Journal of Computer Science and Technology ›› 2020, Vol. 35 ›› Issue (2): 338-352.doi: 10.1007/s11390-020-9970-y

• Special Section on Learning and Mining in Dynamic Environments • Previous Articles     Next Articles

Exploiting Multiple Correlations Among Urban Regions for Crowd Flow Prediction

Qiang Zhou, Jing-Jing Gu*, Member, CCF, Chao Ling, Wen-Bo Li, Yi Zhuang, Jian Wang, Senior Member, CCF, Member, ACM, IEEE        

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • Received:2019-08-20 Revised:2020-01-17 Online:2020-03-05 Published:2020-01-20
  • Contact: Jing-Jing Gu E-mail:gujingjing@nuaa.edu.cn
  • About author:Qiang Zhou received his B.E. degree in software engineering from Nanjing University of Aeronautics and Astronautics, Nanjing, in 2016. He is currently working toward his Ph.D. degree in the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing. His current research interests include urban computing, data mining, deep learning and spatio-temporal prediction.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China under Grant No. 61572253, and the Aviation Science Fund of China under Grant No. 2016ZC52030.

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Crowd flow prediction has become a strategically important task in urban computing, which is the prerequisite for traffic management, urban planning and public safety. However, due to variousness of crowd flows, multiple hidden correlations among urban regions affect the flows. Besides, crowd flows are also influenced by the distribution of Points-of-Interests (POIs), transitional functional zones, environmental climate, and different time slots of the dynamic urban environment. Thus, we exploit multiple correlations between urban regions by considering the mentioned factors comprehensively rather than the geographical distance and propose multi-graph convolution gated recurrent units (MGCGRU) for capturing these multiple spatial correlations. For adapting to the dynamic mobile data, we leverage multiple spatial correlations and the temporal dependency to build an urban flow prediction framework that uses only a little recent data as the input but can mine rich internal modes. Hence, the framework can mitigate the influence of the instability of data distributions in highly dynamic environments for prediction. The experimental results on two real-world datasets in Shanghai show that our model is superior to state-of-the-art methods for crowd flow prediction.

Key words: crowd flow prediction; multi-graph convolutional network; multiple correlations mining;

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