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Cluster-based spatiotemporal dual self-adaptive network for short-term subway passenger flow forecasting

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Abstract

Spatiotemporal modelling of short-term forecasts of metro passenger flows continue to face tremendous challenges. First, there is a need to consider the functional domain made up of several similar stations; Secondly, complex spatiotemporal models depend on a large number of learnable parameters. This paper proposed a spatiotemporal dual self-adaptive network based on the cluster (CG-TaLK) to accurately predict the inflow and outflow of subway passengers. Specifically, through the division of clustering, the members of each group learn a shared embedding, and use the inner product of embedding to mine the flow pattern between urban functional areas, so as to provide more accurate spatial information for prediction. In addition, in order to limit the number of parameters, we migrate a temporal adaptive convolution (TaLK) to capture the time correlation of each station according to the characteristics of passenger flow. The self-adaptive mechanism in space and time can enhance the fitting ability of the model. By comparing six representative algorithms on Hangzhou Metro dataset, the results show that the proposed method is effective and takes up the least parameters. Meanwhile, experiments show that the algorithm can find the main communication between function areas.

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Acknowledgements

This work is supported by the Natural Science Foundation of China (61866007), Natural Science Foundation of Guangxi Province (2018GXNSFDA138006), and the Guangxi Key Laboratory of Trusted Software (KX20202024).

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Authors and Affiliations

  1. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, China

    Qianjin Wei & Yimin Wen

  2. School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China

    Yongheng Qiu

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  1. Qianjin Wei
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  2. Yongheng Qiu
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Correspondence to Qianjin Wei.

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Wei, Q., Qiu, Y. & Wen, Y. Cluster-based spatiotemporal dual self-adaptive network for short-term subway passenger flow forecasting. Appl Intell 52, 14137–14152 (2022). https://doi.org/10.1007/s10489-022-03305-w

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