The study of human mobility is crucial due to its impact on several aspects
of our society, such as disease spreading, urban planning, well-being,
pollution, and more. The proliferation of digital mobility data, such as phone
records, GPS traces, and social media posts, combined with the predictive power
of artificial intelligence, triggered the application of deep learning to human
mobility. Existing surveys focus on single tasks, data sources, mechanistic or
traditional machine learning approaches, while a comprehensive description of
deep learning solutions is missing. This survey provides a taxonomy of mobility
tasks, a discussion on the challenges related to each task and how deep
learning may overcome the limitations of traditional models, a description of
the most relevant solutions to the mobility tasks described above and the
relevant challenges for the future. Our survey is a guide to the leading deep
learning solutions to next-location prediction, crowd flow prediction,
trajectory generation, and flow generation. At the same time, it helps deep
learning scientists and practitioners understand the fundamental concepts and
the open challenges of the study of human mobility.

通过对人类活动的深入研究，结合大量的数字移动数据和人工智能的预测能力，运用深度学习模型，以以往的机械学习模型为基础，归纳整理的分类学，对多个任务（如下一位置预测、人流预测、轨迹和流预测）进行研究，是对人类移动的前沿研究。同时解决基础概念和现有挑战，方便深度学习科学家和实践者的理解。