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STGs: construct spatial and temporal graphs for citywide crowd flow prediction

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Abstract

Crowd flow prediction is one of the most remarkable issues in a wide range of areas, from traffic control to public safety, and aims to forecast the inflow and outflow of crowds in each region of a city. Most existing studies adopt CNN and its variants to discover the spatial patterns of grid maps (each grid represents a region) while ignoring the correlation between distant regions that may share similar temporal patterns. In this paper, we propose a gnn-based prediction method, called STGs, for crowd flow prediction, which jointly constructs spatial and temporal graphs from grid maps and then implements graph neural networks to directly capture the relationship between regions. Additionally, we introduce a gated fusion mechanism to combine spatial and temporal embedding from the corresponding graph, which further improves the performance of our STGs. We conduct numerical experiments to compare STGs with other baseline models using two real-world datasets, BikeNYC and TLC. Experimental results demonstrate the superiority of our STGs model; specifically, our model reduces the mean absolute error (MAE) of crowd flow prediction by approximately 7-8% compared to state-of-the-art baselines.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 61876017 and 61906014.

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Authors and Affiliations

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

    Jintao Xing, Xiangyuan Kong, Weiwei Xing, Xiang Wei, Jian Zhang & Wei Lu

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  1. Jintao Xing
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  2. Xiangyuan Kong
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  3. Weiwei Xing
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  4. Xiang Wei
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  5. Jian Zhang
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  6. Wei Lu
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Correspondence to Wei Lu.

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Xing, J., Kong, X., Xing, W. et al. STGs: construct spatial and temporal graphs for citywide crowd flow prediction. Appl Intell 52, 12272–12281 (2022). https://doi.org/10.1007/s10489-021-02939-6

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