Abstract
Early detection of Sudden Abnormal Large Outflow (SALO) aims to determine abnormal large outflows and locate the station where real-time outflow significantly exceeds expectations. SALO serves as a crucial indicator for city administration to identify emerging crowd gathering events as early as possible. Existing solutions can’t work well for SALO prediction due to the lack of modeling the dynamic gathering trend of passenger flows in SALO instances, characterized by strong randomness and low probability. In this paper, we propose a novel Gathering Score based Prediction Method, called GSPM, for SALO prediction. GSPM introduces a gathering score to quantify the dynamic gathering trend of abnormal online flows, limits the SALO location to a few candidate stations, and locates it using a utility-theory-based model. This method is built on key data-driven insights, such as obvious increases in online flows before SALO occurrences, and passengers are more inclined to gather near stations. We evaluate GSPM with extensive experiments based on smart card data collected by Automatic Fare Collection system over two years. The results demonstrate that GSPM surpasses the results of state-of-the-art baselines.
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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., Ye, K. (2024). GSPM: An Early Detection Approach to Sudden Abnormal Large Outflow in a Metro System. 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_26
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DOI: https://doi.org/10.1007/978-981-97-2262-4_26
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