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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:
    Chen, Peng (Autor:in) / Wang, Tong (Autor:in) / Zheng, Nan (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2022-10-21


    Format / Umfang :

    16 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Assessing the Impact of Automated and Connected Automated Vehicles on Virginia Freeways

    Kim, Bumsik / Heaslip, Kevin P. / Aad, Mirla Abi et al. | Transportation Research Record | 2021



    Cooperative lane control application for fully connected and automated vehicles at multilane freeways

    Khattak, Zulqarnain H. / Smith, Brian L. / Park, Hyungjun et al. | Elsevier | 2019


    Connected Vehicle-Based Advanced Detection of “Slow-Down” Events on Freeways

    Zhao, Zhouqiao / Wu, Guoyuan / Barth, Matthew J. et al. | IEEE | 2021