Handbook of Mobility Data Mining, Volume 2: Mobility Analytics and Prediction - Softcover

Softcover

ISBN 10: 0443184240 ISBN 13: 9780443184246

Publisher: Elsevier, 2023

Synopsis

Handbook of Mobility Data Mining, Volume Two: Mobility Analytics and Prediction 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 introduces how to design MDM platforms that adapt to the evolving mobility environment and new types of transportation and users.

This helpful guide provides a basis for how to simulate and predict mobility data. After an introductory theory chapter, the book then covers crucial topics such as long-term mobility pattern analytics, mobility data generators, user information inference, Grid-based population density prediction, and more. The book concludes with a chapter on graph-based mobility data analytics. 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.

• Discusses how to efficiently simulate massive and large-scale people movement and predict mobility at an urban scale
• Introduces both online detection methods, which can sequentially process data, and offline detection methods, which are usually more robust
• Stems from the editor’s strong network of global transport authorities and transport companies, providing a solid knowledge structure and data foundation as well as geographical and stakeholder coverage

About this title

About the Author

Haoran (Ronan) Zhang is Assistant Professor in the Center for Spatial Information Science at the University of Tokyo, a Researcher at the School of Business Society and Engineering at Mälardalen University in Sweden, and Senior Scientist at Locationmind Inc. in Japan. His research includes smart supply chain technologies, GPS data in shared transportation, urban sustainable performance, GIS technologies in renewable energy systems, and smart cities. He is author of numerous journal articles and Editorial Board Member of several international academic journals. He has Ph.D.’s in both Engineering and Sociocultural Environment and was awarded Excellent Young Researcher by Japan’s Ministry of Education, Culture, Sports, Science and Technology.

From the Back Cover

The Handbook of Mobility Data Mining: Mobility Analytics and Prediction: Volume 2: Mobility Analytics and Prediction 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 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.

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.

Edition details

• Publisher: Elsevier
• Publication date: 2023
• Language: English
• ISBN 10: 0443184240
• ISBN 13: 9780443184246
• Binding: Paperback
• Edition number: 1
• Number of pages: 210
• Editor: Zhang