4.4 Article

When hierarchy meets 2-hop-labeling: efficient shortest distance and path queries on road networks

Journal

VLDB JOURNAL
Volume -, Issue -, Pages -

Publisher

SPRINGER
DOI: 10.1007/s00778-023-00789-x

Keywords

Shortest distance; Road network; Tree decomposition

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This paper addresses the issue of computing the shortest distance between two vertices in road networks. The authors propose a novel hierarchical 2-hop index (H2H-Index) and a query processing algorithm based on this index to overcome the limitations of existing solutions. Experimental results show that their approach achieves a significant speedup in query processing compared to state-of-the-art methods.
Computing the shortest distance between two vertices is a fundamental problem in road networks. Since a direct search using the Dijkstra's algorithm results in a large search space, researchers resort to indexing-based approaches. State-of-the-art indexing-based solutions can be categorized into hierarchy-based solutions and hop-based solutions. However, the hierarchy-based solutions require large search space for long-distance queries, while the hop-based solutions result in high computational waste for short-distance queries. To overcome the drawbacks of both solutions, in this paper, we propose a novel hierarchical 2-hop index (H2H-Index) which assigns a label for each vertex and at the same time preserves a hierarchy among all vertices. With the H2H-Index, we design an efficient query processing algorithm with performance guarantees by visiting part of the labels for the source and the destination based on the hierarchy. We propose a novel algorithm to construct the H2H-Indexbased on distance preserved graphs. We also extend the H2H-Indexand propose a set of algorithms to identify the shortest path between vertices. We conducted extensive performance studies using large real road networks including the whole USA road network. The experimental results demonstrate that our approach can achieve a speedup of an order of magnitude in query processing compared to the state-of-the-art while consuming comparable indexing time and index size.

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