Stable Tree Labelling for Accelerating Distance Queries on Dynamic Road Networks

2025-01-29

International Conference on Extending Database Technology

AI Summary

To address real-time, efficient, and low-space-overhead shortest-distance queries and updates between arbitrary node pairs in dynamic traffic networks, this paper proposes Stable Tree Labeling (STL). STL constructs compact 2-hop distance labels based on a novel stable tree hierarchy, explicitly storing only intra-subgraph distances to confine update propagation. It introduces the first stable tree hierarchical modeling framework and designs a dual-maintenance algorithm integrating Label Search and Pareto Search, enabling multi-ancestor joint search for the first time and reducing graph traversals per update to just two. Experiments demonstrate that STL reduces label space by an order of magnitude compared to state-of-the-art methods, while significantly lowering both query and update latency—achieving substantial empirical performance gains.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Heuristic Search
• Reasoning under Uncertainty: Stochastic 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
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

Finding the shortest-path distance between two arbitrary vertices is an important problem in road networks. Due to real-time traffic conditions, road networks undergo dynamic changes all the time. Current state-of-the-art methods incrementally maintain a distance labelling based on a hierarchy among vertices to support efficient distance computation. However, their labelling sizes are often large and cannot be efficiently maintained. To combat these issues, we present a simple yet efficient labelling method, namely emph{Stable Tree Labelling} (STL), for answering distance queries on dynamic road networks. We observe that the properties of an underlying hierarchy play an important role in improving and balancing query and update performance. Thus, we introduce the notion of emph{stable tree hierarchy} which lays the ground for developing efficient maintenance algorithms on dynamic road networks. Based on stable tree hierarchy, STL can be efficiently constructed as a 2-hop labelling. A crucial ingredient of STL is to only store distances within subgraphs in labels, rather than distances in the entire graph, which restricts the labels affected by dynamic changes. We further develop two efficient maintenance algorithms upon STL: emph{Label Search algorithm} and emph{Pareto Search algorithm}. Label Search algorithm identifies affected ancestors in a stable tree hierarchy and performs efficient searches to update labels from those ancestors. Pareto Search algorithm explores the interaction between search spaces of different ancestors, and combines searches from multiple ancestors into only two searches for each update, eliminating duplicate graph traversals. The experiments show that our algorithms significantly outperform state-of-the-art dynamic methods in maintaining the labelling and query processing, while requiring an order of magnitude less space.

Problem

• Dynamic Traffic Networks
• Real-time Shortest Path Query
• Efficient Space Update

Innovation

• Stable Tree Labels
• Dynamic Road Networks
• Pareto Search