Optimized Customizable Route Planning in Large Road Networks with Batch Processing

2026-04-12

AI Summary

This work addresses the challenge of efficiently supporting dynamic or user-defined cost metrics in large-scale road network routing under high concurrency, a task hindered by limitations in path storage and reconstruction efficiency. Building upon the Customizable Transit Labeling (CTL) framework, the study proposes an optimized path storage structure and reconstruction mechanism, along with the first batch-processing strategy tailored for CTL that significantly boosts performance by sharing computations across multiple queries. The authors further design several algorithmic variants to balance memory usage and query performance and adopt a decoupled architecture separating graph preprocessing from customization. Extensive experiments on 13 real-world road networks demonstrate that the proposed method substantially outperforms the state-of-the-art in both query speed and memory efficiency, scaling effectively to scenarios with millions of concurrent queries.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Algorithm Configuration
• 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
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Modern route planners such as Google Maps and Apple Maps serve millions of users worldwide, optmizing routes in large-scale road networks where fast responses are required under diverse cost metrics including travel time, fuel consumption, and toll costs. Classical algorithms like Dijkstra or A$^*$ are too slow at this scale, and while index-based techniques achieve fast queries, they are often tied to fixed metrics, making them unsuitable for dynamic conditions or user-specific metrics. Customizable approaches address this limitation by separating metric-independent preprocessing and metric-dependent customization, but they remain limited by slower query performance. Notably, Customizable Tree Labeling (CTL) was recently introduced as a promising framework that combines tree labelings with shortcut graphs. The shortcut graph enables efficient customization to different cost metrics, while tree labeling, supported by path arrays, provides fast query answering. Although CTL enables optimizing routes under different cost metrics, it still faces challenges in storing and reconstructing path information efficiently, which hinders its scalability for answering millions of queries. In this article, we build on the Customizable Tree Labeling framework to introduce new optimizations for the storage and reconstruction of path information. We develop several algorithmic variants that differ in the information retained within shortcut graphs and path arrays, offering a spectrum of trade-offs between memory usage and query performance. To further enhance scalability, we propose a batch processing strategy that shares path information across queries to eliminate redundant computation. Empirically, we have evaluated the performance of our algorithms on 13 real-world road networks. The results show that they significantly outperform state-of-the-art methods.

Problem

• Customizable Route Planning
• Path Reconstruction
• Scalability
• Large Road Networks
• Batch Processing

Innovation

• Customizable Route Planning
• Tree Labeling
• Shortcut Graph
• Batch Processing
• Path Reconstruction