Dual-Hierarchy Labelling: Scaling Up Distance Queries on Dynamic Road Networks

2025-06-22

AI Summary

To address the low query efficiency and high update overhead for shortest-distance queries between arbitrary node pairs in dynamic road networks, this paper proposes DHL, a dual-hierarchy labeling framework. DHL decouples query optimization from update optimization by constructing a hierarchical labeling index: an upper layer enables low-latency distance queries, while a lower layer supports efficient maintenance under dynamic edge-weight increments and decrements. By integrating parallelized dynamic algorithms with graph-structure optimizations, DHL jointly improves both query responsiveness and update throughput. Extensive experiments on ten large-scale real-world road networks demonstrate that DHL significantly outperforms state-of-the-art methods in index construction and update speed, achieves 2–4× faster query performance, and reduces labeling space overhead to only 10%–20% of competing approaches—thereby breaking the long-standing trade-off between query efficiency and maintenance cost in large-scale dynamic road networks.

Technology Category

• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Computing the shortest-path distance between any two given vertices in road networks is an important problem. A tremendous amount of research has been conducted to address this problem, most of which are limited to static road networks. Since road networks undergo various real-time traffic conditions, there is a pressing need to address this problem for dynamic road networks. Existing state-of-the-art methods incrementally maintain an indexing structure to reflect dynamic changes on road networks. However, these methods suffer from either slow query response time or poor maintenance performance, particularly when road networks are large. In this work, we propose an efficient solution emph{Dual-Hierarchy Labelling (DHL)} for distance querying on dynamic road networks from a novel perspective, which incorporates two hierarchies with different but complementary data structures to support efficient query and update processing. Specifically, our proposed solution is comprised of three main components: emph{query hierarchy}, emph{update hierarchy}, and emph{hierarchical labelling}, where emph{query hierarchy} enables efficient query answering by exploring only a small subset of vertices in the labels of two query vertices and emph{update hierarchy} supports efficient maintenance of distance labelling under edge weight increase or decrease. We further develop dynamic algorithms to reflect dynamic changes by efficiently maintaining the update hierarchy and hierarchical labelling. We also propose a parallel variant of our dynamic algorithms by exploiting labelling structure. We evaluate our methods on 10 large road networks and it shows that our methods significantly outperform the state-of-the-art methods, i.e., achieving considerably faster construction and update time, while being consistently 2-4 times faster in terms of query processing and consuming only 10%-20% labelling space.

Problem

• Efficiently computing shortest-path distances in dynamic road networks
• Overcoming slow query response and poor maintenance in large networks
• Proposing Dual-Hierarchy Labelling for scalable distance queries and updates

Innovation

• Dual-Hierarchy Labelling for dynamic road networks
• Query and update hierarchies for efficient processing
• Parallel dynamic algorithms for faster updates