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 Jensen; Joon-Sook 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 Jensen, Joon-Sook 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

Data Science & Artifical Intelligence

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

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 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
Number of pages	35
Journal	ACM Transactions on Spatial Algorithms and Systems
Volume	10
Issue number	2
DOIs	https://doi.org/10.1145/3652158
Publication status	Published - 7 May 2024

Research Field

Multimodal Analytics

Keywords

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

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10.1145/3652158

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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, C., 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{c2a529775cf34bd194469e9f1569e350,

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

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 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",

year = "2024",

month = may,

day = "7",

doi = "10.1145/3652158",

language = "English",

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

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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, C, Kim, J-S, Kim, K-S, 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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T1 - Mobility Data Science: 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

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 - 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 - 2024/5/7

Y1 - 2024/5/7

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 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 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 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 - Geospatial intelligence

KW - Spatiotemporal data

KW - Environmental impacts

KW - GPS data

KW - Urban Mobility

KW - Mobility Patterns

U2 - 10.1145/3652158

DO - 10.1145/3652158

M3 - Article

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

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Research Field

Keywords

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