原文传递
Reconstructing vehicle trajectories on freeways based on motion detection data of connected and automated vehicles
| 题名： | Reconstructing vehicle trajectories on freeways based on motion detection data of connected and automated vehicles |
| 正文语种： | eng |
| 作者： | Peng Chen;Tong Wang;Nan Zheng |
| 作者单位： | Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University;Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University;Department of Civil Engineering Monash University |
| 关键词： | trajectory reconstruction;connected and automated vehicle;mixed traffic;mobile sensing;intelligent driver model |
| 摘要： | 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. |
| 出版年： | 2022 |
| 期刊名称： | Journal of Intelligent Transportation Systems |
| 卷： | 26 |
| 期： | 1/6 |
| 页码： | 644-659 |
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