Adaptive Vehicle Navigation With En Route Stochastic Traffic Information

This paper develops an adaptive approach for vehicle navigation in a stochastic network with real-time en route traffic information. This stochastic and adaptive approach is formulated as a probabilistic dynamic programming problem and is solved through a backward recursive procedure. The formulation, as a modeling framework, is designed to be able to incorporate various sources of information and real-time traffic states to improve routing quality. In this paper, we prove that the approach outperforms deterministic instantaneous shortest paths in a statistical sense. We also analyze the algorithm´s computational efficiency. The results from numerical examples are included to illustrate the performance of the adaptive routing policy that was generated by the formulation.