Reconstructing vehicle trajectories on freeways based on motion detection data of connected and automated vehicles

Abstract

Determining the trajectories of all vehicles on freeways is a challenging yet critical topic as trajectories reflect the characteristics of traffic flow and serve as a good basis for traffic management and control. With the advances of mobile sensing technology, connected and automated vehicles (CAVs) as a new source of probe car can provide high-resolution sampled trajectory data. Furthermore, as CAVs sense the surrounding traffic situation, they can offer information to understand the vehicle motions around them. Utilizing the data from CAVs thus supports the trajectory reconstruction of fully-sampled traffic flow and enables sophisticated evaluation of traffic states. This study develops a CAV detection data-based trajectory reconstruction method for freeway traffic. First, the intelligent driver model (IDM) is used to judge the motion of undetected human-driven vehicles (HV) between trajectories. The undetected vehicles will be inserted in traffic flow with the position and speed estimated by a modified IDM model. Subsequently, the complete trajectories of the inserted HVs will be reconstructed by IDM. Last, the validity of the method is verified by both simulation and empirical experiments. The results demonstrate the proposed method enables sufficient reconstruction of vehicle trajectories under different traffic densities and penetration rates of CAVs.

Keywords:

• trajectory reconstruction
• connected and automated vehicle
• mixed traffic
• mobile sensing
• intelligent driver model

Acknowledgements

The authors appreciate the National Key R&D Program of China (2018YFB1600500) and the National Natural Science Foundation of China (No. 61873018) for support of this research.

Disclosure statement

No potential competing interest was reported by the author(s).