On the Short-term Prediction of Traffic State: An Application on Urban Freeways in ROME

This paper explores the traffic state estimation on freeways in urban areas combining point-based and route-based data in order to properly feed a second order traffic flow model, recursively corrected by an Extended Kalman Filter. In order to overcome the possible lack of real-time information, authors propose to use simulation-based data in order to improve the accuracy of the traffic state estimation. This model was tested on a urban freeway stretch in Rome, for which a set of real-time data during the morning of a typical workday was available. Results of the application point out the benefits of the proposed approach in predicting the traffic state, as shown by GEH, RMSE and RME values similar to those presented in the literature.