Knowledge of the upcoming traffic velocity along a route can help in many respects, among them optimizing energy management for hybrid vehicles, which, for instance, could reduce instantaneous battery usage if a traffic jam is upcoming in the next future. While such kind of knowledge can hardly be precise on a single-vehicle level, we show in this paper that a prediction method which combines present and past Vehicle-to-Everything (V2X) information can strongly improve the energy efficiency. Our approach is first compared with other prevailing prediction methods and its advantages in terms of stability and accuracy are shown. Then the prediction results are applied in a hybrid powertrain control example, in which its potential in fuel savings are illustrated.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    V2X Database Driven Traffic Speed Prediction


    Beteiligte:
    Adelberger, Daniel (Autor:in) / Deng, Junpeng (Autor:in) / Del Re, Luigi (Autor:in)


    Erscheinungsdatum :

    19.09.2021


    Format / Umfang :

    1784109 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Traffic speed prediction device and traffic speed prediction method

    KIM NAM-HYUK | Europäisches Patentamt | 2025

    Freier Zugriff

    Traffic Speed Prediction with Neural Networks

    Çakmak, Umut Can / Apaydin, Mehmet Serkan / Çatay, Bülent | British Library Conference Proceedings | 2017



    Traffic speed prediction method based on multi-moment traffic flow

    GAO YACONG / ZHOU CHENJING / CHEN CHUN'AN | Europäisches Patentamt | 2025

    Freier Zugriff

    Traffic speed prediction using deep learning method

    Yuhan Jia / Jianping Wu / Yiman Du | IEEE | 2016