Adaptive Traffic Signal Control (ATSC) is an improvement over fixed timing-based or sensor-based signal management. Having real-time learning-based adaptive signals that can optimize the timings of the signal phases of the intersection reduces waiting time and emissions. However, connected vehicles-based adaptive traffic signal requires a large amount of data to be exchanged between vehicles and the infrastructure. Cellular Vehicle to Everything (CV2X) enables us to build adaptive traffic signals using connected vehicle data. In this paper, we investigate the performance of 5G CV2X for an adaptive traffic signal as well as for wireless signaling of traffic signal status using the 5.9 GHz ITS band and 24 GHz mmWave. Our results indicate that 5G CV2X with 5.9 GHz frequency is capable of supporting CV2X-based adaptive traffic signals in heavy vehicular traffic with an average delay of about 4.25 ms for vehicle-to-infrastructure communication, 2.03 ms for infrastructure-to-vehicle communication and 42.71 ms for vehicle-to-vehicle communication. Using 24 GHz mmWave further reduces the communication delay to the sub-millisecond range. Nevertheless, the performance gain is at the cost of additional infrastructure due to mmWave’s shorter range.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Benchmarking the Performance of 5G CV2X for Connected Vehicles based Adaptive Traffic Signal


    Beteiligte:


    Erscheinungsdatum :

    2024-06-02


    Format / Umfang :

    3143182 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Adaptive Traffic Signal Control using CV2X

    Palash, Mahbubul Alam / Wijesekera, Duminda | IEEE | 2023



    CV2X situationally-dependent service prioritization

    WU ZHIBIN / VASSILOVSKI DAN / CHENG HONG | Europäisches Patentamt | 2023

    Freier Zugriff

    TECHNIQUES FOR UTILIZING CV2X REGISTRATION DATA

    LUND BENJAMIN / SHRIVER GARRETT / PARK EDWIN CHONGWOO et al. | Europäisches Patentamt | 2022

    Freier Zugriff