Energy-Efficient Urban Transportation Planning using Traffic Flow Optimization

Abstract

This study examines how predictive analytics and the IoT might improve sustainable urban transportation systems. Using IoT device data, this study will explore how predictive analytics and IoT integration alter urban transportation. The data covers vehicle speed, traffic density, AQI, and weather. The research estimates traffic congestion, AQI, and volume using predictive modeling. This assesses prediction accuracy and data match. Unfavorable weather increases congestion, whereas traffic density decreases vehicle speed. Predictive methods accurately estimate congestion and air quality, but traffic volume is more difficult. The algorithms' accuracy in anticipating congestion and AQI is confirmed by comparing predicted and actual outcomes. Despite a 1.4% traffic flow increase, predictive analytics and IoT solutions reduce congestion by 25% and improve air quality by 12.7%. The impact research shows that these methods reduce congestion and promote sustainability. This research highlights the potential of predictive analytics and IoT to improve urban mobility, enable smarter decision-making, and create sustainable urban environments via data-driven insights and proactive actions.