A Survey on Deep Learning for Human Mobility
This survey helps deep learning scientists and practitioners understand fundamental concepts and open challenges in human mobility, which is crucial for applications like urban planning and disease spreading.
This survey paper addresses the lack of a comprehensive description of deep learning solutions for human mobility tasks. It provides a taxonomy of mobility tasks, discusses challenges, and describes relevant deep learning solutions for next-location prediction, crowd flow prediction, trajectory generation, and flow generation.
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.