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Estimating urban rail transit passenger inflow caused by special events occurrences fusing multi-source data

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Abstract

It is essential to provide accurate real-time forecasting to manage the intense passenger inflow (IPF) of urban rail transit (URT) stations caused by special events such as concerts and football matches. The IPF is predictable due to the fluctuations in passenger outflow before the special event, which also allows the management department to take measures to control the situation. By combining individual travel card data with event data, station data and others, this article proposes a system for estimating URT station IPF before it happens. It consists of two parts: (1) Offline model training is responsible for modeling the relationship between historical special events information, affected station information and traveler characteristics; (2) Online Inflow prediction takes the current event and affected station information as the input of the model trained in the offline part to estimate the IPF. By using the Shanghai, China URT system as an example, the results show that the proposed passenger inflow estimation system can provide a significant reduction in estimation error compared to the traditional prediction model. Additionally, the constructed system has certain robustness, which could provide a basis for the URT management department to make informed decisions.

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Data availability

Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 72071041 and the Jiangsu Province Key R&D Program under Grant BE2021067.

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Authors and Affiliations

  1. School of Transportation, Southeast University, Nanjing, 211189, China

    Wenbo Lu, Yong Zhang & Ting Wang

  2. School of Traffic and Transportation, Beijing Jiaotong University, Beijing, 100091, China

    Peikun Li

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  1. Wenbo Lu
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  4. Ting Wang
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Correspondence to Yong Zhang.

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Lu, W., Zhang, Y., Li, P. et al. Estimating urban rail transit passenger inflow caused by special events occurrences fusing multi-source data. Neural Comput & Applic 35, 16649–16670 (2023). https://doi.org/10.1007/s00521-023-08546-5

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