Abstract
Congestion is a great concern for urban rail transit, because it has a great impact on commuting efficiency and passenger safety. In order to mitigate congestion, this paper proposes a passenger flow control framework. Its function is to predict passenger number with given data and algorithms, identify possible overcrowding in advance, implement control strategies, and therefore reduce congestion. A subway station model based on the AnyLogic software is built to verify the control framework and to simulate the movement of passengers and trains. Simulation results demonstrate that the control framework has a good effect on congestion alleviation at a station level.
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Ni, Q., Guo, Y. (2023). Passenger Flow Control in Subway Station with Card-Swiping Data. In: Tang, L.C., Wang, H. (eds) Big Data Management and Analysis for Cyber Physical Systems. BDET 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 150. Springer, Cham. https://doi.org/10.1007/978-3-031-17548-0_1
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DOI: https://doi.org/10.1007/978-3-031-17548-0_1
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