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International Conference on Collaborative Computing: Networking, Applications and Worksharing

CollaborateCom 2021: Collaborative Computing: Networking, Applications and Worksharing pp 385–400Cite as

Geographic and Temporal Deep Learning Method for Traffic Flow Prediction in Highway Network

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Abstract

Traffic congestion has become an inevitable situation faced by all countries and the prediction accuracy of traffic flow, as one of the means to solve this problem, still needs to be improved. Most studies lack the consideration of the influence of multiple factors such as spatial factors, time series factors and other external factors, which makes the prediction effect of traffic flow unsatisfactory. In this paper a method is proposed based on deep learning that can capture the geographic spatial relationship among toll stations, the dynamic temporal relationship of historical traffic flow, extreme weather and calendar types. On the three metrics of MAPE, MAE, and RMSE, the prediction effect of our model has increased by 30% compared with KNN, GBRT and LSTM models.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (No. 61702014), Beijing Municipal Natural Science Foundation (No. 4192020) and 2020 “Shipei Plan” Project for the Cultivation of High-level Talents in Beijing Colleges and Universities (No. 21XN217).

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Authors and Affiliations

  1. School of Information Science and Technology, North China University of Technology, Beijing, 100144, China

    Tianpu Zhang, Weilong Ding, Mengda Xing, Jun Chen & Yongkang Du

  2. Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data, Beijing, 100144, China

    Tianpu Zhang, Weilong Ding, Mengda Xing & Jun Chen

  3. Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Science, Beijing, China

    Ying Liang

Authors
  1. Tianpu Zhang
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  2. Weilong Ding
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  3. Mengda Xing
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  4. Jun Chen
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  5. Yongkang Du
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  6. Ying Liang
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Corresponding author

Correspondence to Weilong Ding .

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Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Xinheng Wang

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Zhang, T., Ding, W., Xing, M., Chen, J., Du, Y., Liang, Y. (2021). Geographic and Temporal Deep Learning Method for Traffic Flow Prediction in Highway Network. In: Gao, H., Wang, X. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 407. Springer, Cham. https://doi.org/10.1007/978-3-030-92638-0_23

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  • DOI: https://doi.org/10.1007/978-3-030-92638-0_23

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  • Print ISBN: 978-3-030-92637-3

  • Online ISBN: 978-3-030-92638-0

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