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 Kim; Peer 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

doi:10.1145/3652158

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

Location Intelligence

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

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 language	English
Article number	10
Journal	ACM Transactions on Spatial Algorithms and Systems
Volume	10
Issue number	2
DOIs	https://doi.org/10.1145/3652158
State	Published - Jul 1 2024

Funding

Mohamed F. Mokbel acknowledges the support of the National Science Foundation under grants nos. IIS-1907855 and IIS-2203553. Mahmoud Sakr acknowledges the support of the EU's Horizon Europe research and innovation program under grant agreement nos. 101070279 (MobiSpaces) and 101093051 (EMERALDS). Li Xiong acknowledges the support of the National Science Foundation under grant nos. CNS-2125530 and CNS-2041952. Andreas Z\u00F6fle and Taylor Anderson acknowledge the support of the National Science Foundation under grant no. DEB-2109647.Walid G. Aref acknowledges the support of the National Science Foundation under grant no. IIS-1910216. Gennady and Natalia Andrienko acknowledge the support of the Federal Ministry of Education and Research of Germany and the state of North-Rhine Westphalia as part of the Lamarr Institute for Machine Learning and Artificial Intelligence (Lamarr22B), and of the EU in projects SoBigData++ and CrexData (grant agreement no. 101092749). Reynold Cheng acknowledges the support of the Hong Kong Jockey Club Charities Trust (Project No. 260920140), the University of Hong Kong (Project No. 109000579), and the HKU Outstanding Research Student Supervisor Award 2022-23. Panos K. Chrysanthis acknowledges the support of the National Science Foundation under grant no. SES-2017614 and of National Institute of Health under grant no. R01HL159805. Anita Graser acknowledges the support of the EU's Horizon Europe research and innovation program under grant agreement nos. 101070279 (MobiSpaces) and 101093051 (EMERALDS). Matthias Renz acknowledges the support of the German Research Foundation under grant nos. 290391021 and 491008639, the Helmholtz School for Marine Data Science (MarDATA) partially funded by the Helmholtz Association (grant no. HIDSS-0005) and the Federal Ministry for Economic Affairs and Climate Action (BMWi) under grant no. 68GX21002E. Flora Salim acknowledges the support of the Australian Research Council (ARC) Centre of Excellence for Automated Decision-Making and Society (ADM+S) (grant no. CE200100005). Maxime Schoemans acknowledges the support of the Fund for Scientific Research (FNRS) under grant no. 40018132. Yannis Theodoridis acknowledges the support of the EU's Horizon Europe research and innovation program under grant agreement nos. 101070279 (MobiSpaces) and 101093051 (EMERALDS). Song Wu acknowledges the support of the EU's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement no. 955895 (DEDS). Jianqiu Xu acknowledges the support of the National Science Foundation under grant no. U23A20296.

Keywords

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

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10.1145/3652158

Cite this

Mokbel, M., Sakr, M., Xiong, L., Züfle, A., Almeida, J., Anderson, T., Aref, W., Andrienko, G., Andrienko, N., Cao, Y., Chawla, S., Cheng, R., Chrysanthis, P., Fei, X., Ghinita, G., Graser, A., Gunopulos, D., Jensen, C. S., Kim, J. S., ... Zimányi, E. (2024). Mobility Data Science: Perspectives and Challenges. ACM Transactions on Spatial Algorithms and Systems, 10(2), Article 10. https://doi.org/10.1145/3652158

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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.",

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Mokbel, M, Sakr, M, Xiong, L, Züfle, A, Almeida, J, Anderson, T, Aref, W, Andrienko, G, Andrienko, N, Cao, Y, Chawla, S, Cheng, R, Chrysanthis, P, Fei, X, Ghinita, G, Graser, A, Gunopulos, D, Jensen, CS, Kim, JS, Kim, KS, Kröger, P, Krumm, J, Lauer, J, Magdy, A, Nascimento, M, Ravada, S, Renz, M, Sacharidis, D, Salim, F, Sarwat, M, Schoemans, M, Shahabi, C, Speckmann, B, Tanin, E, Teng, X, Theodoridis, Y, Torp, K, Trajcevski, G, Van Kreveld, M, Wenk, C, Werner, M, Wong, R, Wu, S, Xu, J, Youssef, M, Zeinalipour, D, Zhang, M & Zimányi, E 2024, 'Mobility Data Science: Perspectives and Challenges', ACM Transactions on Spatial Algorithms and Systems, vol. 10, no. 2, 10. https://doi.org/10.1145/3652158

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T2 - Perspectives and Challenges

AU - Mokbel, Mohamed

AU - Sakr, Mahmoud

AU - Xiong, Li

AU - Züfle, Andreas

AU - Almeida, Jussara

AU - Anderson, Taylor

AU - Aref, Walid

AU - Andrienko, Gennady

AU - Andrienko, Natalia

AU - Cao, Yang

AU - Chawla, Sanjay

AU - Cheng, Reynold

AU - Chrysanthis, Panos

AU - Fei, Xiqi

AU - Ghinita, Gabriel

AU - Graser, Anita

AU - Gunopulos, Dimitrios

AU - Jensen, Christian S.

AU - Kim, Joon Seok

AU - Kim, Kyoung Sook

AU - Kröger, Peer

AU - Krumm, John

AU - Lauer, Johannes

AU - Magdy, Amr

AU - Nascimento, Mario

AU - Ravada, Siva

AU - Renz, Matthias

AU - Sacharidis, Dimitris

AU - Salim, Flora

AU - Sarwat, Mohamed

AU - Schoemans, Maxime

AU - Shahabi, Cyrus

AU - Speckmann, Bettina

AU - Tanin, Egemen

AU - Teng, Xu

AU - Theodoridis, Yannis

AU - Torp, Kristian

AU - Trajcevski, Goce

AU - Van Kreveld, Marc

AU - Wenk, Carola

AU - Werner, Martin

AU - Wong, Raymond

AU - Wu, Song

AU - Xu, Jianqiu

AU - Youssef, Moustafa

AU - Zeinalipour, Demetris

AU - Zhang, Mengxuan

AU - Zimányi, Esteban

PY - 2024/7/1

Y1 - 2024/7/1

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KW - Environmental impacts

KW - GPS data

KW - Geospatial intelligence

KW - Mobility Patterns

KW - Spatiotemporal data

KW - Urban Mobility

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JF - ACM Transactions on Spatial Algorithms and Systems

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M1 - 10

ER -

Mobility Data Science: Perspectives and Challenges

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