Mobility Data Science: Perspectives and Challenges

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-Seok Kim, Kyoung-Sook Kim et al. (28 additional authors not shown)

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.