Shortest Paths in Stochastic Time-dependent Networks with Link Travel Time Correlation

This paper develops a simple robust framework for the problem of finding the least expected travel time route from any node to any given destination in a stochastic and time-dependent network. We consider both spatial and temporal link travel time correlations in the proposed solution based on a dynamic programming approach. In particular, the spatial correlation is represented by a Markovian property of the link states where each link is assumed to experience congested or uncongested conditions. The temporal correlation is manifested through the time-dependent expected link travel time given the condition of the link traversed. The framework enables a route guidance system where at any decision node within a network, one can make a decision based on current traffic information about which node to take next to achieve the shortest expected travel time to the destination. Numerical examples are presented to illustrate the computational steps involved in the framework of making route choice decisions and to demonstrate the effectiveness of the proposed solution. Dong, Vu, Nazarathy, Vo, Li, Hoogendoorn 4