Parallel Contraction Hierarchies Construction on Road Networks

IF 8.9 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zi Chen;Xinyu Ji;Long Yuan;Xuemin Lin;Wenjie Zhang;Shan Huang
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引用次数: 0

Abstract

Shortest path query on road networks is a fundamental problem to support many location-based services and wide variant applications. Contraction Hierarchies(CH) is widely adopted to accelerate the shortest path query by leveraging shortcuts among vertices. However, the state-of-the-art CH construction method named $\mathsf{VCHCons}$ suffers from inefficiencies due to their strong reliance on pre-determined vertex order. This leads to the generation of a large number of invalid shortcuts and the limit of parallel processing capability. Motivated by it, in this paper, an innovative CH construction algorithm called $\mathsf{ECHCons}$ is devised following an edge-centric paradigm, which addresses the issue of invalid shortcut production by introducing a novel edge-ordering strategy. Furthermore, it optimizes shortcut calculation within a dynamically constructed optimal subgraph, which is significantly smaller than the original network, thus shrinking the traversal space during index construction. To further enhance efficiency and overcome the limitations in parallelism inherent to $\mathsf{VCHCons}$ , our approach leverages batch contraction of edges and introduces a well-defined lower bound technique to unlock more efficient parallel computation resources. Our approach provides both theoretical guarantee and practical advancement in CH construction. Extensive and comprehensive experiments are conducted on real road networks. The experimental results demonstrate the effectiveness and efficiency of our proposed approach.
在路网上构建并行收缩层次结构
道路网络上的最短路径查询是支持许多基于位置的服务和多种应用的基本问题。人们广泛采用收缩层次结构(Contraction Hierarchies,CH)来利用顶点间的捷径加速最短路径查询。然而,名为 $\mathsf{VCHCons}$ 的最先进 CH 构建方法由于严重依赖于预先确定的顶点顺序而效率低下。这导致生成大量无效捷径,并限制了并行处理能力。受此启发,本文按照以边缘为中心的范式,设计了一种名为 $\mathsf{ECHCons}$ 的创新 CH 构建算法,通过引入一种新颖的边缘排序策略,解决了无效捷径生成的问题。此外,它还优化了动态构建的最优子图内的快捷方式计算,该子图明显小于原始网络,从而缩小了索引构建过程中的遍历空间。为了进一步提高效率并克服 $\mathsf{VCHCons}$ 固有的并行性限制,我们的方法利用了边的批量收缩,并引入了定义明确的下限技术,以释放更高效的并行计算资源。我们的方法为 CH 的构建提供了理论保证和实践进展。我们在真实的道路网络上进行了广泛而全面的实验。实验结果证明了我们提出的方法的有效性和高效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering 工程技术-工程:电子与电气
CiteScore
11.70
自引率
3.40%
发文量
515
审稿时长
6 months
期刊介绍: The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.
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