This study compares the performance of statistical and Bayesian combination models with classical single time series models for short-term traffic forecasting. Combinations are based on fractionally integrated autoregressive time series models of travel speed with exogenous variables that consider speed's spatio-temporal evolution, and volume and weather conditions. Several statistical hypotheses on the effectiveness of combinations compared to the single models are also tested. Results show that, in the specific application, linear regression combination techniques may provide more accurate forecasts than Bayesian combination models. Moreover, combining models with different degrees of spatio-temporal complexity and exogeneities is most likely to be the best choice in terms of accuracy. Moreover, the risk of combining forecasts is lower than the risk of choosing a single model with increased spatio-temporal complexity.@en

We examine the effects of incident occurrence on freeway traffic. Although the true influence of a freeway incident may not be directly observed, it may be identified using the maximum spatial extent of the disturbance induced to upstream traffic. Spatial and temporal extent is susceptible to various traffic, weather, geometry and incident specific factors. The above framework is implemented using a Multiple Indicators-Multiple Causes (MIMIC) latent variable model. Results using data from Athens, Greece indicate that the MIMIC model is able to accurately determine the influence of an incident on upstream traffic with 72% probability of producing mean square errors less than 0.05. Speed, lane volume, alignment, rainfall intensity, clearance time and whether the incident is a secondary or a primary one are among the most influential factors for assessing the anticipatory effect of incidents to traffic.@en