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Article: Mobility Data Science: Perspectives and Challenges

TitleMobility Data Science: Perspectives and Challenges
Authors
Mokbel, MohamedSakr, MahmoudXiong, LiZüfle, AndreasAlmeida, JussaraAnderson, TaylorAref, WalidAndrienko, GennadyAndrienko, NataliaCao, YangChawla, SanjayCheng, ReynoldChrysanthis, PanosFei, XiqiGhinita, GabrielGraser, AnitaGunopulos, DimitriosJensen, Christian S.Kim, Joon SeokKim, Kyoung SookKröger, PeerKrumm, JohnLauer, JohannesMagdy, AmrNascimento, MarioRavada, SivaRenz, MatthiasSacharidis, DimitrisSalim, FloraSarwat, MohamedSchoemans, MaximeShahabi, CyrusSpeckmann, BettinaTanin, EgemenTeng, XuTheodoridis, YannisTorp, KristianTrajcevski, GoceVan Kreveld, MarcWenk, CarolaWerner, MartinWong, RaymondWu, SongXu, JianqiuYoussef, MoustafaZeinalipour, DemetrisZhang, MengxuanZimányi, Esteban
KeywordsEnvironmental impacts
Geospatial intelligence
GPS data
Mobility Patterns
Spatiotemporal data
Urban Mobility
Issue Date1-Jul-2024
PublisherAssociation for Computing Machinery (ACM)
Citation
ACM Transactions on Spatial Algorithms and Systems, 2024, v. 10, n. 2 How to Cite?
DOI: http://dx.doi.org/10.1145/3652158
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.


Persistent Identifierhttp://hdl.handle.net/10722/366285
ISSN
2374-0353
2023 Impact Factor: 1.2
2023 SCImago Journal Rankings: 0.620

 

DC FieldValueLanguage
dc.contributor.authorMokbel, Mohamed-
dc.contributor.authorSakr, Mahmoud-
dc.contributor.authorXiong, Li-
dc.contributor.authorZüfle, Andreas-
dc.contributor.authorAlmeida, Jussara-
dc.contributor.authorAnderson, Taylor-
dc.contributor.authorAref, Walid-
dc.contributor.authorAndrienko, Gennady-
dc.contributor.authorAndrienko, Natalia-
dc.contributor.authorCao, Yang-
dc.contributor.authorChawla, Sanjay-
dc.contributor.authorCheng, Reynold-
dc.contributor.authorChrysanthis, Panos-
dc.contributor.authorFei, Xiqi-
dc.contributor.authorGhinita, Gabriel-
dc.contributor.authorGraser, Anita-
dc.contributor.authorGunopulos, Dimitrios-
dc.contributor.authorJensen, Christian S.-
dc.contributor.authorKim, Joon Seok-
dc.contributor.authorKim, Kyoung Sook-
dc.contributor.authorKröger, Peer-
dc.contributor.authorKrumm, John-
dc.contributor.authorLauer, Johannes-
dc.contributor.authorMagdy, Amr-
dc.contributor.authorNascimento, Mario-
dc.contributor.authorRavada, Siva-
dc.contributor.authorRenz, Matthias-
dc.contributor.authorSacharidis, Dimitris-
dc.contributor.authorSalim, Flora-
dc.contributor.authorSarwat, Mohamed-
dc.contributor.authorSchoemans, Maxime-
dc.contributor.authorShahabi, Cyrus-
dc.contributor.authorSpeckmann, Bettina-
dc.contributor.authorTanin, Egemen-
dc.contributor.authorTeng, Xu-
dc.contributor.authorTheodoridis, Yannis-
dc.contributor.authorTorp, Kristian-
dc.contributor.authorTrajcevski, Goce-
dc.contributor.authorVan Kreveld, Marc-
dc.contributor.authorWenk, Carola-
dc.contributor.authorWerner, Martin-
dc.contributor.authorWong, Raymond-
dc.contributor.authorWu, Song-
dc.contributor.authorXu, Jianqiu-
dc.contributor.authorYoussef, Moustafa-
dc.contributor.authorZeinalipour, Demetris-
dc.contributor.authorZhang, Mengxuan-
dc.contributor.authorZimányi, Esteban-
dc.date.accessioned2025-11-25T04:18:33Z-
dc.date.available2025-11-25T04:18:33Z-
dc.date.issued2024-07-01-
dc.identifier.citationACM Transactions on Spatial Algorithms and Systems, 2024, v. 10, n. 2-
dc.identifier.issn2374-0353-
dc.identifier.urihttp://hdl.handle.net/10722/366285-
dc.description.abstract<p>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.</p>-
dc.languageeng-
dc.publisherAssociation for Computing Machinery (ACM)-
dc.relation.ispartofACM Transactions on Spatial Algorithms and Systems-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectEnvironmental impacts-
dc.subjectGeospatial intelligence-
dc.subjectGPS data-
dc.subjectMobility Patterns-
dc.subjectSpatiotemporal data-
dc.subjectUrban Mobility-
dc.titleMobility Data Science: Perspectives and Challenges-
dc.typeArticle-
dc.identifier.doi10.1145/3652158-
dc.identifier.scopuseid_2-s2.0-85196966582-
dc.identifier.volume10-
dc.identifier.issue2-
dc.identifier.eissn2374-0361-
dc.identifier.issnl2374-0353-

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