STO: A Dynamic AIoT-Based Approach for Energy-Efficient Urban Traffic Management

Urban congestion and environmental pollution are pressing issues in urban sustainability. This study introduces the Streamlined Traffic Optimizer (STO), an AIoT-based system designed to improve urban traffic flow and reduce energy consumption. The STO algorithm dynamically adapts traffic signals using real-time data from the Uber Movement dataset. Initial results from simulations show a significant reduction in average travel time from 35 minutes to 28 minutes and an increase in average speed from 30 km/h to 36 km/h. Additionally, congestion levels dropped from 40% to 25%, while fuel consumption decreased by 18%, from 10,000 liters to 8,200 liters. These improvements are accompanied by a reduction in CO2 emissions from 1,200 to 950 tons per year. The STO system offers a scalable and flexible solution for cities aiming to reduce their environmental impact while optimizing traffic efficiency.