Traffic monitoring systems are nowadays operated with traditionally wired systems which need expensive infrastructure. In this work, we propose a traffic monitoring method for urban environments. The proposed method is passive and exploits RF signals sent in a vehicular network. We utilize different machine learning algorithms to make inference on traffic conditions, directly based on signals observed at a receiver. To verify the feasibility of this approach, we created a database using a ray-tracing simulator and a traffic simulator. Our database stores wireless channel realizations under various traffic conditions created by the traffic simulator. The results show that our method is able to distinguish different traffic intensities with accuracy of %93.0. It also estimates the number of vehicles on the road with mean absolute error of 6.83 for the scenarios with the maximum number of vehicles of 92. The proposed method can be used alongside the current traffic monitoring systems to increase the accuracy of the systems.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Poster: Urban Traffic Monitoring via Machine Learning


    Beteiligte:
    Tulay, H. Bugra (Autor:in) / Barickman, Frank (Autor:in) / Martin, John (Autor:in) / Rao, Sughosh (Autor:in) / Koksal, C. Emre (Autor:in)


    Erscheinungsdatum :

    01.12.2019


    Format / Umfang :

    544387 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Urban Traffic Management using Machine Learning

    Bharti, Ankit / Hasnani, Rohit / Priyadarshan, Manish et al. | IEEE | 2022


    Urban intelligent traffic monitoring intelligent system based on machine vision

    HU XINKE | Europäisches Patentamt | 2021

    Freier Zugriff

    Urban expressway traffic state recognition method based on machine learning

    ZHANG LIYAN / MA JIAN / LIU XIAOFENG et al. | Europäisches Patentamt | 2021

    Freier Zugriff

    Improving Urban Mobility in Dhaka: Machine Learning-Based Traffic Prediction

    Mozumder, Sayif Mahmmud / Alamgir Nishat, Tabassum / Bhuiyan, Bayezid Hasan et al. | IEEE | 2025


    Twitter-informed Prediction for Urban Traffic Flow Using Machine Learning

    Shoaeinaeini, Maryam / Ozturk, Oktay / Gupta, Deepak | IEEE | 2022