Study on a multi-factor lane-changing risk resilience assessment model based on genetic algorithm and fault tree...

Current lane-change risk assessment models often lack dynamic adaptation to adverse weather and validation against real-world outcomes. To bridge this gap, this study re-frames the problem through a resilience engineering lens, defining risk resilience as the lane-changing system's capacity to absorb weather disturbances and maintain safety through adaptation. To operationalize this concept, we introduce two complementary metrics: the Risk Exposure Level (REL) and the Risk Severity Level (RSL). We propose a weather-aware, resilience-oriented assessment framework that integrates a Genetic Algorithm (GA)-calibrated Stopping Sight Distance (SSD) model with Fault Tree Analysis (FTA). Using the CitySim naturalistic driving dataset, a dual-threshold identification algorithm was applied to extract 310 lane-change events (218 sunny, 92 rainy). Key influencing factors, including weather, surrounding vehicle distribution, lane-change direction, and location, were identified through statistical testing. The GA was employed to optimize critical braking parameters (deceleration, reaction time) in the SSD/SDI model, enabling self-adaptive risk thresholds under different weather conditions. REL and RSL quantify the probability (exposure) and severity (consequence) of conflicts from multiple vehicle groups, which are systematically integrated via FTA to assess overall system robustness. Model calibration and testing using trajectory data showed a 42.38% improvement in fitness over the baseline model. A PyQt5-based visualization platform was developed to support practical application. The results confirm that the model effectively captures real-time lane-changing risk, providing a reliable tool for proactive safety management and resilience-oriented decision support in intelligent transportation systems.