A traffic incident is defined by an event which provokes a disruption on the normal (free) flow condition of any highway. Such incidents must be caused by a recurrent excessive demand or, in alternative, by a series of possible stochastic occurrences which may suddenly reduce the road capacity (e.g. car accidents, extreme weather changes). This paper proposes a novel binary supervised learning method to classify congestion predictions regarding their causes - CJAMmer. It leverages on heterogeneous and ubiquitous data sources - such as weather, flow counts and traffic incident event logs - to generalize decision models able to understand the road congestion nature. CJAMmer settles on boosted decision trees using the well-known C4.5, as well as a straightforward feature generation process. A real world experiment was used to compare this method against other state-of-the-art classifiers. The results uncovered the high potential impact of this methodology on industrial scale traffic control systems.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    CJAMmer - traffic JAM Cause Prediction using Boosted Trees


    Beteiligte:


    Erscheinungsdatum :

    01.11.2016


    Format / Umfang :

    1648205 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    PERFORMANCE OF GRADIENT BOOSTED TREES FOR PREDICTION OF COAXIAL INFLOW MODELS WITH XGBOOSTTM

    Seidel, C. / Genter, E. / Peters, D. | British Library Conference Proceedings | 2020


    Boosted Genetic Algorithm Using Machine Learning for Traffic Control Optimization

    Mao, Tuo / Mihaita, Adriana-Simona / Chen, Fang et al. | IEEE | 2022



    SHORT-TERM TRAFFIC VOLUME PREDICTION USING CLASSIFICATION AND REGRESSION TREES

    Xu, Y. / Kong, Q. / Liu, Y. et al. | British Library Conference Proceedings | 2013