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 deep learning for trajectory computing (DL4Traj). 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). Notably, we encapsulate recent advancements in Large Language Models (LLMs) that hold the potential to augment trajectory computing. Additionally, we summarize application scenarios, public datasets, and toolkits. Finally, we outline current challenges in DL4Traj research and propose future directions. Relevant papers and open-source resources have been collated and are continuously updated at: \href{https://github.com/yoshall/Awesome-Trajectory-Computing}{DL4Traj Repo}.

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各类实际应用中的重要作用而受到广泛关注。传统方法侧重于简单的时空特征，面临计算复杂、可扩展性有限以及难以适应现实世界复杂性的挑战。本文全面综述了深度学习在轨迹计算（DL4Traj）中的发展历程与最新进展。我们首先定义轨迹数据，并简要概述广泛使用的深度学习模型。系统性地，我们探讨了深度学习在轨迹管理（预处理、存储、分析与可视化）和轨迹挖掘（轨迹相关预测、轨迹相关推荐、轨迹分类、旅行时间估计、异常检测和移动性生成）中的应用。特别地，我们总结了最近在大型语言模型（LLMs）方面的进展，这些进展有潜力增强轨迹计算。此外，我们总结了应用场景、公共数据集和工具包。最后，我们概述了当前DL4Traj研究中的挑战，并提出了未来方向。相关论文和开源资源已整理并持续更新于：\href{https://github.com/yoshall/Awesome-Trajectory-Computing}{DL4Traj仓库}。