随机准时到达问题的易处理寻路算法
Tractable Pathfinding for the Stochastic On-Time Arrival Problem
摘要：
我们提出了一种新的和更有效的技术来计算的路线，最大限度地提高概率的随机网络，也被称为基于路径的随机准时到达（SOTA）问题。我们的主要贡献是一个寻路算法，它使用的解决方案，以基于政策的SOTA问题-这是伪多项式时间复杂度的时间预算的旅程-作为搜索启发式的最佳path. In特别是，我们表明，这种启发式可以是非常有效的，在实践中，有效地使之成为可能，以解决基于路径的SOTA问题，尽快为基于政策的SOTA问题。我们的第二个贡献是基于策略的预处理的SOTA问题的路径为基础的预处理的扩展。在这个过程中，我们还介绍了弧势，一个更有效的推广随机弧旗，可用于基于策略和路径的SOTA。在开发了路径查找和预处理算法之后，我们在两个不同的现实网络上评估了它们的性能。据我们所知，这些技术提供了最有效的计算策略的路径为基础的SOTA问题的一般概率分布，无论有和没有预处理。
英文摘要：
We present a new and more efficient technique for computing the route that maximizes the probability of on-time arrival in stochastic networks, also known as the path-based stochastic on-time arrival (SOTA) problem. Our primary contribution is a pathfinding algorithm that uses the solution to the policy-based SOTA problem---which is of pseudo-polynomial-time complexity in the time budget of the journey---as a search heuristic for the optimal path. In particular, we show that this heuristic can be exceptionally efficient in practice, effectively making it possible to solve the path-based SOTA problem as quickly as the policy-based SOTA problem. Our secondary contribution is the extension of policy-based preprocessing to path-based preprocessing for the SOTA problem. In the process, we also introduce Arc-Potentials, a more efficient generalization of Stochastic Arc-Flags that can be used for both policy- and path-based SOTA. After developing the pathfinding and preprocessing algorithms, we evaluate their performance on two different real-world networks. To the best of our knowledge, these techniques provide the most efficient computation strategy for the path-based SOTA problem for general probability distributions, both with and without preprocessing.
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