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 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

Qatar Computing Research Institute

University of Minnesota Twin Cities
Université libre de Bruxelles
Ain Shams University
Emory University
Universidade Federal de Minas Gerais
Department of Computer Science
KD
Kyoto University
HKU
Department of Computer Science
George Mason University
Austrian Institute of Technology
National and Kapodistrian University of Athens
Aalborg University
Pacific Northwest National Laboratory
National Institute of Advanced Industrial Science and Technology
Kiel University
University of Southern California
HERE
University of California
Northeastern University
Oracle Corporation
University of New South Wales
Arizona State University
Eindhoven University of Technology
University of Melbourne
Iowa State University
University of Piraeus
Utrecht University
Tulane University School of Science and Engineering
Technical University of Munich
Hong Kong University of Science and Technology
Nanjing University of Aeronautics and Astronautics
American University in Cairo
Max Planck Institute for Informatics
University of Cyprus
Australian National University

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

24 Citations (Scopus)

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
Publication status	Published - 1 Jul 2024

Keywords

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

Access to Document

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

@article{3bba7b2837c8475ba04f66a9154230a2,

title = "Mobility Data Science: Perspectives and Challenges",

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.",

keywords = "Environmental impacts, GPS data, Geospatial intelligence, Mobility Patterns, Spatiotemporal data, Urban Mobility",

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

note = "Publisher Copyright: {\textcopyright} 2024 Copyright held by the owner/author(s).",

year = "2024",

month = jul,

day = "1",

doi = "10.1145/3652158",

language = "English",

volume = "10",

journal = "ACM Transactions on Spatial Algorithms and Systems",

issn = "2374-0353",

publisher = "Association for Computing Machinery",

number = "2",

}

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

TY - JOUR

T1 - Mobility Data Science

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

N2 - 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.

AB - 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.

KW - Environmental impacts

KW - GPS data

KW - Geospatial intelligence

KW - Mobility Patterns

KW - Spatiotemporal data

KW - Urban Mobility

UR - https://www.scopus.com/pages/publications/85196966582

U2 - 10.1145/3652158

DO - 10.1145/3652158

M3 - Article

AN - SCOPUS:85196966582

SN - 2374-0353

VL - 10

JO - ACM Transactions on Spatial Algorithms and Systems

JF - ACM Transactions on Spatial Algorithms and Systems

IS - 2

M1 - 10

ER -

Mobility Data Science: Perspectives and Challenges

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this