Volume 1. Data preprocessing and visualization -- volume 2. Mobility analytics and prediction -- volume 3. Mobility data-driven applications.
"The Handbook of Mobility Data Mining introduces the fundamental technologies of mobile big data mining (MDM), advanced AI methods, and upper-level applications, helping readers comprehensively understand MDM with a bottom-up approach. It explains how to preprocess mobility big data, visualize urban mobility, simulate and predict human travel behavior, and assess urban mobility characteristics and their matching performance as conditions and constraints in transport, emergency management, and sustainability development systems. Further, it focuses on introducing how to design MDM platforms that adapt to the evolving mobility environment, new types of transportation, and users based on an integrated solution that utilizes sensing and communication capabilities to tackle significant challenges faced by the MDM field."--
"Volume 1: Data Preprocessing and Visualization focuses on how to efficiently preprocess mobility big data to extract and utilize critical feature information of high-dimensional city people flow. It first provides a conceptual theory and framework, then goes on to discuss data sources, trajectory map matching, noise filtering, trajectory data segmentation, data quality assessment, and more. It concludes with a chapter on mobility big data visualization."--
"Volume 2: Mobility Analytics and Prediction provides a basis for how to simulate and predict mobility data. After an introductory theory chapter, it then covers crucial topics such as long-term mobility pattern analytics, mobility data generators, user information inference, grid-based population density prediction, and more. It concludes with a chapter on graph-based mobility data analytics."--
"Volume 3: Mobility Data-driven Applications looks at various cases studies to illustrate and explore the methods introduced in the first two volumes. It begins with a set of chapters on Intelligent Transportation Management, using cases of bicycle-sharing, ride-hailing, travel time prediction, railway usage analysis, mobility data-driven service, and dynamic road pricing. It concludes with chapters on urban sustainability development, including road emission and living environment inequality analysis."--
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