Real time route guidance with correlated link cost

Route finding issues have always been a significant research focus in intelligence transportation system. Many solution models have been proposed in the previous work and analyzed in detail. Successive link travel time correlation has been identified to play an important role in these models to realistically reflect the property of traffic flows. In this paper, we establish a framework to find the best route in a stochastic time-dependent network by considering link travel time indeterminacy and correlation between adjacent links with real time information. We provide explicit mathematical formulations to update the outing link travel time distributions according to the real time information and help the travelers to find the best route to their destination. A simple illustrative example is shown to demonstrate the effectiveness and advantages of the proposed method.