A Vehicle Conflict Risk Identification Method Based on an Improved Intelligent Driver Model

With accelerating urbanization, vehicle conflict risk identification has become a critical research focus for improving road traffic safety. To address the discrepancies between microscopic traffic simulation outputs and real-world traffic flow characteristics caused by stochastic factors, this study proposes a vehicle conflict risk identification framework based on an enhanced intelligent driver model (IDM). Through VISSIM secondary development and scenario calibration, a simulation environment was constructed to replicate real-world road test conditions. Multi-vehicle trajectory data were employed to calibrate the IDM parameters. The conventional IDM was further improved by integrating driver-state variables and braking dynamics, while the inverse time to collision (ITTC) was adopted as the primary metric for collision risk assessment. Three risk levels were defined: potential collision (PC), general collision (GC), and serious collision (SC). The experimental results demonstrated strong alignment between the enhanced model and VISSIM simulations in vehicle speed and headway calibration, achieving classification accuracy rates exceeding 92.71%. The ITTC thresholds (0.25/s and 0.48/s) effectively differentiated between risk levels. This research provides theoretical and technical foundations for dynamic vehicle conflict risk identification and offers actionable insights for safety-critical decision making in intelligent driving systems.