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

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung

1 Zitat (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.

OriginalspracheEnglisch
Aufsatznummer10
FachzeitschriftACM Transactions on Spatial Algorithms and Systems
Jahrgang10
Ausgabenummer2
DOIs
PublikationsstatusVeröffentlicht - 1 Juli 2024

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