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An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning

Author

Listed:
  • Zhanzhong Wang

    (Transportation College, Jilin University, Changchun 130022, China)

  • Ruijuan Chu

    (Transportation College, Jilin University, Changchun 130022, China)

  • Minghang Zhang

    (Transportation College, Jilin University, Changchun 130022, China)

  • Xiaochao Wang

    (Transportation College, Jilin University, Changchun 130022, China)

  • Siliang Luan

    (Transportation College, Jilin University, Changchun 130022, China)

Abstract

For intelligent transportation systems (ITSs), reliable and accurate real-time traffic flow prediction is an important step and a necessary prerequisite for alleviating traffic congestion and improving highway operation efficiency. In this paper, we propose an improved hybrid predicting model including two steps: decomposition and prediction to predict highway traffic flow. First, we adopted the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method to adaptively decompose the original nonlinear, nonstationary, and complex highway traffic flow data. Then, we used the improved weighted permutation entropy (IWPE) to obtain new reconstructed components. In the prediction step, we used the gray wolf optimizer (GWO) algorithm to optimize the least-squares support vector machine (LSSVM) prediction model established for each reconstruction component and integrate the prediction results of each subsequence to obtain the final prediction result. We experimentally validated the effectiveness of the proposed approach. The research results reveal that the proposed model is useful for predicting traffic flow and its changing trends and also allowing transportation officials to make more effective traffic decisions.

Suggested Citation

  • Zhanzhong Wang & Ruijuan Chu & Minghang Zhang & Xiaochao Wang & Siliang Luan, 2020. "An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning," Sustainability, MDPI, vol. 12(20), pages 1-22, October.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:20:p:8298-:d:425308
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    References listed on IDEAS

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