2023
Journal article
Open Access
A survey on deep learning for human mobility
Luca M., Barlacchi G., Lepri B., Pappalardo L.
Computer Science - Machine Learning Crowd flow prediction Social and Information Networks (cs.SI) General Computer Science Next-location prediction Trajectory generation I.2 Human mobility Deep learning Datasets Computer Science - Social and Information Networks FOS: Computer and information sciences Artificial Intelligence (cs.AI) Mobility flows Theoretical Computer Science Trajectory Artificial intelligence Machine Learning (cs.LG) Computer Science - Artificial Intelligence
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.
Source: ACM computing surveys 55 (2023). doi:10.1145/3485125
Publisher: Association for Computing Machinery,, New York, N.Y. , Stati Uniti d'America
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BibTeX entry
@article{oai:it.cnr:prodotti:477672,
title = {A survey on deep learning for human mobility},
author = {Luca M. and Barlacchi G. and Lepri B. and Pappalardo L.},
publisher = {Association for Computing Machinery,, New York, N.Y. , Stati Uniti d'America},
doi = {10.1145/3485125 and 10.48550/arxiv.2012.02825},
journal = {ACM computing surveys},
volume = {55},
year = {2023}
}CNR authorsLaboratories
0000-0002-1547-6007
SoBigData-PlusPlus
SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics
