Driver's journey from historical traffic violations to future accidents: A China case based on multilayer complex network approach.

Abstract

Traffic violation records serve as key indicators for predicting drivers' future accidents. However, beyond statistical correlations, the underlying mechanisms linking historical traffic violations to future accidents remain inadequately understood. This study introduces a research framework to address this gap: Using Propensity Score Matching and an adapted mutual information-based feature selection algorithm to precisely identify correlations and optimal time windows between drivers' historical traffic violations and future accidents. A multilayer complex network approach was then applied to abstract and model the progression from drivers' historical traffic violations to subsequent accidents, revealing intrinsic patterns through adapted network analysis metrics and ultimately uncovering underlying mechanisms. Actual data from over 17,000 drivers in Shenzhen, China, spanning the period of 2010 to 2020, was utilized. Results revealed significant heterogeneity among driver subtypes with various driving license types regarding optimal time windows and key traffic violations indicative of future accident risks. A universal "Stable Defect Effect" was identified across all driver subtypes, characterized by persistent driving-related deficiencies resistant to temporal decay and penalties. This effect's gradual formation and maturation appear to govern the progression from traffic violations to future accidents. In addition, multilayer complex network models demonstrated significant potential in accident risk studies, particularly in providing valuable latent information by overcoming the limitations of accident data samples.