Trajectory similarity retrieval is an important part of spatiotemporal data mining, however, existing methods have the following limitations: traditional metrics are computationally expensive, while learning-based methods suffer from substantial training costs and potential instability. This paper addresses these problems by proposing \textbf{Geo}metric \textbf{P}rototype \textbf{T}rajectory \textbf{H}ashing (GeoPTH), a novel, lightweight, and non-learning framework for efficient category-based trajectory retrieval. GeoPTH constructs data-dependent hash functions by using representative trajectory prototypes, i.e., small point sets preserving geometric characteristics, as anchors. The hashing process is efficient, which involves mapping a new trajectory to its closest prototype via a robust, \textit{Hausdorff} metric. Extensive experiments show that GeoPTH's retrieval accuracy is highly competitive with both traditional metrics and state-of-the-art learning methods, and it significantly outperforms binary codes generated through simple binarization of the learned embeddings. Critically, GeoPTH consistently outperforms all competitors in terms of efficiency. Our work demonstrates that a lightweight, prototype-centric approach offers a practical and powerful alternative, achieving an exceptional retrieval performance and computational efficiency.

翻译：轨迹相似性检索是时空数据挖掘的重要组成部分，然而现有方法存在以下局限：传统度量方法计算成本高昂，而基于学习的方法则面临显著的训练开销和潜在的不稳定性。本文通过提出一种新颖、轻量且无需学习的框架——几何原型轨迹哈希（GeoPTH），以解决上述问题，实现高效的基于类别的轨迹检索。GeoPTH通过使用代表性轨迹原型（即保留几何特征的小型点集）作为锚点，构建数据依赖的哈希函数。哈希过程高效，涉及通过鲁棒的Hausdorff度量将新轨迹映射到其最近的原型。大量实验表明，GeoPTH的检索精度与传统度量方法及前沿学习方法相比具有高度竞争力，且显著优于通过简单二值化学习嵌入生成的二进制编码。关键的是，GeoPTH在效率方面持续优于所有竞争对手。我们的工作表明，这种以原型为中心的轻量级方法提供了一种实用且强大的替代方案，实现了卓越的检索性能和计算效率。