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International Conference on Wireless Algorithms, Systems, and Applications

WASA 2021: Wireless Algorithms, Systems, and Applications pp 203–214Cite as

Temporal Attention-Based Graph Convolution Network for Taxi Demand Prediction in Functional Areas

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12937))

Abstract

Shared travel is increasingly becoming an indispensable way of urban transportation. Accurately calculating the demand for taxis in various regions of the city has become a huge problem. In this paper, we divide the city into multiple lattices of different sizes and propose a graph convolution network based on the temporal attention mechanism for taxi demand prediction in each functional area of the city. The model includes graph convolution network (GCN), temporal convolution network (TCN), and the attention mechanism, which are respectively used to capture the spatial correlation of roads, time dependence, and highlight the characteristics of the time-series data. Extensive experiments on three datasets validate the effectiveness of the proposed method, compared against several state-of-the-art methods. Despite there are amount differences among the three datasets in our experiment, our model still has a high prediction accuracy. Our model code is available at https://github.com/qdu318/TAGCN.

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Acknowledgments

This research was supported in part by Shandong Province colleges and universities youth innovation technology plan innovation team project under Grant No. 2020KJN011, Shandong Provincial Natural Science Foundation under Grant No. ZR2020MF060, Program for Innovative Postdoctoral Talents in Shandong Province under Grant No. 40618030001, National Natural Science Foundation of China under Grant No. 61802216, and Postdoctoral Science Foundation of China under Grant No.2018M642613.

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Authors and Affiliations

  1. College of Computer Science and Technology, Qingdao University, Qingdao, 266071, China

    Yue Wang, Jianbo Li, Aite Zhao, Zhiqiang Lv & Guangquan Lu

Authors
  1. Yue Wang
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  2. Jianbo Li
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  3. Aite Zhao
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  4. Zhiqiang Lv
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  5. Guangquan Lu
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Corresponding author

Correspondence to Jianbo Li .

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Editors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Zhe Liu

  2. Shanghai Jiao Tong University, Shanghai, China

    Fan Wu

  3. Missouri University of Science and Technology, Rolla, MO, USA

    Sajal K. Das

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Wang, Y., Li, J., Zhao, A., Lv, Z., Lu, G. (2021). Temporal Attention-Based Graph Convolution Network for Taxi Demand Prediction in Functional Areas. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12937. Springer, Cham. https://doi.org/10.1007/978-3-030-85928-2_16

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  • DOI: https://doi.org/10.1007/978-3-030-85928-2_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85927-5

  • Online ISBN: 978-3-030-85928-2

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