Online dynamic travel time prediction using speed and flow measurements

Traffic forecasting is considered nowadays as one of the most important traffic management techniques on road networks. To provide suitable control strategies and advanced traveler information, which improve traffic performance, a continuous short-term prediction is a significant requirement. In this paper, we propose a new approach for travel time forecasting between two points of interest of a given highway divided in nodes and links. Since nodes and links have distinct characteristics, two different prediction methods are proposed. The resulting predicted travel time is then computed as the sum of predicted travel times in nodes with those in links. An adaptive Kalman filtering approach is considered for predicting sojourn time in nodes and flows at boundaries of links. Inside links, divided in cells for improving resolution, a deterministic observer is used for computing unmeasured densities. The performance of the proposed method is evaluated by using data of the Grenoble south ring, a case study of the NoE Hycon2.