The paper discusses the issues to face in applications of short-term traffic predictions on urban road networks and the opportunities provided by explicit and implicit models. Different specifications of Bayesian Networks and Artificial Neural Networks are applied for prediction of road link speed and are tested on a large floating car data set. Moreover, two traffic assignment models of different complexity are applied on a sub-area of the road network of Rome and validated on the same floating car data set.


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    Titel :

    Short-term traffic predictions on large urban traffic networks: Applications of network-based machine learning models and dynamic traffic assignment models


    Beteiligte:


    Erscheinungsdatum :

    01.06.2015


    Format / Umfang :

    1640597 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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