Handbook of Mobility Data Mining, Volume 3: Mobility Data-Driven Applications

The Handbook of Mobility Data Mining: Volume 3: Mobility Data-driven Applications 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 mobile 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 the significant challenges that the MDM field faces.

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 dynamic road pricing, P2P bidding systems, bicycle-sharing systems, ride-sharing simulation, and customized bus systems. Part 2 then discusses Smart Emergency Management, detailing cases such as the Fukushima Earthquake, Hurricane Katrina, and COVID-19. Part 3 concludes with a set of chapters on Urban Sustainability Development, covering bicycle travel behavior, railway travel behavior, mobility inequality, and both road and light pollution inequality.

The information in this work is crucial for researchers, engineers, operators, company administrators, and policymakers in related fields, to comprehensively understand current technologies' infra-knowledge structure and limitations.