Abstract
In this paper, we investigate the real-time fine-grained passenger flows in a complex metro system. Our primary focus is on addressing crucial questions, such as determining the number of passengers on a moving train and in specific station areas (e.g., access channel, transfer channel, platform). These insights are essential for effective traffic management and ensuring public safety. Existing visual analysis methods face limitations in achieving comprehensive network coverage due to deployment costs. To overcome this challenge, we introduce FMSYS, a cloud-based analysis system leveraging smart card data for efficient and reliable real-time passenger flow predictions. FMSYS identifies each passenger’s travel patterns and classifies passengers into two groups: regular (D-group) and stochastic (ND-group). It models stochastic movement of passengers using a state transition process at the group level and employs a combined approach of KNN and Gaussian Process Regression for dynamic state transition prediction. Empirical analysis, based on six months of smart card transactions in Shenzhen, China, validates the effectiveness of FMSYS.
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Acknowledgement
This study was funded by the National Key R &D Program of China (No. 2023YFC3321600), National Natural Science Foundation of China (No. 62372443, No. 62376263), Shenzhen Industrial Application Projects (No. CJGJZD20210408091600002).
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Sun, L., Zhao, J., Zhang, F., Zhang, R., Ye, K. (2024). FMSYS: Fine-Grained Passenger Flow Monitoring in a Large-Scale Metro System Based on AFC Smart Card Data. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14649. Springer, Singapore. https://doi.org/10.1007/978-981-97-2262-4_27
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DOI: https://doi.org/10.1007/978-981-97-2262-4_27
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