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Meta-learning based passenger flow prediction for newly-operated stations

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Abstract

By tapping into the human mobility of the urban rail transit (URT) network to understand the travel demands and characteristics of passengers in the urban space, URT managers are able to obtain more support for decision-making to improve the effectiveness of operation and management, the travel experience of passengers, as well as public safety. However, not all URT networks have sufficient human mobility data (e.g., newly-operated URT networks). It is necessary to provide data support for mining human mobility in data-poor URT networks. Therefore, we propose a method called Meta Long Short-Term Memory Network (Meta-LSTM) for passenger flow prediction at URT stations to provide data support for networks that lack data. The Meta-LSTM is to construct a framework that increases the generalization ability of a long short-term memory network (LSTM) to various passenger flow characteristics by learning passenger flow characteristics from multiple data-rich stations and then applying the learned parameter to data-scarce stations by parameter initialization. The Meta-LSTM is applied to the URT network of Nanning, Hangzhou, and Beijing, China. The experiments on three real-world URT networks demonstrate the effectiveness of our proposed Meta-LSTM over several competitive baseline models. Results also show that our proposed Meta-LSTM has a good generalization ability to various passenger flow characteristics, which can provide a reference for passenger flow prediction in the stations with limited data.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported by the Fundamental Research Funds for the Central Universities (No. 2023JBMC040) and the National Natural Science Foundation of China (Nos. 72201029, 72161023, 72288101).

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Authors and Affiliations

  1. School of Civil Engineering, Beijing Jiaotong University, Beijing, China

    Kuo Han, Songsong Li & Chunqi Zhu

  2. School of Systems Science, Beijing Jiaotong University, Beijing, China

    Jinlei Zhang

  3. School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou, China

    Xiaopeng Tian

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  1. Kuo Han
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  2. Jinlei Zhang
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  3. Xiaopeng Tian
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  4. Songsong Li
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Contributions

K.H. wrote the main manuscript text. J.Z. provided guidance on ideas for writing the manuscript and the methods involved in the manuscript. X.T. provided guidance on ideas for writing the manuscript. S.L. contributed significantly to the analysis and manuscript preparation. C.Z. helped perform the analysis with constructive discussions. All authors reviewed the manuscript.

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Correspondence to Jinlei Zhang.

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Han, K., Zhang, J., Tian, X. et al. Meta-learning based passenger flow prediction for newly-operated stations. Geoinformatica (2023). https://doi.org/10.1007/s10707-023-00510-8

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