  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}.
