Towards Mobility Data Science (Vision Paper)

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

doi:10.48550/ARXIV.2307.05717

Towards Mobility Data Science (Vision Paper)

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

Publikation: Working paper/Preprint › Preprint

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.

Originalsprog	Engelsk
Udgiver	arXiv
Antal sider	34
DOI	https://doi.org/10.48550/ARXIV.2307.05717
Status	Udgivet - 2023

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10.48550/ARXIV.2307.05717Licens: CC BY 4.0

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Mokbel, M. F., Sakr, M. A., Xiong, L., Züfle, A., Almeida, J. M., Anderson, T., Aref, W. G., Andrienko, G. L., Andrienko, N. V., Cao, Y., Chawla, S., Cheng, R., Chrysanthis, P. K., Fei, X., Ghinita, G., Graser, A., Gunopulos, D., Jensen, C. S., Kim, J.-S., ... Zimányi, E. (2023). Towards Mobility Data Science (Vision Paper). arXiv. https://doi.org/10.48550/ARXIV.2307.05717

@techreport{0c39c92d63e84bc0ab5da9d2ac7784a5,

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 = "Mokbel, {Mohamed F.} and Sakr, {Mahmoud Attia} and Li Xiong and Andreas Z{\"u}fle and Almeida, {Jussara M.} and Taylor Anderson and Aref, {Walid G.} and Andrienko, {Gennady L.} and Andrienko, {Natalia V.} and Yang Cao and Sanjay Chawla and Reynold Cheng and Chrysanthis, {Panos K.} and Xiqi Fei and Gabriel Ghinita and Anita Graser and Dimitrios Gunopulos and Jensen, {Christian S.} and Joon-Sook Kim and Kyoung-Sook Kim and Peer Kr{\"o}ger and John Krumm and Johannes Lauer and Amr Magdy and Nascimento, {Mario A.} and Siva Ravada and Matthias Renz and Dimitris Sacharidis and Cyrus Shahabi and Salim, {Flora D.} 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 Kreveld, {Marc J. van} and Carola Wenk and Martin Werner and Wong, {Raymond Chi-Wing} and Song Wu and Jianqiu Xu and Moustafa Youssef and Demetris Zeinalipour and Mengxuan Zhang and Esteban Zim{\'a}nyi",

year = "2023",

doi = "10.48550/ARXIV.2307.05717",

language = "English",

publisher = "arXiv",

type = "WorkingPaper",

institution = "arXiv",

}

Mokbel, MF, Sakr, MA, Xiong, L, Züfle, A, Almeida, JM, Anderson, T, Aref, WG, Andrienko, GL, Andrienko, NV, Cao, Y, Chawla, S, Cheng, R, Chrysanthis, PK, Fei, X, Ghinita, G, Graser, A, Gunopulos, D, Jensen, CS, Kim, J-S, Kim, K-S, Kröger, P, Krumm, J, Lauer, J, Magdy, A, Nascimento, MA, Ravada, S, Renz, M, Sacharidis, D, Shahabi, C, Salim, FD, Sarwat, M, Schoemans, M, Speckmann, B, Tanin, E, Teng, X, Theodoridis, Y, Torp, K, Trajcevski, G, Kreveld, MJV, Wenk, C, Werner, M, Wong, RC-W, Wu, S, Xu, J, Youssef, M, Zeinalipour, D, Zhang, M & Zimányi, E 2023 'Towards Mobility Data Science (Vision Paper)' arXiv. https://doi.org/10.48550/ARXIV.2307.05717

TY - UNPB

T1 - Towards Mobility Data Science (Vision Paper)

AU - Mokbel, Mohamed F.

AU - Sakr, Mahmoud Attia

AU - Xiong, Li

AU - Züfle, Andreas

AU - Almeida, Jussara M.

AU - Anderson, Taylor

AU - Aref, Walid G.

AU - Andrienko, Gennady L.

AU - Andrienko, Natalia V.

AU - Cao, Yang

AU - Chawla, Sanjay

AU - Cheng, Reynold

AU - Chrysanthis, Panos K.

AU - Fei, Xiqi

AU - Ghinita, Gabriel

AU - Graser, Anita

AU - Gunopulos, Dimitrios

AU - Jensen, Christian S.

AU - Kim, Joon-Sook

AU - Kim, Kyoung-Sook

AU - Kröger, Peer

AU - Krumm, John

AU - Lauer, Johannes

AU - Magdy, Amr

AU - Nascimento, Mario A.

AU - Ravada, Siva

AU - Renz, Matthias

AU - Sacharidis, Dimitris

AU - Shahabi, Cyrus

AU - Salim, Flora D.

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 - Kreveld, Marc J. van

AU - Wenk, Carola

AU - Werner, Martin

AU - Wong, Raymond Chi-Wing

AU - Wu, Song

AU - Xu, Jianqiu

AU - Youssef, Moustafa

AU - Zeinalipour, Demetris

AU - Zhang, Mengxuan

AU - Zimányi, Esteban

PY - 2023

Y1 - 2023

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.

U2 - 10.48550/ARXIV.2307.05717

DO - 10.48550/ARXIV.2307.05717

M3 - Preprint

BT - Towards Mobility Data Science (Vision Paper)

PB - arXiv

ER -

Towards Mobility Data Science (Vision Paper)

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