Using Incremental Many-to-One Queries to Build a Fast and Tight Heuristic for A* in Road Networks

Q2 Mathematics Journal of Experimental Algorithmics Pub Date : 2022-11-30 DOI:10.1145/3571282
Ben Strasser, Tim Zeitz
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引用次数: 5

Abstract

We study exact, efficient, and practical algorithms for route planning applications in large road networks. On the one hand, such algorithms should be able to answer shortest path queries within milliseconds. On the other hand, routing applications often require integrating the current traffic situation, planning ahead with predictions for future traffic, respecting forbidden turns, and many other features depending on the specific application. Therefore, such algorithms must be flexible and able to support a variety of problem variants. In this work, we revisit the A* algorithm to build a simple, extensible, and unified algorithmic framework applicable to many route planning problems. A* has been previously used for routing in road networks. However, its performance was not competitive because no sufficiently fast and tight distance estimation function was available. We present a novel, efficient, and accurate A* heuristic using Contraction Hierarchies, another popular speedup technique. The core of our heuristic is a new Contraction Hierarchies query algorithm called Lazy RPHAST, which can efficiently compute shortest distances from many incrementally provided sources toward a common target. Additionally, we describe A* optimizations to accelerate the processing of low-degree vertices, which are typical in road networks, and present a new pruning criterion for symmetrical bidirectional A*. An extensive experimental study confirms the practicality of our approach for many applications.
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基于增量多对一查询的道路网络a *快速严密启发式算法
我们研究精确、高效和实用的算法,用于大型道路网络的路线规划应用。一方面,这样的算法应该能够在毫秒内回答最短路径查询。另一方面,路由应用程序通常需要集成当前的交通状况,提前规划未来的交通预测,尊重禁止转弯,以及根据具体应用程序的许多其他功能。因此,这种算法必须是灵活的,能够支持各种各样的问题变体。在这项工作中,我们重新审视A*算法,以建立一个简单,可扩展和统一的算法框架,适用于许多路由规划问题。A*以前用于道路网络的路由。然而,由于没有足够快速和严密的距离估计函数,其性能不具有竞争力。我们提出了一种新颖、高效、准确的a *启发式算法,它使用了另一种流行的加速技术——收缩层次结构。我们的启发式算法的核心是一种新的称为Lazy RPHAST的Contraction Hierarchies查询算法,它可以有效地计算从许多增量提供的源到公共目标的最短距离。此外,我们描述了A*优化来加速道路网络中典型的低度顶点的处理,并提出了对称双向A*的新的修剪准则。一项广泛的实验研究证实了我们的方法在许多应用中的实用性。
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来源期刊
Journal of Experimental Algorithmics
Journal of Experimental Algorithmics Mathematics-Theoretical Computer Science
CiteScore
3.10
自引率
0.00%
发文量
29
期刊介绍: The ACM JEA is a high-quality, refereed, archival journal devoted to the study of discrete algorithms and data structures through a combination of experimentation and classical analysis and design techniques. It focuses on the following areas in algorithms and data structures: ■combinatorial optimization ■computational biology ■computational geometry ■graph manipulation ■graphics ■heuristics ■network design ■parallel processing ■routing and scheduling ■searching and sorting ■VLSI design
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