On the short-term prediction of traffic state: an application on urban freeways in Rome

摘要

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