Real-time traffic data

Abstract

Motor vehicles significantly contribute to the escalating levels of air and noise pollution in urban centers worldwide. Numerous studies have established a strong correlation between vehicle exhaust emissions, noise levels, and various factors such as traffic flow rate, vehicle composition, fleet speed, as well as deceleration and acceleration speeds. This research monitors ambient air quality and noise levels in diverse city centers during peak hours, shedding light on the impact of vehicular activities. The study investigates into the intricate relationship between vehicular composition and the concentration of particulate matter (PM). Furthermore, it conducts a comprehensive analysis of how traffic composition influences roadside noise pollution, identifying key factors contributing to this environmental concern. Employing an efficient deep learning process, the research employs image detection and tracking of vehicles to enhance understanding. Additionally, various machine learning tools are applied for the prediction of traffic-related air and noise pollution. This research makes a significant contribution to sustainable transportation planning, offering valuable insights into the complex dynamics of vehicular impact on urban environments. The findings not only enhance our understanding of pollution sources but also pave the way for informed decision-making in developing strategies to mitigate the adverse effects of motor vehicle activities.