Abstract
Stochastic shortest path (SSP) computations are often performed under very strict time constraints, so computational efficiency is critical. A major determinant for the CPU time is the number of scenarios used. We demonstrate that by carefully picking the right scenario generation method for finding scenarios, the quality of the computations can be improved substantially over random sampling for a given number of scenarios. We study extensive SSP instances from a freeway network and an urban road network, which involve 10,512 and 37,500 spatially and temporally correlated speed variables, respectively. On the basis of experimental results from a total of 42 origin–destination pairs and 6 typical objective functions for SSP problems, we find that (1) the scenario generation method generates unbiased scenarios and strongly outperforms random sampling in terms of stability (i.e., relative difference and variance) whichever origin–destination pair and objective function is used; (2) to achieve a certain accuracy, the number of scenarios required for scenario generation is much lower than that for random sampling, typically about 6–10 times lower for a stability level of 1% in the freeway network; and (3) different origin–destination pairs and different objective functions could require different numbers of scenarios to achieve a specified stability.