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 dataharnessed from mobile devices, sensors, and participatory inputsenhances 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.

Deals with emerging crowdsensing technologies and models for intelligent urban planning applications Covers crowdsensing paradigms and practices in urban sustainability applications Introduces crowd-sourced data acquisition and analysis methods for urban planning and control

Autorentext

Dr. Saeid Pourroostaei Ardakani holds the position of Senior Lecturer (Associate Professor) in Computer Science at Queen Mary University of London. He earned his PhD in Computer Science from the University of Bath and has since held academic positions at the University of Lincoln (UoL), the University of Nottingham Ningbo China (UNNC), and Allameh Tabatabai University (ATU). Saeid also served as a core member of the MLearn Research Group and associated academic member of the Lincoln Centre for Autonomous Systems (UoL), founding member of the Next Generation Internet of Everything (NGIoE) Laboratory and active member of the Artificial Intelligence Optimisation Research Group (UNNC), and head of ICT (ATU). Saeid scholarly work is situated within Applied AI with a specific focus on architecting smart and adaptive computing and communication solutions for Internet of Things (IoT) and Cloud-based ecosystems. To date, he has published 3 academic books and over 100 scholarly articles in high-impact international journals and peer-reviewed conferences.

Ali CHESHMEHZANGI is a Professor and Head of the School of Architecture, Design and Planning (ADP) at The University of Queensland, Brisbane. He is member of the Sub-Committee for Games Legacy 2032 as well as the Olympics and Paralympics Expert Group. He has been in the World’s top 2 per cent field leader since 2022, recognised by Stanford University. He is among the top 25 global scholars in the urban sustainability research area. Ali has a wealth of experience, expertise, and a strong commitment to innovation and excellence in the fields of architecture, design, and planning, the three core disciplines of his School at UQ. With a career spanning over two decades, he has made significant contributions to the academic and professional communities, with a focus on sustainable and environmentally conscious design. So far, Ali has published over 500 journal papers, articles, conference papers, book chapters, and reports. He also has 43 other academic books, some of which have received awards at the national, provincial, and municipal levels. He is the creator and author of “Advanced City”, which is his main urban revolution contribution to the field. He also has received international awards and recognition for his research on urban resilience studies and sustainability research, as well as the 2018 Vice-Chancellor’s award for his impactful contribution to higher education.

Zusammenfassung

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 dataharnessed from mobile devices, sensors, and participatory inputsenhances 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.

Inhalt

Chapter 1. Urban Intelligence in Motion: Crowdsensing as a Catalyst for Change.- Chapter 2. Paradigms and Practices of Urban Crowdsensing: A Comprehensive Exploration in Urban Informatics.- Chapter 3. Land Surface Temperature Patterns and Private Car Mobility: A Crowdsensing-Based Spatial Study in Tehran.- Chapter 4. Crowdsensing Security and Privacy Preservation for Trusted Smart Cities.- Chapter 5. Fusion of Vehicular Communications, Crowdsensing and Autonomous Agents to Acquire Urban Intelligence.- Chapter 6. Global Navigation Satellite System (GNSS) and Internet of Things (IoT) Assisted Smart Parking Concept for Urban Areas.- Chapter 7. Leveraging Augmented Reality and Reinforcement Learning for Optimizing Evacuation in Crowded Public Spaces.- Chapter 8. The Role of Crowdsensing Technologies in Urban Digital Twins.- Chapter 9. Urban Intelligence in Action: Shaping the Pulse of the Data-Driven City.