This article discusses the application of generalized autoregressive conditional heteroscedasticity (GARCH) time series models for representing the dynamics of traffic flow volatility. The methods encountered in the literature focus on the levels of traffic flows and assume that variance is constant through time. The approach adopted in this paper concentrates primarily on the autoregressive properties of traffic variability, with the aim to provide better confidence intervals for traffic flow forecasts. The model-building procedure is illustrated with 7.5-min average traffic flow data for a set of 11 loop detectors located at major arterials that direct to the center of the city of Athens, Greece. A sensitivity analysis for coefficient estimates is undertaken with respect to both time and space.


    Zugriff

    Download

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Modeling Traffic Volatility Dynamics in an Urban Network


    Weitere Titelangaben:

    Transportation Research Record: Journal of the Transportation Research Board


    Beteiligte:


    Erscheinungsdatum :

    01.01.2005




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Modeling Traffic Volatility Dynamics in an Urban Network

    Kamarianakis, Yiannis / Kanas, Angelos / Prastacos, Poulicos | Transportation Research Record | 2005


    Modeling Traffic Volatility Dynamics in an Urban Network

    Kamarianakis, Yiannis | Online Contents | 2005


    Modeling Traffic Volatility in Urban Network

    National Research Council (U.S.) | British Library Conference Proceedings | 2005



    Short-Term Prediction of Urban Traffic Variability: Stochastic Volatility Modeling Approach

    Tsekeris, T. / Stathopoulos, A. | British Library Online Contents | 2010