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Deep spatio-temporal neural network based on interactive attention for traffic flow prediction

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Abstract

Traffic flow forecasting is of great significance to urban traffic control and public safety applications. The key challenge of traffic flow forecasting is how to capture the complex correlation of different time levels and learn time dependence. Some external information is closely related to traffic flow, such as accidental traffic accidents, weather, and Point of Interests (PoI) information. This paper proposes a deep learning-based model, called AttDeepSTN+, which is used to predict the inflow and outflow of each area of the entire city. Specifically, AttDeepSTN+ uses the structure of interactive attention and convolution to model the temporal closeness, trend, and periodicity of crowd flow, in the interactive attention layer, learn the importance of closeness to periodicity and trend respectively to model the long-term dependence of time, and then use feature fusion to capture complex correlations at different levels, thereby reducing model prediction accuracy. In addition, PoI information are combined with time factors to express the influence of location attributes on crowd flow, to learn prior knowledge of crowd flow. Finally, a new fusion mechanism is used to fuse the attention layer modules and PoI information and other information together into the module to capture the complex correlation between different levels of features, to predict the final crowd flow in each area, and further improve the prediction accuracy of the model. The New York City crowd flow experiment shows that the model is better than the current state-of-the-art baseline.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China(No.62062033) and the Science and Technology Research Project of the Education Department of Jiangxi Province (200604) and the Natural Science Foundation of Jiangxi Province under Grant No.20192ACBL21006 and the Key Research & Development Plan of Jiangxi Province No.20203BBE53034.

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Authors and Affiliations

  1. School of Information Engineering Department, East China Jiaotong University, Nanchang, 330013, China

    Hui Zeng, Zhiying Peng, XiaoHui Huang, Yixue Yang & Rong Hu

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  1. Hui Zeng
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  2. Zhiying Peng
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  3. XiaoHui Huang
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Correspondence to Zhiying Peng.

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Zeng, H., Peng, Z., Huang, X. et al. Deep spatio-temporal neural network based on interactive attention for traffic flow prediction. Appl Intell 52, 10285–10296 (2022). https://doi.org/10.1007/s10489-021-02879-1

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