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A Two-Stage Hybrid Extreme Learning Model for Short-Term Traffic Flow Forecasting

Author

Listed:
  • Zhihan Cui

    (Department of Computer Science, Shantou University, Shantou 515063, China)

  • Boyu Huang

    (Department of Computer Science, Shantou University, Shantou 515063, China)

  • Haowen Dou

    (Department of Computer Science, Shantou University, Shantou 515063, China)

  • Yan Cheng

    (Medical College, Shantou University, Shantou 515063, China)

  • Jitian Guan

    (Medical College, Shantou University, Shantou 515063, China)

  • Teng Zhou

    (Department of Computer Science, Shantou University, Shantou 515063, China
    Key Laboratory of Intelligent Manufacturing Technology, Ministry of Education, Shantou University, Shantou 515063, China)

Abstract

Credible and accurate traffic flow forecasting is critical for deploying intelligent traffic management systems. Nevertheless, it remains challenging to develop a robust and efficient forecasting model due to the nonlinear characteristics and inherent stochastic traffic flow. Aiming at the nonlinear relationship in the traffic flow for different scenarios, we proposed a two-stage hybrid extreme learning model for short-term traffic flow forecasting. In the first stage, the particle swarm optimization algorithm is employed for determining the initial population distribution of the gravitational search algorithm to improve the efficiency of the global optimal value search. In the second stage, the results of the previous stage, rather than the network structure parameters randomly generated by the extreme learning machine, are used to train the hybrid forecasting model in a data-driven fashion. We evaluated the trained model on four real-world benchmark datasets from highways A1, A2, A4, and A8 connecting the Amsterdam ring road. The RMSEs of the proposed model are 288.03, 204.09, 220.52, and 163.92, respectively, and the MAPEs of the proposed model are 11.53 % , 10.16 % , 11.67 % , and 12.02 % , respectively. Experimental results demonstrate the superior performance of our proposed model.

Suggested Citation

  • Zhihan Cui & Boyu Huang & Haowen Dou & Yan Cheng & Jitian Guan & Teng Zhou, 2022. "A Two-Stage Hybrid Extreme Learning Model for Short-Term Traffic Flow Forecasting," Mathematics, MDPI, vol. 10(12), pages 1-17, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:12:p:2087-:d:840221
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    References listed on IDEAS

    as
    1. Shumin Yang & Huaying Li & Yu Luo & Junchao Li & Youyi Song & Teng Zhou, 2022. "Spatiotemporal Adaptive Fusion Graph Network for Short-Term Traffic Flow Forecasting," Mathematics, MDPI, vol. 10(9), pages 1-12, May.
    2. Li, Shaoying & Zhuang, Caigang & Tan, Zhangzhi & Gao, Feng & Lai, Zhipeng & Wu, Zhifeng, 2021. "Inferring the trip purposes and uncovering spatio-temporal activity patterns from dockless shared bike dataset in Shenzhen, China," Journal of Transport Geography, Elsevier, vol. 91(C).
    3. Hamid Moeeni & Hossein Bonakdari & Isa Ebtehaj, 2017. "Integrated SARIMA with Neuro-Fuzzy Systems and Neural Networks for Monthly Inflow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(7), pages 2141-2156, May.
    Full references (including those not matched with items on IDEAS)

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