Integrated Artificial Intelligence Frameworks for Adaptive Urban Traffic Rerouting and Signal Control in Sustainable Smart Cities

Abstract

Urban transportation systems have become one of the most complex socio-technical infrastructures in contemporary cities, shaped simultaneously by rapid urbanization, increasing vehicle ownership, environmental sustainability imperatives, and accelerating advances in artificial intelligence. Traditional traffic management paradigms, rooted in static control logic and deterministic modeling, have proven increasingly inadequate in addressing dynamic congestion patterns, heterogeneous driver behavior, and the multi-objective demands of safety, efficiency, and sustainability. In response, a new generation of intelligent traffic systems has emerged, integrating adaptive rerouting mechanisms, real-time driver monitoring, and learning-based signal control strategies. This research article develops a comprehensive and theoretically grounded examination of integrated AI-driven traffic rerouting and control frameworks, positioning them as a critical enabler of sustainable smart city mobility. Drawing strictly on established scholarly literature, the study synthesizes perspectives from traffic flow theory, reinforcement learning, neural network–based prediction, graph-based modeling, and embedded systems design to articulate a unified conceptual framework for adaptive traffic management. Particular emphasis is placed on the role of traffic-based vehicle rerouting and driver monitoring as foundational components of intelligent systems, as articulated in recent embedded systems research (Deshpande, 2025). The article advances a descriptive and interpretive methodological approach, critically analyzing how AI-driven decision-making reshapes traffic signal optimization, congestion mitigation, and energy efficiency outcomes in urban environments. Through extensive theoretical elaboration, the study examines the historical evolution of traffic control models, the epistemological shift toward data-driven intelligence, and the emerging debates surrounding algorithmic governance, scalability, and ethical deployment. The results are presented as an integrated narrative grounded in comparative literature analysis, demonstrating how adaptive rerouting combined with intelligent signal control can reduce systemic inefficiencies while supporting broader sustainability goals in smart cities (Macioszek et al., 2022). The discussion section provides an in-depth scholarly interrogation of competing viewpoints, limitations, and unresolved challenges, including model generalizability, real-time responsiveness, and human–machine interaction in traffic ecosystems. By articulating future research directions and policy implications, this article contributes a rigorous academic foundation for the design and evaluation of next-generation intelligent transportation systems that are resilient, energy-aware, and socially responsive.