Trajectory data combines the complexities of time series, spatial data, and (sometimes irrational) movement behavior. As data availability and computing power have increased, so has the popularity of deep learning from trajectory data. This review paper provides the first comprehensive overview of deep learning approaches for trajectory data. We have identified eight specific mobility use cases which we analyze with regards to the deep learning models and the training data used. Besides a comprehensive quantitative review of the literature since 2018, the main contribution of our work is the data-centric analysis of recent work in this field, placing it along the mobility data continuum which ranges from detailed dense trajectories of individual movers (quasi-continuous tracking data), to sparse trajectories (such as check-in data), and aggregated trajectories (crowd information).

翻译：轨迹数据融合了时间序列、空间数据以及（有时非理性的）移动行为的复杂性。随着数据可用性和计算能力的提升，基于轨迹数据的深度学习日益流行。本文首次全面综述了用于轨迹数据的深度学习方法。我们识别出八个特定的移动应用场景，并围绕所使用的深度学习模型及训练数据进行了分析。除对2018年以来的文献进行系统性定量综述外，本文的主要贡献在于对近期领域研究进行数据导向分析，将其沿移动数据连续谱（涵盖个体移动者的密集轨迹数据（准连续跟踪数据）、稀疏轨迹数据（如签到数据）以及聚合轨迹数据（群体信息））进行定位。