Online Stochastic Routing Incorporating Real-Time Traffic Information

Abstract

In this paper, online stochastic routing policies are developed to identify the optimal next action (path choice) at the current decision node (intersection) for travelers on the basis of their preference for future paths with the shortest travel time, the lowest travel time variability, or a combination, given the network conditions. A modified label-correcting algorithm is provided to solve for the shortest path that results from the proposed routing policies. Its running time is bounded by O(mn2), where m and n are the number of arcs and nodes, respectively, in the network. Since real-time traffic information is usually available with a certain level of accuracy, the proposed online routing policy integrates an existing information fusion model, which provides real-time short-term arc travel time distributions by considering information accuracy. Numerical experiments are used to demonstrate the performance of the proposed routing policies and algorithms, as well as the impacts of real-time information accuracy on the online stochastic routing.