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.

随机最短路径 (SSP) 计算通常在非常严格的时间限制下执行，因此计算效率至关重要。CPU 时间的一个主要决定因素是使用的场景数量。我们证明，通过仔细选择正确的场景生成方法来寻找场景，对于给定数量的场景，计算质量可以比随机抽样得到显着提高。我们研究了来自高速公路网络和城市道路网络的大量 SSP 实例，它们分别涉及 10,512 和 37,500 个空间和时间相关的速度变量。基于对 SSP 问题总共 42 个起点-终点对和 6 个典型目标函数的实验结果，我们发现（1）场景生成方法生成无偏场景，并且在稳定性（即相对差异和方差）方面优于随机抽样，无论使用哪个来源-目的地对和目标函数；(2) 为达到一定的精度，场景生成所需的场景数量远低于随机抽样，通常在高速公路网络1%的稳定性水平下低6-10倍左右；(3) 不同的起点-终点对和不同的目标函数可能需要不同数量的场景来实现指定的稳定性。场景生成所需的场景数量远低于随机抽样的数量，对于高速公路网络中 1% 的稳定性水平，通常低 6-10 倍；(3) 不同的起点-终点对和不同的目标函数可能需要不同数量的场景来实现指定的稳定性。场景生成所需的场景数量远低于随机抽样的数量，对于高速公路网络中 1% 的稳定性水平，通常低 6-10 倍；(3) 不同的起点-终点对和不同的目标函数可能需要不同数量的场景来实现指定的稳定性。