Full Description
Cities are engines of economic growth and innovation. To sustain growth in urban agglomerations, local governments need strategies to address disruptions caused by climate change, resource scarcity, and social inequity. Machine learning (ML) offers a powerful way for city planners to manage, predict, and mitigate these complex challenges. While urban planning has traditionally relied on techniques based on historical data and deterministic models, the dynamic and interconnected nature of contemporary challenges demands more sophisticated and AI-augmented approaches. ML algorithms ingest massive amounts of data to identify patterns and generate real-time predictions across multiple urban systems. ML models predict future patterns and assess vulnerabilities across infrastructure, mobility, and social systems.
This book examines how ML supports sustainable smart cities—cities prepared to address multiple challenges while prioritizing resident quality of life by maintaining the capacity to thrive economically and socially. It explores ways to use ML to enable smarter infrastructure and building design, from system-level networks to specific applications in construction and operations. Chapters demonstrate how ML optimizes urban mobility through improved public transportation, fleet management, and urban flow prediction. The book also examines ML applications in urban resilience, including flood risk management, air quality monitoring, and disaster response systems. Additionally, it explores innovative applications in social sustainability, such as affective computing for community well-being and equity-focused planning tools.
Featuring contributions from leading experts in architecture, urban planning, engineering, computer science, and sustainability, the book showcases real-world case studies of successful ML applications in cities worldwide. The resulting volume bridges theoretical developments with practical implementations, offering technical depth and actionable insights for creating sustainable urban futures.
Contents
Chapter 1: Sustainable Cities 2.0: An overview of the impact of machine learning on urbanity (Editorial Team).- Chapter 2: Advancing Urban Studies: Machine Learning Approaches for Urban Multi-Source Data Fusion (Zhu Tao, Xue Zheng, Jingxia Wang, Xiaofeng Zheng, John Calautit).- Chapter 3: Can Machine Learning Enable More Sustainable and Smarter Cities: Solutions, Challenges, and Best Practices (Bokolo Anthony Jnr).- Chapter 4: The Role of Machine Learning in Developing Sustainable and Resilient Interconnected Infrastructure Systems (Basem Alkhaleel).- Chapter 5: Machine Learning Applications in the Design of Infrastructures and Buildings (Abdulsalam Ibrahim Shema).- Chapter 6: An Intelligent NLP-Based Workflow for Automated Smart Building Design (Ebere Donatus Okonta).- Chapter 7: Machine Learning-Driven Predictions for Construction and Operational Project Metrics (Panagiotis Karadimos, Leonidas Anthopoulos).- Chapter 8: Ze-Com: A bottom-up low/zero energy consumption building stock modelling tool (Gül Nihal Güğül, Derek Baker, Ursula Eicker, Furkan Gökçül, Burak Behlül Ölmez, Mustafa Kuru, Kenan Geçer, Benan Dönmez).- Chapter 9: Machine Learning-Driven Retrofitting for Urban Microclimates: A Comparative Analysis of Interventions (Mohammad Hamdan).- Chapter 10: Optimizing Eco-Lodge Design in Switzerland for Sustainable Urban Futures (Ayat-Allah Bouramdane).- Chapter 11: Smart Public Transportation Through IoT: Addressing Scalability, Security, Infrastructure Integration and Environmental Impacts (Dillip Kumar Das, Mohamed Hassan Mostafa Hassan).- Chapter 12: Using Machine Learning to Integrate People's Preferences in Urban Mobility System Design: A Case Study of Cairo 2030 (Tjark Gall, Sebastian Hörl, Hazem Fahmi, Sherif Goubran, Nabil Mohareb).- Chapter 13: Leveraging Deep Learning for Predictive Urban Flow Management in Smart Cities (Rodrigo Simões, Adriano Lopes, Fernando Brito e Abreu).- Chapter 14: Autonomous Navigation Systems in GPS-denied Environments: A Review of Techniques and Applications (Saleh Alghamdi).- Chapter 15: ML-Based Dynamic Fleet Routing for Minimizing Greenhouse Gas Emissions (Jianyuan Peng, Roger J. Jiao, Fan Zhang).- Chapter 16: Sustainable Dynamic Strategies for Hybrid Urban Buses Operation Using Machine Learning (Ivan Jakus, Mia Kalaica, José Miguel Aragón-Jurado, Bernabé Dorronsoro, Patricia Ruiz).- Chapter 17: Machine Learning and Urban Sensors for Sustainable Flood Risk Management in Amman's Old Downtown (Zaid M. Al-Zrigat, Musab Wedyan).- Chapter 18: Innovative Approaches of AI-Driven Decision-Making for Sustainable Urban Disaster Response and Crisis Management (Fahim K Sufi).- Chapter 19: Early Detection of Urban Air Pollution in Smart Cities Through a Hybrid CNN-Transformer Model (Khosro Rezaee).- Chapter 20: Affective Urbanism: Negating Emotionally Negative Bottlenecks of our Intelligent Cities by ML (Fatma Farrag, Khaled Taher).- Chapter 21: Machine Learning for Smart, Sustainable, Green Cities: Advancing Inclusivity and Resilience (Vahid Javidroozi).- Chapter 22: Spatial Analysis and Machine Learning for Equitable Accessibility to Caregiving Facilities in Urban Contexts (Mariana Huskinson, Álvaro Bernabéu-Bautista, Francisco Gómez Donoso, Félix Escalona Moncholí, Leticia Serrano Estrada).- Chapter 23: Leveraging Machine Learning for Sustainable Urban Equity and Infrastructure Resilience: A Case Study of West Texas (Asma Mehan).- Chapter 24: TinyML in Urban Contexts: Opportunities and Challenges for Sustainable Cities in Developing Countries (Mostafa Abdelfattah Gouda).- Chapter 25: Transformative Technologies in Logistics: AI, Machine Learning, and Sustainable Practices (Khalid Z. Elwakeel).- Chapter 26: Future Trends and Policy Frameworks for Smart Cities 2.0 (Editorial Team).
