Modeling Traffic Volatility Dynamics in an Urban Network

This article discusses the application of Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) time series models for representing the dynamics of traffic flow volatility. The methods encountered in the literature so far, focus on the levels of traffic flows while regarding variance constant through time. The approach adopted in this paper concentrates mostly on the autoregressive properties of traffic variability aiming to provide better confidence intervals for traffic flow forecasts. The model building procedure is illustrated using 7.5 min average traffic flow data for a set of eleven 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. Kamarianakis, Kanas and Prastacos 3