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

Area of Science:

• Complex Systems Science
• Urban Planning
• Network Science

Background:

• Mega-mobility systems are crucial for smart cities, integrating transportation, communication, and energy networks.
• These systems exhibit 'organized complexity' with features like nonlinearity and emergent properties.
• Traditional analysis struggles with the interdependencies between macro and micro levels in these systems.

Purpose of the Study:

• To systematically advance macro-micro integration with feedback (MMIF) as a paradigm for analyzing urban mega-mobility systems.
• To synthesize state-of-the-art developments within constituent subsystems using the MMIF perspective.
• To explore AI-driven methods enabling MMIF and identify future research directions.

Main Methods:

• Systematic review of existing literature on mega-mobility systems and complexity science.
• Conceptual framework development for macro-micro integration with feedback (MMIF).
• Analysis of artificial intelligence applications in enabling MMIF for urban mega-mobility.

Main Results:

• The MMIF paradigm offers a unified perspective to bridge theoretical and empirical gaps in mega-mobility analysis.
• AI technologies are identified as key enablers for implementing MMIF.
• The review highlights challenges and future research avenues for AI-powered MMIF.

Conclusions:

• MMIF provides a scientifically sound approach for urban development, harmonizing emergent patterns with granular dynamics.
• AI-empowered MMIF can reshape interdisciplinary collaboration in complexity science.
• This paradigm offers a blueprint for developing intelligent, adaptive, and human-centric smart cities.