Division and Analysis of Accident-Prone Areas near Highway Ramps Based on Spatial Autocorrelation

This study focuses on identifying accident-prone areas and analyzing the factors contributing to distribution of traffic accidents near highway ramps. A combined method kernel density estimation, spatial autocorrelation analysis, multivariate logistic regression analysis helped identify accident hotspots. Through data collection clustering characteristics in diversion merging were identified. Four levels divided according rates. The influencing analyzed. results showed that area concentrated exit, but had a wider range distribution. this phenomenon was conducted using multinomial logit model results. important different clarified. temperature, lane, weather conditions, time day significant impacts accidents. study’s findings provide an decision-making basis for prevention management.