This study proposes a spatial modeling framework for analyzing traffic accident risks using six years of traffic data (2018-2023) from Cheongju City, South Korea.To address the limitations of traditional statistical approaches that fail to capture spatial continuity and neighborhood interactions, we employ Graph Trend Filtering (GTF), a regularization-based technique that smooths data across a graph structure reflecting subdistrict-level adjacency.We further enhance the model by introducing edge-adaptive penalty weights based on observed accident differentials, enabling locally adaptive estimation.The proposed method effectively identifies spatially heterogeneous risk patterns and discontinuities, revealing high-risk zones in both urban cores and peripheral industrial areas.Visualization and clustering of smoothed accident risks offer actionable insights into regional disparities in traffic safety.This work contributes a robust, data-driven methodology for localized policy design and provides empirical evidence to support targeted accident prevention strategies at the subdistrict level.
Lee et al. (Tue,) studied this question.