An Interpretable Spatial–Nonlinear Learning Framework for Provincial Traffic Accident Analysis

Inspired by the concept of symmetry in functional representation, complex nonlinear relationships can be decomposed into combinations of lower-dimensional functions, providing an interpretable framework for modeling high-dimensional systems. With the continuous growth of road traffic volume in China and the rapid acceleration of urbanization, traffic safety issues have become increasingly prominent. To address the limitations of traditional traffic accident prediction models—including insufficient spatial information representation, weak nonlinear fitting capability, and poor interpretability—this study proposes an improved Kolmogorov–Arnold Networks (KANs) model. Specifically, a spatial embedding module, a multi-scale spline mechanism, and a residual connection structure are incorporated into the original KAN framework to enhance its ability to capture spatial heterogeneity and complex nonlinear relationships in traffic accident data. Experimental results demonstrate that the improved KAN model achieves a 2.38% increase in the coefficient of determination, while reducing the mean absolute deviation and mean squared prediction error by 24.89% and 34.69%, respectively, indicating a significant improvement in both prediction accuracy and model stability. Furthermore, the proposed model enhances interpretability by visualizing variable relationships through spline functions, enabling intuitive analysis of nonlinear effects. Overall, the improved KAN model exhibits strong capability in modeling spatially non-stationary and nonlinear structures, making it a promising tool for macroscopic traffic safety modeling with substantial application potential and practical value.