对比10种机器学习路网距离索引，评估其速度与精度权衡。
An Empirical Survey and Benchmark of Learned Distance Indexes for Road Networks
- 用7个真实路网数据集测试10种学习型距离索引
- 在查询延迟、存储和准确率上系统比较性能
- 开源代码支持复现，适合导航系统优化研究者
道路网络中最短路径距离计算是导航系统、位置服务与空间分析的核心操作。尽管经典算法（如Dijkstra）能给出精确结果，但其延迟在现代大规模实时应用中不可接受。过去二十年来，学者提出了多种距离索引以加速最短路径查询。近年来，随着机器学习发展，研究者设计了基于机器学习的近似距离索引，可高效响应查询。然而，对这些方法的系统性评估仍显不足。本文首次对道路网络中的学习型距离索引进行实证调查，从训练时间、查询延迟、存储开销和准确率四个维度进行评估。使用七个真实世界路网及基于轨迹数据生成的工作负载查询集，我们基准测试了十种代表性机器学习技术，并与强基线经典非学习方法对比，揭示关键洞察与实际权衡。我们发布了统一的开源代码库，以支持可复现性与未来研究。
原文摘要 · Abstract (English)
The calculation of shortest-path distances in road networks is a core operation in navigation systems, location-based services, and spatial analytics. Although classical algorithms, e.g., Dijkstra's algorithm, provide exact answers, their latency is prohibitive for modern real-time, large-scale deployments. Over the past two decades, numerous distance indexes have been proposed to speed up query processing for shortest distance queries. More recently, with the advancement in machine learning (ML), researchers have designed and proposed ML-based distance indexes to answer approximate shortest path and distance queries efficiently. However, a comprehensive and systematic evaluation of these ML-based approaches is lacking. This paper presents the first empirical survey of ML-based distance indexes on road networks, evaluating them along four key dimensions: Training time, query latency, storage, and accuracy. Using seven real-world road networks and workload-driven query datasets derived from trajectory data, we benchmark ten representative ML techniques and compare them against strong classical non-ML baselines, highlighting key insights and practical trade-offs. We release a unified open-source codebase to support reproducibility and future research on learned distance indexes.
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