Formal methods for modeling, analyzing, and dynamically optimizing road traffic

This study builds on existing work in traffic flow modeling, optimization techniques, and Intelligent Transportation Systems (ITS). Research on Lagrange optimization methods and real-time adaptive algorithms has been foundational, as has prior work integrating ITS into urban traffic networks. Related studies in SCRD Journal and SCIC Proceedings highlight the growing importance of dynamic traffic management systems for sustainable urban planning. This research extends these concepts by addressing gaps in real-time adaptability and environmental optimization. The study employs a hybrid methodology that includes simulation-based traffic flow models, algorithmic optimization via Lagrange methods, and real-world data integration from ITS. A combination of case studies and empirical analysis evaluates the efficacy of the proposed models. Simulations incorporate dynamic variables such as traffic density, road network complexity, and environmental metrics to ensure comprehensive assessment. The findings demonstrate significant improvements in traffic flow efficiency, with reductions in congestion levels and CO2 emissions by up to 20% under simulated conditions. The proposed models also enhance adaptive traffic management, showing real-time responsiveness to fluctuations in traffic patterns. This research provides valuable insights for academics, transportation planners, and policymakers. For researchers, it offers a framework for integrating formal methods with ITS technologies. For practitioners, it outlines practical strategies to implement environmentally sustainable traffic solutions. The study’s key contribution lies in its integration of formal methods with dynamic traffic optimization, offering a unique and practical approach to addressing urban traffic challenges. The originality of this research ensures its relevance to advancing sustainable and intelligent transportation systems.