Trajectory computing is a pivotal domain encompassing trajectory data management and mining, garnering widespread attention due to its crucial role in various practical applications such as location services, urban traffic, and public safety. Traditional methods, focusing on simplistic spatio-temporal features, face challenges of complex calculations, limited scalability, and inadequate adaptability to real-world complexities. In this paper, we present a comprehensive review of the development and recent advances in trajectory computing, from deep learning to the more recent large language models. We first define trajectory data and provide a brief overview of widely-used deep learning models. Systematically, we explore deep learning applications in trajectory management (pre-processing, storage, analysis, and visualization) and mining (trajectory-related forecasting, trajectory-related recommendation, trajectory classification, travel time estimation, anomaly detection, and mobility generation). Furthermore, we discuss emerging research directions and recent advancements in large models (represented by foundation models and large language models) for trajectory computing, which promise to reshape the next generation of trajectory computing. Additionally, we summarize application scenarios, public datasets, and toolkits. Finally, we outline current challenges in trajectory computing research and propose future directions. Relevant papers and open-source resources have been collated and are continuously updated at: https://github.com/yoshall/Awesome-Trajectory-Computing.

翻译：轨迹计算是涵盖轨迹数据管理与挖掘的关键领域，因其在位置服务、城市交通和公共安全等实际应用中的关键作用而受到广泛关注。传统方法侧重于简化的时空特征，面临着计算复杂、可扩展性有限以及对现实世界复杂性适应不足的挑战。本文全面回顾了从深度学习到近期大语言模型的轨迹计算发展与最新进展。我们首先定义了轨迹数据，并简要概述了广泛使用的深度学习模型。系统性地探讨了深度学习在轨迹管理（预处理、存储、分析与可视化）与挖掘（轨迹相关预测、轨迹相关推荐、轨迹分类、行程时间估计、异常检测与移动生成）中的应用。进一步讨论了以大模型（以基础模型和大语言模型为代表）在轨迹计算中的新兴研究方向与最新进展，这些进展有望重塑下一代轨迹计算。此外，我们总结了应用场景、公共数据集与工具包。最后，概述了当前轨迹计算研究面临的挑战并提出了未来方向。相关论文与开源资源已整理并持续更新于：https://github.com/yoshall/Awesome-Trajectory-Computing。