Analyzing mega-mobility systems in smart cities: A macro–micro integration with feedback paradigm empowered by artificial intelligence
Peer-Reviewed Publication
Updates every hour. Last Updated: 10-Jun-2026 07:16 ET (10-Jun-2026 11:16 GMT/UTC)
In this work, the authors 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 (AI) that enable MMIF, and critically analyze the enduring challenges and prospective research directions. As urban mobility systems increasingly serve as testbeds for complexity science, the MMIF paradigm using AI promises to reshape interdisciplinary collaboration, offering a blueprint for building intelligent, adaptive, and human-centric cities.
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