GSA-KAN: A Hybrid Model for Short-Term Traffic Forecasting
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 overh...
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2025-03-01
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| author | Zhizhe Lin Dawei Wang Chuxin Cao Hai Xie Teng Zhou Chunjie Cao |
| author_facet | Zhizhe Lin Dawei Wang Chuxin Cao Hai Xie Teng Zhou Chunjie Cao |
| author_sort | Zhizhe Lin |
| collection | DOAJ |
| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>3.95</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>6.96</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2.71</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2.29</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and the MAPE of GSA-KAN is reduced by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>6.66</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>5.88</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>6.41</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4.87</mn><mo>%</mo></mrow></semantics></math></inline-formula> on the A1, A2, A4, and A8 datasets, respectively. The experimental results demonstrate that GSA-KAN performs advanced parametric and nonparametric models. |
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| publishDate | 2025-03-01 |
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| spelling | doaj-art-d4fa0e8db01b4ca5a6325b2fcfec2a4e2025-08-20T02:09:17ZengMDPI AGMathematics2227-73902025-03-01137115810.3390/math13071158GSA-KAN: A Hybrid Model for Short-Term Traffic ForecastingZhizhe Lin0Dawei Wang1Chuxin Cao2Hai Xie3Teng Zhou4Chunjie Cao5School of Cyberspace Security, Hainan University, Haikou 570228, ChinaSchool of Cyberspace Security, Hainan University, Haikou 570228, ChinaSchool of Information and Communication Engineering, Hainan University, Haikou 570228, ChinaSchool of Cyberspace Security, Hainan University, Haikou 570228, ChinaSchool of Cyberspace Security, Hainan University, Haikou 570228, ChinaSchool of Cyberspace Security, Hainan University, Haikou 570228, ChinaShort-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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>3.95</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>6.96</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2.71</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2.29</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and the MAPE of GSA-KAN is reduced by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>6.66</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>5.88</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>6.41</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4.87</mn><mo>%</mo></mrow></semantics></math></inline-formula> on the A1, A2, A4, and A8 datasets, respectively. The experimental results demonstrate that GSA-KAN performs advanced parametric and nonparametric models.https://www.mdpi.com/2227-7390/13/7/1158traffic flow theoryintelligent transportationKolmogorov–Arnold networksgravitational search algorithm |
| spellingShingle | Zhizhe Lin Dawei Wang Chuxin Cao Hai Xie Teng Zhou Chunjie Cao GSA-KAN: A Hybrid Model for Short-Term Traffic Forecasting Mathematics traffic flow theory intelligent transportation Kolmogorov–Arnold networks gravitational search algorithm |
| title | GSA-KAN: A Hybrid Model for Short-Term Traffic Forecasting |
| title_full | GSA-KAN: A Hybrid Model for Short-Term Traffic Forecasting |
| title_fullStr | GSA-KAN: A Hybrid Model for Short-Term Traffic Forecasting |
| title_full_unstemmed | GSA-KAN: A Hybrid Model for Short-Term Traffic Forecasting |
| title_short | GSA-KAN: A Hybrid Model for Short-Term Traffic Forecasting |
| title_sort | gsa kan a hybrid model for short term traffic forecasting |
| topic | traffic flow theory intelligent transportation Kolmogorov–Arnold networks gravitational search algorithm |
| url | https://www.mdpi.com/2227-7390/13/7/1158 |
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