In fast-developing Indian cities, congestion is a significant concern, as travel times are much longer, the environment is negatively affected, and infrastructure is overstretched. Conventional traffic management systems tend to be ineffective at addressing such problems because they rely on fixed traffic lights and lack data inputs. This paper presents a next-generation smart traffic management system to address congestion in urban environments. The combination of multiple technical tools, such as IoT sensors, AI, ML models, and dynamic signal control, will allow traffic to move more freely in real time while optimizing vehicle routing. The goal of this proposed solution is to reduce traffic congestion, lower CO2 emissions, and enhance the commuter experience through IoT-based traffic monitoring, AI-driven predictive analytics, and dynamically controlled traffic signals. Traffic data from Indian metro cities will be used to validate the model's accuracy through simulations. The data analysis conducted on the test results has shown that the average travel times (p < 0.05), the rate of traffic congestion (p < 0.05), and total traffic emissions (p < 0.05) are all statistically significant reductions due to the use of the Smart Transportation Group Solution, which provides a means to reduce congestion and improve traffic. The major findings indicate significant decreases in mean travel time, congestion rates, and total traffic emissions, providing a prospective solution for managing urban mobility. The study will be valuable to the research because it offers a cost-effective, scalable solution to the specific issues faced by Indian cities. The future research focus will be on machine learning to improve and expand the capabilities of the system, including statistically reliable results from validated studies.
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