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Handbook of Mobility Data Mining: Volume Three: 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. The book 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. The book contains crucial information for researchers, engineers, operators, administrators, and policymakers seeking greater understanding of current technologies' infra-knowledge structure and limitations.
The book introduces how to design MDM platforms that adapt to the evolving mobility environment―and 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. This third volume looks at various cases studies to illustrate and explore the methods introduced in the first two volumes, covering topics such as Intelligent Transportation Management, Smart Emergency Management and Urban Sustainability Development, covering bicycle and railway travel behavior, mobility inequality, and road and light pollution inequality.
The handbook includes three volumes:
Volume 3: Mobility Data-driven Applications looks at various case 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 inequity analysis