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GSA-KAN: A Hybrid Model for Short-Term Traffic Forecasting

Author

Listed:
  • Zhizhe Lin

    (School of Cyberspace Security, Hainan University, Haikou 570228, China
    School of Information and Communication Engineering, Hainan University, Haikou 570228, China
    These authors contributed equally to this work.)

  • Dawei Wang

    (School of Cyberspace Security, Hainan University, Haikou 570228, China
    These authors contributed equally to this work.)

  • Chuxin Cao

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

  • Hai Xie

    (School of Cyberspace Security, Hainan University, Haikou 570228, China
    School of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China)

  • Teng Zhou

    (School of Cyberspace Security, Hainan University, Haikou 570228, China
    Yangtze Delta Region Institute, University of Electronic Science and Technology of China, Quzhou 324003, China)

  • Chunjie Cao

    (School of Cyberspace Security, Hainan University, Haikou 570228, China)

Abstract

Short-term traffic flow forecasting is an essential part of intelligent transportation systems. However, it is challenging to model traffic flow accurately due to its rapid changes over time. The Kolmogorov–Arnold Network (KAN) has shown parameter efficiency with lower memory and computational overhead via spline-parametrized functions to handle high-dimensional temporal data. In this paper, we propose to unlock the potential of the Kolmogorov–Arnold network for traffic flow forecasting by optimizing its parameters with a heuristic algorithm. The gravitational search algorithm learns to understand optimized KANs for different traffic scenarios. We conduct extensive experiments on four real-world benchmark datasets from Amsterdam, the Netherlands. The RMSE of GSA-KAN is reduced by 3.95 % , 6.96 % , 2.71 % , and 2.29 % , and the MAPE of GSA-KAN is reduced by 6.66 % , 5.88 % , 6.41 % , and 4.87 % on the A1, A2, A4, and A8 datasets, respectively. The experimental results demonstrate that GSA-KAN performs advanced parametric and nonparametric models.

Suggested Citation

  • Zhizhe Lin & Dawei Wang & Chuxin Cao & Hai Xie & Teng Zhou & Chunjie Cao, 2025. "GSA-KAN: A Hybrid Model for Short-Term Traffic Forecasting," Mathematics, MDPI, vol. 13(7), pages 1-20, March.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:7:p:1158-:d:1625221
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    References listed on IDEAS

    as
    1. Ming Jiang & Zhiwei Liu, 2023. "Traffic Flow Prediction Based on Dynamic Graph Spatial-Temporal Neural Network," Mathematics, MDPI, vol. 11(11), pages 1-16, May.
    2. Karamichailidou, Despina & Kaloutsa, Vasiliki & Alexandridis, Alex, 2021. "Wind turbine power curve modeling using radial basis function neural networks and tabu search," Renewable Energy, Elsevier, vol. 163(C), pages 2137-2152.
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