Spatio-temporal trajectory analytics is at the core of smart mobility solutions, which offers unprecedented information for diversified applications such as urban planning, infrastructure development, and vehicular networks. Trajectory similarity measure, which aims to evaluate the distance between two trajectories, is a fundamental functionality of trajectory analytics. In this paper, we propose a comprehensive survey that investigates all the most common and representative spatio-temporal trajectory measures. First, we provide an overview of spatio-temporal trajectory measures in terms of three hierarchical perspectives: Non-learning vs. Learning, Free Space vs. Road Network, and Standalone vs. Distributed. Next, we present an evaluation benchmark by designing five real-world transformation scenarios. Based on this benchmark, extensive experiments are conducted to study the effectiveness, robustness,nefficiency, and scalability of each measure, which offers guidelines for trajectory measure selection among multiple techniques and applications such as trajectory data mining, deep learning, and distributed processing.

翻译：时空轨迹分析是智能移动解决方案的核心，为城市规划、基础设施建设和车载网络等多样化应用提供了前所未有的信息。轨迹相似性度量旨在评估两条轨迹之间的距离，是轨迹分析的基础功能。本文提出了一项全面综述，研究了所有最常见且最具代表性的时空轨迹度量方法。首先，我们从三个层次视角对时空轨迹度量进行了概述：非学习型与学习型、自由空间与道路网络、独立式与分布式。接着，我们通过设计五种真实世界的转换场景，构建了一个评估基准。基于该基准，我们开展了大量实验，以研究每种度量的有效性、鲁棒性、效率和可扩展性，从而为轨迹数据挖掘、深度学习及分布式处理等多种技术与应用中的轨迹度量选择提供指导。