Forecasting Traffic Flow Conditions in an Urban Network: Comparison of Multivariate and Univariate Approaches

Several univariate and multivariate models have been proposed for performing short term forecasting of traffic flow. In this paper two different univariate (historical average and ARIMA) and two multivariate (VARMA and STARIMA) models are presented and discussed. A comparison of the forecasting performance of these four models is undertaken using datasets from 25 loop detectors located in major arterials in the city of Athens, Greece. The variable under study is the relative velocity that is the traffic volume divided by the road occupancy. Although the specification of the network’s neighborhood structure for the STARIMA model was relative simple and can be further refined, the results obtained indicate a comparable forecasting performance for the ARIMA, VARMA and STARIMA models. The historical average model could not cope with the variability of the data sets at hand.