AI-Based Computational Mathematical Models for Smart Cities

Smart cities have become the most demanding technical challenge of the twenty-first century, cramming multimodal transportation, distributed energy, interdependent public services, and shifting social dynamics into the same operational space. Traditional urban planning, built on static models and isolated indicators, simply wasn't designed for this kind of complexity. Cities today behave nonlinearly, interact across multiple scales at once, and resist the assumptions of stability that older planning tools depend on. What is needed instead are models that combine deep neural networks, reinforcement learning, and optimization techniques with an awareness of urban structure itself, so that solutions that look optimal on paper don't quietly fail once embedded in the real, interdependent systems of a living city. The underlying goal is to build urban systems that can learn, adapt, and be continuously validated as conditions change. AI-Based Computational Mathematical Models for Smart Cities: Generation, Simulation, and Decision Systems bridges rigorous computational mathematics with operational smart-city practice by providing a reproducible, end-to-end framework for designing, implementing, and evaluating urban models. By connecting deep learning, reinforcement learning, fuzzy control, Bayesian modeling, and metaheuristic optimization with structural and topology-aware criteria that guard against brittle solutions, this book provides valuable insights into building smart-city models that integrate prediction, simulation, explanation, and continuous validation within a single computational workflow. Covering topics such as decision intelligence, labor dynamics, and urban governance, this book is an indispensable academic resource for graduate and doctoral students, urban planners, infrastructure engineers, data scientists, control systems engineers, policymakers, and more.