Description

This book examines the transformative role of crowd-sourced data and AI-driven analytics in redefining urban environments. This book presents a thorough investigation into how large-scale and real-time data--harnessed from mobile devices, sensors, and participatory inputs--enhances urban systems, including transportation, public safety, sustainability, and civic engagement. Through cutting-edge data analytics, machine learning techniques, and scalable system architectures, the book demonstrates how intelligent urban management is achieved.

This book integrates theoretical foundations with applied perspectives, offering a structured discussion around three central domains including Urban Crowdsensing Practices (i.e., concepts, models, and service patterns), Data Processing and Analytics (e.g., data fusion, machine learning, and intelligent frameworks), and Technological Enablers (such as mobile sensing, IoT, and cloud-based systems). Key Contributions of this book are:

1. Innovative Integration of Technologies: Demonstrates the convergence of crowdsensing, AI, big data analytics, and IoT to support intelligent and responsive urban decision making.

2. Practical Applications and Case Studies: Showcases real-world implementations of crowdsensing, highlighting its tangible impact on smart city development and urban innovation.

3. Interdisciplinary and Forward-Looking Perspectives: Bridges academia, industry, and policy, offering actionable insights for researchers, engineers, planners, and decision-makers.