Grid-Based Spatial Analysis of Road Accident Patterns in Thailand

Road accidents in Thailand remain a significant public health concern. This study analyzes accident patterns by combining grid-based spatial analysis (0.02° × 0.02° resolution) with vehicle type distribution data from 2019–2023. Using k-means clustering, we identified eight distinct clusters, with the most critical cluster (Cluster 7) showing accident rates 5.4 times higher than the national average, particularly around Suvarnabhumi Airport. Urban centers demonstrated accident frequencies 3.2 times higher than rural areas, with private vehicles and motorcycles being the predominant vehicle types involved. Our analysis identified strong correlations between accident-prone areas and specific vehicle types, highlighting critical locations for targeted interventions such as improved road design and stricter speed regulations. These findings offer a data-driven foundation for policymakers to enhance traffic monitoring, redesign hazardous intersections, and improve infrastructure in high-risk zones.