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.

The integration of generative artificial intelligence (GAI) in traffic forecasting is transforming urban mobility systems, particularly through multimodal data fusion. While prior research has advanced the modeling of spatial and temporal dependencies in traffic systems, the role of weather information has been largely underexplored. To address this gap, we propose a weather-aware dynamic graph neural network (WADGNet) that leverages GAI to enhance traffic flow prediction by integrating multisource weather data, improving both accuracy and robustness. The framework consists of two key modules: 1) The WAEformer utilizes generative representations to capture latent meteorological patterns. 2) The SaGCN, a semi-autoregressive spatiotemporal graph convolution network, learns dynamic spatial and temporal dependencies in traffic flow. By fusing multimodal data from traffic and weather systems, WADGNet enables more accurate and reliable traffic flow forecasting in diverse urban environments. Extensive experiments on several benchmark datasets show that WADGNet consistently outperforms state-of-the-art models, demonstrating strong generalizability. These findings highlight the potential of GAI to power smart city infrastructures through improved traffic management and prediction systems.