Attention based simplified deep residual network for citywide crowd flows prediction

doi:10.1007/s11704-020-9194-x

Front. Comput. Sci.

2021, Vol. 15

Issue (2) : 152317 https://doi.org/10.1007/s11704-020-9194-x

RESEARCH ARTICLE

Attention based simplified deep residual network for citywide crowd flows prediction

Genan DAI¹, Xiaoyang HU¹, Youming GE¹, Zhiqing NING¹, Yubao LIU^1,²(

)

¹. School of Data and Computer Science, Sun Yat-Sen University, Guangzhou 510006, China
². Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou 510006, China

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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.

Keywords crowd flows prediction spatio-temporal data mining attention

Corresponding Author(s): Yubao LIU

Just Accepted Date: 17 January 2020 Issue Date: 24 December 2020

Cite this article:

Genan DAI,Xiaoyang HU,Youming GE, et al. Attention based simplified deep residual network for citywide crowd flows prediction[J]. Front. Comput. Sci., 2021, 15(2): 152317.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-020-9194-x
https://academic.hep.com.cn/fcs/EN/Y2021/V15/I2/152317

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