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.
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CITATION STYLE
Mokbel, M., Sakr, M., Xiong, L., Züfle, A., Almeida, J., Anderson, T., … Zimányi, E. (2024). Mobility Data Science: Perspectives and Challenges. ACM Transactions on Spatial Algorithms and Systems, 10(2). https://doi.org/10.1145/3652158
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