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.


    Access

    Download

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Modeling Traffic Volatility Dynamics in an Urban Network


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2005-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    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