Highlights Data-driven approach for local traffic state estimation and prediction. Pre-existing modeling components in a methodologically novel way. Flexibility in incorporating additional explanatory variables. Shown to outperform current state-of-the-art models.

    Abstract Traffic state prediction is a key problem with considerable implications in modern traffic management. Traffic flow theory has provided significant resources, including models based on traffic flow fundamentals that reflect the underlying phenomena, as well as promote their understanding. They also provide the basis for many traffic simulation models. Speed–density relationships, for example, are routinely used in mesoscopic models. In this paper, an approach for local traffic state estimation and prediction is presented, which exploits available (traffic and other) information and uses data-driven computational approaches. An advantage of the method is its flexibility in incorporating additional explanatory variables. It is also believed that the method is more appropriate for use in the context of mesoscopic traffic simulation models, in place of the traditional speed–density relationships. While these general methods and tools are pre-existing, their application into the specific problem and their integration into the proposed framework for the prediction of traffic state is new. The methodology is illustrated using two freeway data sets from Irvine, CA, and Tel Aviv, Israel. As the proposed models are shown to outperform current state-of-the-art models, they could be valuable when integrated into existing traffic estimation and prediction models.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Dynamic data-driven local traffic state estimation and prediction


    Beteiligte:


    Erscheinungsdatum :

    2013-05-31


    Format / Umfang :

    19 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Dynamic data-driven local traffic state estimation and prediction

    Antoniou, Constantinos | Online Contents | 2013


    ETC data-based highway traffic state dynamic estimation method

    CHEN TAO / LI XIAOYANG / GU RUNPENG | Europäisches Patentamt | 2024

    Freier Zugriff

    Dynamic traffic diversion model based on dynamic traffic demand estimation and prediction

    Peng-peng, Jiao / Yi-gang, Li / Dong-yue, Li | IET | 2018

    Freier Zugriff

    Scalable data-driven short-term traffic prediction

    Friso, K. / Wismans, L. J. J. / Tijink, M. B. | IEEE | 2017


    Dynamic traffic diversion model based on dynamic traffic demand estimation and prediction

    Peng‐peng, Jiao / Yi‐gang, Li / Dong‐yue, Li | Wiley | 2018

    Freier Zugriff