High Throughput Shortest Distance Query Processing on Large Dynamic Road Networks

TL;DR

This work tackles high-throughput shortest-path querying on large dynamic road networks by introducing Partitioned Shortest Path (PSP) indexes and two novel multi-stage index structures. It first establishes a cross-boundary strategy to speed up PSP queries and analyzes its theoretical upper bound, then develops Partitioned Multi-stage Hub Labeling (PMHL) to blend CH and H2H techniques for fast maintenance and progressively faster queries. Building on this, it introduces tree-decomposition-based TD-partitioning and PostMHL, a post-boundary enhanced index that achieves near-H2H-level query efficiency with accelerated updates. Empirical results on real-world networks show substantial throughput gains, with PostMHL and PMHL yielding up to two orders of magnitude higher query throughput than state-of-the-art baselines, demonstrating practical viability for dynamic SP in large-scale road networks.

Abstract

Shortest path (SP) computation is the building block for many location-based services, and achieving high throughput SP query processing with real-time response is crucial for those services. However, existing solutions can hardly handle high throughput queries on large dynamic road networks due to either slow query efficiency or poor dynamic adaption. In this paper, we leverage graph partitioning and propose novel Partitioned Shortest Path (PSP) indexes to address this problem. Specifically, we first put forward a cross-boundary strategy to accelerate the query processing of PSP index and analyze its efficiency upper bound theoretically. After that, we propose a non-trivial Partitioned Multi-stage Hub Labeling (PMHL) that subtly aggregates multiple PSP strategies to achieve fast index maintenance and consecutive query efficiency improvement during index update. Lastly, to further optimize throughput, we design tree decomposition-based graph partitioning and propose Post-partitioned MHL (PostMHL) with faster query processing and index update. Experiments on real-world road networks show that our methods outperform state-of-the-art baselines in query throughput, yielding up to 2 orders of magnitude improvement.

Figures (18)

• Figure 1: Throughput Illustration of Dynamic SP Index (A larger Y-axis value (QPS, Queries per Second) indicates faster query processing; a larger yellow area indicates a higher QoS-independentqu2006preference throughput; length of red and green is index update and query processing time, respectively)
• Figure 2: Example Road Network $G$, CH Index $G_{sc}$, Tree Decomposition $T$, and H2H Index
• Figure 3: Illustration of No-boundary and Post-boundary Strategy
• Figure 4: Illustration of Cross-boundary Strategy
• Figure 5: Illustration of Boundary-first Vertex Ordering

Theorems & Definitions (16)

• Lemma 1
• proof
• Definition 1: Tree Decomposition
• Lemma 2
• proof
• Lemma 3
• proof
• Theorem 1: The Upper Bound of PSP Query Efficiency
• proof
• Lemma 4