic Information:
DOI：10.13705/j.issn.1671-6841.2024120
China Classification Code:U495
Citation Information:
[1]YAN Jianqiang,ZHAO Renqi,GAO Yuan ,et al.Bus Passenger Flow Prediction Based on Multiple Information Attention and Adversarial Graph Convolution[J].Journal of Zhengzhou University(Natural Science Edition),2026,58(02):17-24.DOI:10.13705/j.issn.1671-6841.2024120.
2025-01-13
2025-01-13
2025-01-13