Identifying dangerous lane-change behavior through coupled lateral–longitudinal interaction risk assessment

Dangerous lane changes in urban expressway merging areas involve multi-vehicle interactions and coupled lateral–longitudinal risk, which instantaneous longitudinal surrogate safety measures alone cannot capture. This study proposes a framework jointly capturing longitudinal car-following risk and lateral intrusion risk. Vehicle trajectories were extracted from UAV videos at an urban expressway merging area, yielding 206 lane-change samples after screening and reconstruction. Longitudinal safety distance indices were built using a stopping sight distance model, and lateral indices via a responsibility-sensitive safety model. Risk exposure and severity were calculated per dimension and integrated through a fault-tree structure into a composite risk index. The classification threshold was determined using a probability-weighted moments method, with robustness evaluated through bootstrap resampling. The framework identified 53 dangerous lane changes (25.7% of samples). Cross-scenario validation using near-crash events from the 100-Car Naturalistic Driving Study achieved 100% recall, versus 69.2% for a conventional TTC-based method. Risk decomposition showed that TTC-missed events were predominantly lateral-driven, demonstrating superior capability in detecting longitudinally safe but laterally dangerous scenarios. These findings provide a quantitative foundation for identifying dangerous lane changes and supporting proactive safety management in expressway merging areas.