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A time-dependent attention convolutional LSTM method for traffic flow prediction

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Abstract

With traffic network becoming increasingly complicated, traffic flow prediction has important practical significance for the management of traffic roads and public safety. For example, an accurate taxi demand prediction can help to improve efficiency of vehicle scheduling and reduce traffic congestion. The main issue of flow prediction is how to extract the information of complex spatio-temporal dependencies and interactions between arrival and departure. To solve these problems, we develop a deep learning method based on time-dependent attention convolutional LSTM (TDAConvLSTM) in which a time-dependent attention mechanism is designed to learn similarities of historical traffic flows among different time intervals and a fusion mechanism is introduced to aggregate the feature information produced by convolutional LSTM and attention module. And then, the result of the feature aggregation is fed to a multi-layer deconvolutional network to gain the results of flow prediction. Experimental studies on two real-life datasets indicate that TDAConvLSTM achieves better results than the compared models. The source code of our proposed method is available at the URL1.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China under Grant Nos.62062033 and 62067002, and the Natural Science Foundation of Jiangxi Province under Grant Nos.20212BAB202008 and 20192ACBL21006.

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Authors and Affiliations

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

    Xiaohui Huang, Jie Tang & Liyan Xiong

  2. Faculty of Science and Technology, University of Macau, E11, Macau, 330013, China

    Xiaofei Yang

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  1. Xiaohui Huang
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  2. Jie Tang
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  3. Xiaofei Yang
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  4. Liyan Xiong
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Correspondence to Xiaohui Huang.

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https://github.com/yym201819/1111/tree/master/demotdaconvlstm/demotdaconvlstm

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Huang, X., Tang, J., Yang, X. et al. A time-dependent attention convolutional LSTM method for traffic flow prediction. Appl Intell 52, 17371–17386 (2022). https://doi.org/10.1007/s10489-022-03324-7

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