Analyzing Mega-mobility Systems in Smart Cities: A Macro–Micro Integration with Feedback Paradigm Empowered by Artificial Intelligence

As pivotal drivers of smart cities, mega-mobility systems integrate large-scale transportation networks, communication nodes, and energy circuits into a coupled multinetwork system. Urban megasystems epitomize the grand challenge of “organized complexity”, exhibiting characteristic features such as adaptive openness, nonlinear dynamics, hierarchical organization, and emergent properties. Analytical investigations, constrained by the rigid separation of macro- and microlevel paradigms, struggle to capture the nonlinear interdependencies across levels that define mega-mobility systems. In this review, we systematically advance macro–micro integration with feedback (MMIF) as a transformative paradigm for analyzing urban mega-mobility systems, synthesizing the state-of-the-art developments in typical constituent subsystems under this unified perspective. The MMIF paradigm bridges the gap between theoretical abstraction and empirical practice, contributing to scientifically sound urban development by harmonizing emergent patterns with granular behavioral dynamics. Building upon this paradigm, we investigate the key methods and technologies empowered by artificial intelligence that enable MMIF and critically analyze the enduring challenges and prospective research directions. As urban mobility systems increasingly serve as test beds for complexity science, the MMIF paradigm using artificial intelligence promises to reshape interdisciplinary collaboration, offering a blueprint for building intelligent, adaptive, and human-centric cities.