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

Zhizhe Lin, Dawei Wang, Chuxin Cao, Hai Xie, Teng Zhou () and Chunjie Cao
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Zhizhe Lin: School of Cyberspace Security, Hainan University, Haikou 570228, China
Dawei Wang: School of Cyberspace Security, Hainan University, Haikou 570228, China
Chuxin Cao: School of Information and Communication Engineering, Hainan University, Haikou 570228, China
Hai Xie: School of Cyberspace Security, Hainan University, Haikou 570228, China
Teng Zhou: School of Cyberspace Security, Hainan University, Haikou 570228, China
Chunjie Cao: School of Cyberspace Security, Hainan University, Haikou 570228, China

Mathematics, 2025, vol. 13, issue 7, 1-20

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.

Keywords: traffic flow theory; intelligent transportation; Kolmogorov–Arnold networks; gravitational search algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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