Vehicle-Trajectory Prediction Method for an Extra-Long Tunnel Based on Section Traffic Data

Abstract

The driving situation is complicated in an extra-long tunnel. If a traffic collision happens, car evacuation and people rescue in the tunnel will be more challenging. The driving safety and traffic efficiency of an extra-long tunnel will be considerably enhanced if vehicle trajectory can be precisely assessed and the vehicle driving risk can be predicted in advance. However, due to the limitations of the data capture of vehicle-mounted equipment in tunnels, estimating vehicle trajectory with vehicle-side data is difficult. In this research, a vehicle-trajectory prediction model based on section traffic data in an extra-long tunnel is developed using existing roadside traffic-gathering equipment. The model re-optimizes the driver-sensitivity coefficient by calibrating the parameters of the vehicle-following model based on mining the motion laws of cars under different scenarios. The model is validated using observed velocity and position data, and the average accuracy of velocity prediction is 94.14% and 95.45% for trajectory prediction. The results show that using existing roadside collecting equipment, this model can accurately forecast vehicle trajectories in extra-long tunnels.