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Attention based simplified deep residual network for citywide crowd flows prediction

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Abstract

Crowd flows prediction is an important problem of urban computing whose goal is to predict the number of incoming and outgoing people of regions in the future. In practice, emergency applications often require less training time. However, there is a little work on how to obtain good prediction performance with less training time. In this paper, we propose a simplified deep residual network for our problem. By using the simplified deep residual network, we can obtain not only less training time but also competitive prediction performance compared with the existing similar method. Moreover, we adopt the spatio-temporal attention mechanism to further improve the simplified deep residual network with reasonable additional time cost. Based on the real datasets, we construct a series of experiments compared with the existing methods. The experimental results confirm the efficiency of our proposed methods.

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Acknowledgements

We thank all the anonymous reviewers for their insightful and helpful comments, which improve the paper. This work was supported by the National Nature Science Foundation of China (NSFC Grant Nos. 61572537, U1501252).

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Authors and Affiliations

  1. School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, 510006, China

    Genan Dai, Xiaoyang Hu, Youming Ge, Zhiqing Ning & Yubao Liu

  2. Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, 510006, China

    Yubao Liu

Authors
  1. Genan Dai
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  2. Xiaoyang Hu
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  3. Youming Ge
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  4. Zhiqing Ning
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  5. Yubao Liu
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Corresponding author

Correspondence to Yubao Liu.

Additional information

Genan Dai is currently working towards the PhD degree in the School of Data and Computer Science, Sun Yat-Sen University, China. Her research interests include data mining and artificial intelligence.

Xiaoyang Hu is a graduate student in the School of Data and Computer Science, Sun Yat-Sen University, China. His research interests include data mining and artificial intelligence.

Youming Ge is currently working towards the PhD degree in the School of Data and Computer Science, Sun Yat-Sen University, China. His research interests include data mining and artificial intelligence.

Zhiqing Ning is graduate student of the School of Data and Computer Science, Sun Yat-Sen University, China. His research interests include data mining and artificial intelligence.

Yubao Liu is currently a professor with the Department of Computer Science of Sun Yat-Sen University, China. He received his PhD in computer science from Huazhong University of Science and Technology in 2003, China. He has published more than 50 refereed journal and conference papers including SIGMOD, TODS, VLDB and VLDBJ, etc. His research interests include database systems and data mining. He is a senior member of the China Computer Federation (CCF).

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Dai, G., Hu, X., Ge, Y. et al. Attention based simplified deep residual network for citywide crowd flows prediction. Front. Comput. Sci. 15, 152317 (2021). https://doi.org/10.1007/s11704-020-9194-x

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