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MFAGCN: Multi-Feature Based Attention Graph Convolutional Network for Traffic Prediction

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Wireless Algorithms, Systems, and Applications (WASA 2021)

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

Abstract

Large-scale traffic data mining provides a new solution to alleviate traffic congestion and improves traffic service. As an important part of traffic data analysis, traffic multi-feature prediction is widely concerned, and several machine learning algorithms are also applied in this field. However, this is very challenging, because each traffic feature has a highly nonlinear and complex pattern, and there are great differences between multiple features. A large number of the existing traffic feature prediction methods focus on extracting a single traffic feature and lack the ability of analyzing multiple features. This paper proposes a new multi-feature based attention graph convolutional network (MFAGCN) to solve the problem of the prediction of multiple features in traffic, the proposed method has 4.53% improved to the conventional methods. The three features predicted by MFAGCN are traffic flow, occupancy rate, and vehicle speed. Each feature is modeled with three temporal attributes, namely weekly period, daily period, and nearest period. In this paper, multi-feature prediction mainly includes two parts: (1) Establish a spatiotemporal attention mechanism for capturing the dynamic spatiotemporal correlation of multiple features. (2) Different convolutional kernels and activation functions are used for each feature after splitting. The experimental results on real traffic datasets have verified the effectiveness of the MFAGCN .

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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

    Haoran Li, Jianbo Li, Zhiqiang Lv & Zhihao Xu

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  1. Haoran Li
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  2. Jianbo Li
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  3. Zhiqiang Lv
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  4. Zhihao Xu
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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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Li, H., Li, J., Lv, Z., Xu, Z. (2021). MFAGCN: Multi-Feature Based Attention Graph Convolutional Network for Traffic Prediction. 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_18

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  • DOI: https://doi.org/10.1007/978-3-030-85928-2_18

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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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