Effective prediction of travel times is central to many advanced traveler information and transportation management systems. In this article we propose a method to predict freeway travel times using a linear model in which the coefficients vary as smooth functions of the departure time. The method is straightforward to implement, computationally efficient and applicable to widely available free...

Road traffic congestion is one of the biggest frustrations for the daily commuter. By improving the currently available travel estimates, one can hope to save time, fuel and the environment by avoiding traffic jams. Before one can predict the best route for a user to take, one must first be able to accurately predict future travel times. In this thesis, we develop a classification-based techniq...

Travel time has been identified as an important variable to evaluate the performance of a transportation system. Based on the travel time prediction, road users can make their optimal decision in choosing route and departure time. In order to utilize adequately the advanced data collection methods that provide realtime different types of information, this paper is aimed at a novel approach to t...

In this paper we propose a “prediction-correction” framework to model traveler’s perception evolution under network disruption. Distinctive from existing models, the proposed framework assumes drivers make their trip choices according to their predictions on future traffic, since previous daily experiences become invalid when network is disrupted. Drivers predict travel costs after network disr...