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MAPredRNN: multi-attention predictive RNN for traffic flow prediction by dynamic spatio-temporal data fusion

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Abstract

Traffic flow prediction is a key component of intelligent transportation system, especially for increasingly complex urban traffic networks. An accurate flow prediction can help to relieve traffic congestion and reduce traffic accidents. However, the patterns of traffic flow are very complex and volatile, which will be affected by many factors, such as traffic accident, weather, point-of-interests, etc. It is still a challenging issue due to the high nonlinearity and dynamicity of traffic flow. In this paper, we propose a multi-attention predictive recurrent neural networks (MAPredRNN) for traffic flow prediction by dynamic spatio-temporal data fusion. First, convolutional neural network and predictive recurrent neural network are used to obtain the spatio-temporal information of the closeness, periodicity and trend features. And then, multi-attention mechanism is employed to further extract feature fusing information of closeness, periodicity and trend. Experimental results conducted on two real datasets show that our proposed method outperforms the compared algorithms.

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Acknowledgements

This research was funded by the National Natural Science Foundation of China under Grant No.62062033 and No.62067002, and the Natural Science Foundation of Jiangxi Province under Grant No.20212BAB202008.

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

    Xiaohui Huang, Yuan Jiang & Jie Tang

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  1. Xiaohui Huang
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  2. Yuan Jiang
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  3. Jie Tang
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Correspondence to Yuan Jiang.

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Huang, X., Jiang, Y. & Tang, J. MAPredRNN: multi-attention predictive RNN for traffic flow prediction by dynamic spatio-temporal data fusion. Appl Intell 53, 19372–19383 (2023). https://doi.org/10.1007/s10489-023-04494-8

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