A Survey on Deep Learning for Human Mobility

TL;DR

This survey comprehensively reviews deep learning methods applied to human mobility, covering tasks like location prediction and flow modeling, highlighting challenges and future directions in the field.

Contribution

It provides a detailed taxonomy of deep learning solutions for mobility tasks, addressing gaps in existing surveys that focus on limited aspects or traditional approaches.

Findings

Deep learning effectively improves next-location prediction.

Current models face challenges with data heterogeneity and scalability.

Future research should focus on multi-source data integration.

Abstract

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.