Handbook of Mobility Data Mining, Volume 1: Data Preprocessing and Visualization

Handbook of Mobility Data Mining, Volume One: Data Preprocessing and Visualization provides a comprehensive understanding of mobile big data mining (MDM) with a bottom-up approach, covering technologies, AI methods, and applications in urban mobility. It explains how to preprocess mobile data, visualize urban mobility, simulate and predict human travel behavior, and assess mobility characteristics. The book is essential for researchers, engineers, operators, administrators, and policymakers seeking to understand current technologies and design MDM platforms that adapt to the evolving mobility environment.

Format: Paperback / softback
Length: 222 pages
Publication date: 01 January 2023
Publisher: Elsevier - Health Sciences Division

The Handbook of Mobility Data Mining, Volume One: Data Preprocessing and Visualization is a comprehensive guide that delves into the fundamental technologies, advanced AI methods, and upper-level applications of mobile big data mining (MDM). This book aims to provide readers with a comprehensive understanding of MDM from a bottom-up approach, enabling them to grasp the intricacies of this field.

In the first chapter, the book introduces the essential technologies of MDM, including mobile data collection, storage, processing, and analysis. It discusses the challenges associated with handling large-scale and diverse mobile data and highlights the importance of data preprocessing in extracting valuable insights. The chapter also introduces advanced AI methods such as machine learning, natural language processing, and computer vision, which are used in MDM to analyze and interpret mobile data.

The second chapter focuses on visualizing urban mobility. It discusses the use of data visualization techniques to represent and analyze mobility patterns, such as traffic congestion, pedestrian flows, and transportation modes. The chapter introduces various visualization tools and techniques, including heat maps, scatter plots, and interactive dashboards, that help researchers and policymakers understand the dynamics of urban mobility and identify areas for improvement.

The third chapter explores the simulation and prediction of human travel behavior. It discusses the use of statistical models, machine learning algorithms, and agent-based simulations to model and predict human mobility patterns. The chapter discusses the challenges of capturing and representing human behavior in mobility data and highlights the importance of incorporating contextual information and individual preferences into travel behavior models.

The fourth chapter examines the assessment of urban mobility characteristics and their matching performance as conditions and constraints in transport, emergency management, and sustainability development systems. It discusses the use of performance metrics, such as travel time, distance, and energy consumption, to evaluate the effectiveness of transportation systems and identify areas for improvement. The chapter also introduces the concept of matching algorithms, which are used to match transportation demand with supply in real-time.

The fifth chapter introduces the design of 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. It discusses the challenges of designing MDM platforms that are scalable, robust, and privacy-preserving. The chapter introduces the concept of federated learning, which enables the integration of data from multiple sources and the sharing of resources among different stakeholders.

The sixth chapter focuses on how to efficiently pre-process mobile big data to extract and utilize critical feature information of high-dimensional city people flow. It discusses the use of data cleaning, feature extraction, and dimensionality reduction techniques to transform raw mobile data into a format that is suitable for analysis. The chapter also introduces the concept of data augmentation, which is used to enhance the quality and diversity of mobility data.

The final chapter discusses privacy protection in mobile big data mining. It discusses the legal and ethical considerations associated with collecting, storing, and analyzing mobile data and highlights the importance of implementing privacy-preserving techniques such as anonymization, encryption, and differential privacy. The chapter also introduces the concept of privacy-by-design, which involves designing MDM platforms and algorithms with privacy considerations in mind from the beginning.

In conclusion, The Handbook of Mobility Data Mining, Volume One: Data Preprocessing and Visualization is a valuable resource for researchers, engineers, operators, administrators, and policymakers seeking greater understanding of current technologies, infra-knowledge structure, and limitations. This book provides a comprehensive guide to MDM, covering the fundamental technologies, advanced AI methods, and upper-level applications of this field. By providing a bottom-up approach, the book enables readers to grasp the intricacies of MDM and apply its principles to real-world scenarios. Whether you are interested in urban mobility, transportation planning, emergency management, or sustainability development, this book offers valuable insights and practical solutions to tackle the significant challenges faced by the MDM field.

Weight: 450g
Dimension: 229 x 152 (mm)
ISBN-13: 9780443184284