Volume 26 Issue 2
Feb.  2026
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DAI Zhi-cheng, LI De-wei, LI Hua, XU En-hua, ZHANG Ruo-nan. Short-term passenger flow prediction framework for comprehensive transportation hub based on behavioral representation spatial correlation network[J]. Journal of Traffic and Transportation Engineering, 2026, 26(2): 94-109. doi: 10.19818/j.cnki.1671-1637.2026.026
Citation: DAI Zhi-cheng, LI De-wei, LI Hua, XU En-hua, ZHANG Ruo-nan. Short-term passenger flow prediction framework for comprehensive transportation hub based on behavioral representation spatial correlation network[J]. Journal of Traffic and Transportation Engineering, 2026, 26(2): 94-109. doi: 10.19818/j.cnki.1671-1637.2026.026
shu

Short-term passenger flow prediction framework for comprehensive transportation hub based on behavioral representation spatial correlation network

doi: 10.19818/j.cnki.1671-1637.2026.026
  • 1.

    School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China

  • 2.

    Frontiers Science Center for Smart High-Speed Railway System, Beijing Jiaotong University, Beijing 100044, China

  • 3.

    China Railway Shanghai Group Co., Ltd., Shanghai 200071, China

Funds:

Fundamental Research Funds for the Central Universities 2024YJS081

Fundamental Research Funds for the Central Universities 2022JBQY006

National Natural Science Foundation of China 72471023

More Information
  • Corresponding author: LI De-wei, professor, PhD, E-mail: lidw@bjtu.edu.cn
  • Received Date: 2025-07-30
  • Accepted Date: 2025-06-06
  • Rev Recd Date: 2025-05-26
  • Publish Date: 2026-02-28
    Abstract
  • To improve the accuracy of short-term passenger flow prediction for heterogeneous functional areas within the transportation hub, the prediction accuracy issue caused by the limitation of existing prediction methods in accurately capturing complex spatiotemporal interactions among non-adjacent areas was investigated. The comprehensive application of virtual reality (VR) technology was studied and the multi-scale temporal feature encoding module was improved to establish a behavioral representation spatial correlation network (BRSCN) framework for short-term passenger flow prediction. A comprehensive transportation hub scenario was constructed using VR technology. The passenger behavior experiments were conducted to collect trajectory data of passengers in the virtual environment. The trajectory data were processed through regional identification, dwell-time detection, and activity-chain reconstruction to generate a complete set of passenger spatial activity chains. Subsequently, sliding-window sampling and graph embedding techniques were applied to derive feature vectors for each functional area. A spatial correlation graph reflecting non-adjacent inter-area relationships was constructed by jointly considering cosine similarity and passenger flow transition frequencies. During the prediction stage, a graph attention network (GAT) was employed to aggregate spatial features from both adjacent and non-adjacent areas. A multi-scale temporal feature encoding module was then designed by combining a bidirectional extended long short-term memory network (Bi-sLSTM) with a dynamic-window sparse-attention Transformer, enabling adaptive capturing of complex nonlinear and multi-scale spatiotemporal passenger flow fluctuation features within the hub. According to experimental results, compared with state-of-the-art models such as AGCRN and STTN, the real passenger flow data selected from the elevated level of the Shanghai Hongqiao comprehensive transportation hub reduces root mean square error (RMSE), mean absolute error, and mean absolute percentage error by 30.2%, 21.1%, and 28.3%, respectively. The incorporation of the spatial correlation graph further decreases RMSE by 16.7%, significantly enhancing the model's ability to predict passenger flow interactions between non-adjacent areas. Moreover, without lowering prediction accuracy, the dynamic-window sparse attention mechanism reduces model complexity by 2.9%. The proposed BRSCN framework effectively captures complex spatiotemporal relationships among non-adjacent functional areas within comprehensive transportation hubs, substantially improving short-term passenger flow prediction accuracy and model generalization performance. A scientific decision-making basis can be provided for spatial resource allocation optimization and dynamic passenger flow management in comprehensive transportation hubs.

     

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