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

Abstract

This paper addresses the problem of modeling and predicting urban traffic flow variability, which involves considerable implications for the deployment of dynamic transportation management systems. Traffic variability is described in terms of a volatility metric, i.e., the conditional variance of traffic flow level, as a latent stochastic (low-order Markov) process. A discrete-time parametric stochastic model, referred to as stochastic volatility (SV) model is employed to provide short-term adaptive forecasts of traffic (speed) variability by using real-time detector measurements of volumes and occupancies in an urban arterial. The predictive performance of the SV model is compared to that of the generalized autoregressive conditional heteroscedasticity (GARCH) model, which has been recently used for the traffic variability forecasting, with regard to different measurement locations, forms of data input, lengths of forecasting horizon and performance measures. The results indicate the potential of the SV model to produce out-of-sample forecasts of speed variability with significantly higher accuracy, in comparison to the GARCH model.