A Survey on Deep Learning for Human Mobility
By Massimiliano Luca, Gianni Barlacchi, Bruno Lepri, Luca Pappalardo. ACM Computing Surveys, 2020
324 citations recorded by the source corpus.
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.
Subfields: Building and Construction, Epidemiology, Transportation
View Paper: https://www.semanticscholar.org/paper/89f86a915245043e1ecd7a3c462945fdde171e7b
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Citations in the corpus
- A Deep Gravity model for mobility flows generation (2020, background): “The average CPC slightly decreases when tested on Bristol and Shef fi eld, while it does not change signi fi cantly with respect to DG on Leeds, Birmingham and Manchester.”