In order to further reduce emissions of particulate matter, nitrogen oxides and carbon dioxide into the atmosphere from road transport, the causes of congestion on city streets should be eliminated. The movement of transport in urban conditions is quite cyclical, however, in the conditions of each city it has its own specifics. The use of adaptive traffic light control systems within the framework of intelligent transport systems (ITS) contributes to solving this problem. However, there is a need not only to directly respond to traffic jams and reduce the speed of vehicles, but also to predict these situations in advance. The article shows an example of using the normalized range method (Hurst exponent) for predictive detection of changes in traffic flow indicators on a city highway for subsequent correction of traffic lights.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Detection of Abnormal Traffic Flow with the Hurst Parameter for Traffic Management


    Beteiligte:
    Karelina, E. A. (Autor:in) / Stepanov, A. A. (Autor:in) / Smirnov, P. I. (Autor:in) / Podgornuy, A. V. (Autor:in) / Guly, V. V. (Autor:in)


    Erscheinungsdatum :

    15.11.2023


    Format / Umfang :

    2239924 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Application of the Hurst index to research the traffic flow intensity

    M. G. Boyarshinov / A. S. Vavilin / E. V. Vaskina | DOAJ | 2022

    Freier Zugriff

    Methodology for Detecting Anomalous Traffic Flows in the Network Based on the Hurst Parameter

    Subbotin, Boris S. / Sudorgin, Roman O. / Karelina, Ekaterina A. et al. | IEEE | 2024


    DeepFlow: Abnormal Traffic Flow Detection Using Siamese Networks

    Sabour, Sepehr / Rao, Sanjeev / Ghaderi, Majid | ArXiv | 2021

    Freier Zugriff

    Highway Traffic Flow Prediction Supporting Multi-Traffic Parameter Detection

    Zhu, Ruixin / Chen, Ling / Jiang, Xinyu et al. | IEEE | 2023