Towards Mobility Data Science (Vision Paper)

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 Jensen; Joon-Sook Kim; Kyoung-Sook Kim; Peer Kröger; John Krumm; Johannes Lauer; Amr Magdy; Mario Nascimento; Siva Ravada; Matthias Renz; Dimitris Sacharidis; Cyrus Shahabi; Flora Salim; Mohamed Sarwat; Maxime Schoemans; 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

Towards Mobility Data Science (Vision Paper)

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 Jensen
, Joon-Sook Kim
, Kyoung-Sook Kim

Peer Kröger, John Krumm, Johannes Lauer, Amr Magdy, Mario Nascimento, Siva Ravada, Matthias Renz, Dimitris Sacharidis, Cyrus Shahabi, Flora Salim, Mohamed Sarwat, Maxime Schoemans, 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

Data Science & Artifical Intelligence

Purdue University
University of Minnesota Twin Cities
Université libre de Bruxelles
Emory University
Federal University of Minas Gerais
George Mason University
Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS
Kyoto University
Qatar Computing Research Institute
Chinese University of Hong Kong
University of Pittsburgh
University of Massachusetts Boston
National and Kapodistrian University of Athens
Aalborg University
Oak Ridge National Laboratory (ORNL)
National Institute of Advanced Industrial Science and Technology
Kiel University of Applied Sciences
University of Southern California
HERE Technologies
University of California at Riverside
Northeastern University
Oracle Corporation
University of New South Wales
Arizona State University
Eindhoven University of Technology
University of Melbourne
ESRI Inc.
University of Piraeus
Iowa State University
Utrecht University
Tulane University
Technical University of Munich (TUM)
Hong Kong University of Science and Technology
Nanjing University of Aeronautics and Astronautics
American University in Cairo
Alexandria University
University of Cyprus

Research output: Contribution to journal › Article

Abstract

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of 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 significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision 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
Pages (from-to)	1-34
Number of pages	34
Journal	arXiv
Volume	2023
Publication status	Published - 21 Jun 2023

Research Field

Former Research Field - Data Science

Access to Document

https://arxiv.org/abs/2307.05717

Cite this

@article{2835e6897d8244a5b707ecc6bdaf62cb,

title = "Towards Mobility Data Science (Vision Paper)",

abstract = "Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of 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 significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision 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.",

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 Christian Jensen and Joon-Sook Kim and Kyoung-Sook Kim 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 Cyrus Shahabi and Flora Salim and Mohamed Sarwat and Maxime Schoemans 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",

year = "2023",

month = jun,

day = "21",

language = "English",

volume = "2023",

pages = "1--34",

}

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, Kim, J-S, Kim, K-S, Kröger, P, Krumm, J, Lauer, J, Magdy, A, Nascimento, M, Ravada, S, Renz, M, Sacharidis, D, Shahabi, C, Salim, F, Sarwat, M, Schoemans, M, 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 2023, 'Towards Mobility Data Science (Vision Paper)', arXiv, vol. 2023, pp. 1-34. <https://arxiv.org/abs/2307.05717>

TY - JOUR

T1 - Towards Mobility Data Science (Vision Paper)

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

AU - Kim, Joon-Sook

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 - Shahabi, Cyrus

AU - Salim, Flora

AU - Sarwat, Mohamed

AU - Schoemans, Maxime

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 - 2023/6/21

Y1 - 2023/6/21

N2 - Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of 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 significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision 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 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 significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision 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.

M3 - Article

VL - 2023

SP - 1

EP - 34

JO - arXiv

JF - arXiv

ER -

Towards Mobility Data Science (Vision Paper)

Abstract

Research Field

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