survey

A Survey on Deep Learning for Human Mobility

Authors:

Massimiliano Luca,

Gianni Barlacchi,

Bruno Lepri, and

Luca PappalardoAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 55, Issue 1

Article No.: 7, Pages 1 - 44

https://doi.org/10.1145/3485125

Published: 23 November 2021 Publication History

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.

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Cited By

Zhang YYu ZDang MXu EGuo BLiang YYin YZimmermann R(2024)Predictability in Human Mobility: From Individual to Collective (Vision Paper)ACM Transactions on Spatial Algorithms and Systems10.1145/365664010:2(1-17)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3656640
Mai GHuang WSun JSong SMishra DLiu NGao SLiu TCong GHu YCundy CLi ZZhu RLao N(2024)On the Opportunities and Challenges of Foundation Models for GeoAI (Vision Paper)ACM Transactions on Spatial Algorithms and Systems10.1145/365307010:2(1-46)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3653070
Mokbel MSakr MXiong LZüfle AAlmeida JAnderson TAref WAndrienko GAndrienko NCao YChawla SCheng RChrysanthis PFei XGhinita GGraser AGunopulos DJensen CKim JKim KKröger PKrumm JLauer JMagdy ANascimento MRavada SRenz MSacharidis DSalim FSarwat MSchoemans MShahabi CSpeckmann BTanin ETeng XTheodoridis YTorp KTrajcevski Gvan Kreveld MWenk CWerner MWong RWu SXu JYoussef MZeinalipour DZhang MZimányi E(2024)Mobility Data Science: Perspectives and ChallengesACM Transactions on Spatial Algorithms and Systems10.1145/365215810:2(1-35)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3652158
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Index Terms

A Survey on Deep Learning for Human Mobility
1. Applied computing
  1. Operations research
    1. Transportation
2. Computing methodologies
  1. Artificial intelligence
  2. Machine learning

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 1

January 2023

860 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3492451

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 23 November 2021

Accepted: 01 August 2021

Revised: 01 June 2021

Received: 01 December 2020

Published in CSUR Volume 55, Issue 1

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Zhang YYu ZDang MXu EGuo BLiang YYin YZimmermann R(2024)Predictability in Human Mobility: From Individual to Collective (Vision Paper)ACM Transactions on Spatial Algorithms and Systems10.1145/365664010:2(1-17)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3656640
Mai GHuang WSun JSong SMishra DLiu NGao SLiu TCong GHu YCundy CLi ZZhu RLao N(2024)On the Opportunities and Challenges of Foundation Models for GeoAI (Vision Paper)ACM Transactions on Spatial Algorithms and Systems10.1145/365307010:2(1-46)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3653070
Mokbel MSakr MXiong LZüfle AAlmeida JAnderson TAref WAndrienko GAndrienko NCao YChawla SCheng RChrysanthis PFei XGhinita GGraser AGunopulos DJensen CKim JKim KKröger PKrumm JLauer JMagdy ANascimento MRavada SRenz MSacharidis DSalim FSarwat MSchoemans MShahabi CSpeckmann BTanin ETeng XTheodoridis YTorp KTrajcevski Gvan Kreveld MWenk CWerner MWong RWu SXu JYoussef MZeinalipour DZhang MZimányi E(2024)Mobility Data Science: Perspectives and ChallengesACM Transactions on Spatial Algorithms and Systems10.1145/365215810:2(1-35)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3652158
Rong CLiu ZDing JLi Y(2024)Learning to Generate Temporal Origin-destination Flow Based-on Urban Regional Features and Traffic InformationACM Transactions on Knowledge Discovery from Data10.1145/364914118:6(1-17)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1145/3649141
Jiang FWang HLi Y(2024)VesNet: A Vessel Network for Jointly Learning Route Pattern and Future TrajectoryACM Transactions on Intelligent Systems and Technology10.1145/363937015:2(1-25)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3639370
Gecer MGarbinato B(2024)Federated Learning for Mobility ApplicationsACM Computing Surveys10.1145/363786856:5(1-28)Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1145/3637868
Tang CDong LGuo HWang XChen XDong QLiu Y(2024)Downscaling spatial interaction with socioeconomic attributesEPJ Data Science10.1140/epjds/s13688-024-00487-w13:1Online publication date: 5-Jul-2024
https://doi.org/10.1140/epjds/s13688-024-00487-w
Xiong GLi ZZhao MZhang YMiao QLv YWang F(2024)TrajSGAN: A Semantic-Guiding Adversarial Network for Urban Trajectory GenerationIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.323592311:2(1733-1743)Online publication date: Apr-2024
https://doi.org/10.1109/TCSS.2023.3235923
Xu SFu XPi DMa Z(2024)Inferring Individual Human Mobility From Sparse Check-in Data: A Temporal-Context-Aware ApproachIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.323160111:1(600-611)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2022.3231601
Anno STsubouchi KShimosaka M(2024)Forecasting Lifespan of Crowded Events With Acoustic Synthesis-Inspired Segmental Long Short-Term MemoryIEEE Access10.1109/ACCESS.2024.341750912(87309-87322)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3417509
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