可定制收缩层次中的最近邻算法及其应用
Nearest-Neighbor Queries in Customizable Contraction Hierarchies and Applications
摘要：
可定制的收缩层次结构是实践中最流行的路线规划框架之一，由于其简单性和多功能性。在这项工作中，我们提出了一种新的算法，通过系统地探索相关的分离器分解树，在可定制的收缩层次结构中找到k-最近邻。与以前的基于桶的方法相比，我们的算法需要更少的目标相关的预处理工作。此外，我们在两个具体的应用程序中使用我们的新方法。第一个应用是在线k-最近的兴趣点查询，其中兴趣点仅在查询时显示。我们实现查询时间约25毫秒的大陆公路网，这是足够快的交互式系统。第二个应用是产生旅行需求。我们展示了如何加速最近推出的出行需求生成器的一个因素超过50，使用我们的新的最近邻算法。
英文摘要：
Customizable contraction hierarchies are one of the most popular route planning frameworks in practice, due to their simplicity and versatility. In this work, we present a novel algorithm for finding k-nearest neighbors in customizable contraction hierarchies by systematically exploring the associated separator decomposition tree. Compared to previous bucket-based approaches, our algorithm requires much less target-dependent preprocessing effort. Moreover, we use our novel approach in two concrete applications. The first application are online k-closest point-of-interest queries, where the points of interest are only revealed at query time. We achieve query times of about 25 milliseconds on a continental road network, which is fast enough for interactive systems. The second application is travel demand generation. We show how to accelerate a recently introduced travel demand generator by a factor of more than 50 using our novel nearest-neighbor algorithm.
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