Stochastic motion planning and applications to traffic

Abstract

This paper presents a stochastic motion planning algorithm and its application to traffic navigation. The algorithm copes with the uncertainty of road traffic conditions by stochastic modeling of travel delay on road networks. The algorithm determines paths between two points that optimize a cost function of the delay data probability distribution. It can be used to find paths that maximize the probability of reaching a destination within a particular travel deadline. For such problems, standard shortest-path algorithms do not work because the optimal substructure property does not hold. We evaluate our algorithm using both simulations and real-world drives, using delay data gathered from a set of taxis equipped with global positioning system sensors and a wireless network. Our algorithm can be integrated into on-board navigation systems as well as route-finding websites, providing drivers with good paths that meet their desired goals.