Least possible time paths in stochastic, time-varying networks

In this paper, two computationally efficient algorithms are presented for determining the least possible time paths for all origins to a single destination in networks where the arc weights are discrete random variables whose probability distribution functions vary with time. The first algorithm determines the least possible time path from each node for each departure time interval, the least possible travel time and a lower bound on the associated probability of the occurrence of this travel time. The second algorithm determines up to k least possible time paths, the associated travel times and the corresponding probabilities of occurrence of the travel times (or a lower bound on this probability). No such efficient algorithms for determining least time paths in stochastic, time-varying networks exist in the literature.