Mobility Data Science: Perspectives and Challenges

Mohamed Mokbel, Mahmoud Sakr, Li Xiong, Andreas Züfle, Jussara Almeida, Taylor Anderson, Walid Aref, Gennady Andrienko, Natalia Andrienko, Yang Cao, Sanjay Chawla, Reynold Cheng, Panos Chrysanthis, Xiqi Fei, Gabriel Ghinita, Anita Graser, Dimitrios Gunopulos, Christian S. Jensen, Joon Seok Kim, Kyoung Sook KimPeer Kröger, John Krumm, Johannes Lauer, Amr Magdy, Mario Nascimento, Siva Ravada, Matthias Renz, Dimitris Sacharidis, Flora Salim, Mohamed Sarwat, Maxime Schoemans, Cyrus Shahabi, Bettina Speckmann, Egemen Tanin, Xu Teng, Yannis Theodoridis, Kristian Torp, Goce Trajcevski, Marc Van Kreveld, Carola Wenk, Martin Werner, Raymond Wong, Song Wu, Jianqiu Xu, Moustafa Youssef, Demetris Zeinalipour, Mengxuan Zhang, Esteban Zimányi

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of Global Positioning System (GPS)-equipped mobile devices and other inexpensive location-Tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated a significant impact in various domains, including traffic management, urban planning, and health sciences. In this article, we present the domain of mobility data science. Towards a unified approach to mobility data science, we present a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state-of-The-Art, and describe open challenges for the research community in the coming years.

Original languageEnglish
Article number10
Number of pages35
JournalACM Transactions on Spatial Algorithms and Systems
Volume10
Issue number2
DOIs
Publication statusPublished - 1 Jul 2024

Bibliographical note

Publisher Copyright:
© 2024 Copyright held by the owner/author(s).

Funding

FundersFunder number
Helmholtz School for Marine Data Science
Bundesministerium für Bildung und Forschung
Bundesministerium für Wirtschaft und Klimaschutz
Hong Kong Jockey Club Charities Trust260920140
Bundesministerium für Wirtschaft und Energie68GX21002E
Elon University101092749
Helmholtz AssociationHIDSS-0005
Deutsche Forschungsgemeinschaft290391021, 491008639
Fonds De La Recherche Scientifique - FNRS40018132
Australian Research CouncilCE200100005
University of Hong Kong2022-23, 109000579, SES-2017614
EU's Horizon Europe research and innovation programCNS-2041952, 101093051, 101070279, CNS-2125530, IIS-1910216, DEB-2109647
National Science FoundationIIS-2203553, IIS-1907855
EU's Horizon 2020 research and innovation program955895, U23A20296
Lamarr Institute for Machine Learning and Artificial IntelligenceLamarr22B
National Institutes of HealthR01HL159805

    Keywords

    • Environmental impacts
    • Geospatial intelligence
    • GPS data
    • Mobility Patterns
    • Spatiotemporal data
    • Urban Mobility

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