Enhancing Driving Safety Evaluation Through Correlation Analysis of Driver Behavior

This paper presents a method for evaluating driving behavior safety based on real-world urban driving data collected from on-road experiments. The aim of this study is to develop a comprehensive and interpretable evaluation framework to improve the identification and correction of unsafe driving behaviors, particularly in urban electric vehicle applications. Five driving behavior indicators were selected: average speed, speed fluctuation difference, acceleration range, speeding frequency, and speed change frequency. The Frequent Pattern Growth (FP-growth) algorithm was applied to model and analyze the hidden relationships between these indicators. Principal component analysis (PCA) was used to determine the weight of each indicator, resulting in a comprehensive safety evaluation method based on the correlation of driving behaviors. The findings reveal that unsafe driving behaviors often occur in combination, with speeding, rapid acceleration, and speed change frequency frequently coexisting on the same road segment, collectively influencing driving safety. The proposed evaluation method was validated through comparative analysis of driving safety scores across different drivers, providing a useful reference for improving and correcting unsafe driving behaviors.