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GSA-KELM-KF: A Hybrid Model for Short-Term Traffic Flow Forecasting

Author

Listed:
  • Wenguang Chai

    (School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China)

  • Liangguang Zhang

    (School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China)

  • Zhizhe Lin

    (School of Information and Communication Engineering, Hainan University, Haikou 570228, China)

  • Jinglin Zhou

    (School of Computer Science, Fudan University, Shanghai 200433, China)

  • Teng Zhou

    (State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550000, China)

Abstract

Short-term traffic flow forecasting, an essential enabler for intelligent transportation systems, is a fundamental and challenging task for dramatically changing traffic flow over time. In this paper, we present a gravitational search optimized kernel extreme learning machine, named GSA-KELM, to avoid manually traversing all possible parameters to improve the potential performance. Furthermore, with the interference of heavy-tailed impulse noise, the performance of KELM may be seriously deteriorated. Based on the Kalman filter that cleverly combines observed data and estimated data to perform the closed-loop management of errors and limit the errors within a certain range, we propose a combined model, termed GSA-KELM-KF. The experimental results of two real-world datasets demonstrate that GSA-KELM-KF outperforms the state-of-the-art parametric and non-parametric models.

Suggested Citation

  • Wenguang Chai & Liangguang Zhang & Zhizhe Lin & Jinglin Zhou & Teng Zhou, 2023. "GSA-KELM-KF: A Hybrid Model for Short-Term Traffic Flow Forecasting," Mathematics, MDPI, vol. 12(1), pages 1-16, December.
    • Handle: RePEc:gam:jmathe:v:12:y:2023:i:1:p:103-:d:1308835
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