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

The Handbook of Mobility Data Mining: Volume Three: Mobility Data-Driven Applications provides an in-depth understanding of mobile big data mining (MDM) technologies, advanced AI methods, and upper-level applications. It covers preprocessing, visualization, behavior simulation, and assessment in transport, emergency management, and sustainability development. The book offers insights for researchers, engineers, operators, administrators, and policymakers, highlighting the importance of designing MDM platforms that adapt to the evolving mobility environment.

Format: Paperback / softback
Length: 242 pages
Publication date: 01 January 2023
Publisher: Elsevier - Health Sciences Division

The Handbook of Mobility Data Mining: Volume Three: Mobility Data-Driven Applications 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 explores the preprocessing of mobile big data, a crucial step in extracting valuable insights. It discusses techniques such as data cleansing, feature extraction, and data transformation, which are essential for preparing mobile data for analysis. The chapter also introduces visualization tools that can help researchers and practitioners understand the patterns and trends in mobility data.

The second chapter focuses on urban mobility, a complex and rapidly evolving domain. It discusses the challenges of capturing and analyzing mobility data in urban environments, including the use of sensors, GPS tracking, and mobile phone data. The chapter also explores the use of machine learning algorithms to simulate and predict human travel behavior, which can be valuable for urban planning, traffic management, and public transportation systems.

The third chapter delves into the realm of human travel behavior simulation and prediction. It discusses various AI methods, such as regression analysis, decision trees, and neural networks, that can be used to model and analyze human behavior. The chapter also explores the use of big data analytics to identify patterns and trends in travel behavior, which can inform transportation planning and policy decisions.

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 and evaluation frameworks to measure the effectiveness of mobility solutions and identify areas for improvement. The chapter also highlights the role of MDM in developing smart cities and sustainable transportation systems.

In the fifth chapter, the book introduces the concept of designing MDM platforms that can adapt to the evolving mobility environment. It discusses the use of integrated solutions that utilize sensing and communication capabilities to tackle significant challenges faced by the MDM field. The chapter also explores the use of cloud computing and big data technologies to facilitate the storage, processing, and analysis of large-scale mobility data.

The sixth chapter presents various case studies that illustrate and explore the methods introduced in the first two volumes. The case studies cover topics such as Intelligent Transportation Management, Smart Emergency Management, and Urban Sustainability Development. Each case study provides real-world examples of how MDM has been applied to address specific mobility challenges and improve transportation systems.

The seventh chapter discusses the challenges and limitations of MDM. It discusses the ethical considerations surrounding the collection and use of mobility data, as well as the potential risks and biases associated with AI-driven analysis. The chapter also highlights the need for interdisciplinary collaboration and the integration of different stakeholders in the development and implementation of MDM solutions.

In conclusion, The Handbook
Handbook of Mobility Data Mining: Volume Three: Mobility Data-Driven Applications is a valuable resource for researchers, engineers, operators, administrators, and policymakers seeking to gain a deeper understanding of current technologies, infra-knowledge structure, and limitations in the field of mobility data mining. This book provides a comprehensive guide that covers the fundamental technologies, advanced AI methods, and upper-level applications of MDM, enabling readers to develop a comprehensive understanding of this rapidly evolving field from a bottom-up approach.

Weight: 450g
Dimension: 229 x 152 (mm)
ISBN-13: 9780323958929