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.

Passenger cars have emerged as a substantial segment of the vehicles traversing expressways, generating extensive traffic data on a daily basis. Accurately identifying individual vehicles and their travel patterns and characteristics is crucial in addressing the issues that impede the sustainable development of expressways, including traffic accidents, congestion, environmental pollution, and losses of both personnel and property. Regrettably, the utilization of electronic toll collection (ETC) data on expressways is currently not adequate, and data analysis and feature mining methods are underdeveloped, leading to the undervaluation of data potential. Focusing on ETC data from expressways, this study deeply analyzes the spatiotemporal characteristics of travel by passenger car users. Here, we propose an advanced user classification model by combining the traditional clustering algorithm with the feature grouping recognition model based on a back propagation neural network (BPNN) algorithm. Real-world data on expressway vehicle travel are used to validate our models. The results show a significant improvement in iteration efficiency of over 26.4% and a 23.17% accuracy improvement compared to traditional algorithms. The travel feature grouping recognition model yielded an accuracy of 95.23%. Furthermore, among the identified groups, such as “Public and commercial affairs” and “Commuting”, there is a notable characteristic of high travel frequency and concentrated travel periods. This indicates that these groups have placed significant pressure on the construction of a safe, efficient, and sustainable urban transportation system.