Smart Mobility and Cities 2.0: Advancing Urban Transportation Planning Through Artificial Intelligence and Machine Learning

Urban mobility systems worldwide are buckling under unprecedented strains from rising urbanization, motorization, and climate imperatives. As legacy transportation paradigms falter, emerging data-driven solutions offer renewed hope. This pioneering research elucidates the transformational potential of artificial intelligence and machine learning (AI/ML) to enable intelligent mobility ecosystems through a robust mixed-methods approach. By synergizing insights from an exhaustive literature review encompassing cutting-edge technical scholarship and comparative case study analysis of pioneering global implementations, this work comprehensively investigates AI/ML applications across the urban mobility spectrum. The findings unveil profound enhancements in sustainability, efficiency, and equity outcomes achieved by leading cities on the vanguard of mobility innovation. However, equitably extending these capabilities remains contingent on purposeful governance, ethics, and social justice frameworks. This agenda-setting research crystallizes integral insights, lessons, and policy pathways to responsibly harness AI/ML's immense potential in service of accessible, green, and livable transportation futures for all. The knowledge generated propels both scholarship and practice at the nexus of technological capabilities and the public good.