The rapid urbanization of global populations and the corresponding escalation of vehicular density have intensified long-standing challenges related to traffic congestion, road safety, environmental sustainability, and infrastructural efficiency. In response to these pressures, artificial intelligence has emerged as a transformative force within intelligent transportation systems, offering adaptive, data-driven mechanisms for traffic rerouting and driver monitoring. This research article develops a comprehensive, theoretically grounded, and critically reflective examination of AI-driven traffic-based vehicle rerouting and driver monitoring frameworks, situating them within broader historical, technological, legal, and socio-ethical contexts. Drawing extensively on interdisciplinary scholarship, the article synthesizes advances in reinforcement learning, predictive analytics, distributed control, contraflow systems, and AI-enabled sensing infrastructures to articulate an integrated conceptual architecture for modern traffic management. Central to this analysis is the incorporation of a comprehensive framework for traffic-based vehicle rerouting and driver monitoring, which serves as a unifying reference point for understanding how real-time traffic data, driver behavior analytics, and adaptive control strategies converge in practice (Deshpande, 2025). Rather than treating rerouting and monitoring as isolated technical functions, this study conceptualizes them as mutually reinforcing components of a socio-technical ecosystem shaped by governance structures, urban morphology, legal regimes, and public trust. The methodology adopts a qualitative, interpretive research design grounded in systematic literature synthesis and theoretical triangulation, enabling a nuanced exploration of both enabling mechanisms and structural constraints. The results section presents an integrative interpretation of how AI-driven approaches reshape traffic dynamics, decision-making processes, and sustainability outcomes, while the discussion critically interrogates competing scholarly perspectives, unresolved tensions, and future research trajectories. By foregrounding theoretical depth over instrumental efficiency, the article contributes a holistic academic perspective that advances the conceptual maturity of AI-driven traffic rerouting and driver monitoring research, offering implications for scholars, policymakers, and system designers alike (Zheng et al., 2021; Lukic Vujadinovic et al., 2024).ac

Artificial intelligence, intelligent transportation systems, traffic rerouting, driver monitoring

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