Road Traffic Forecasting Using Air Pollution and Atmospheric Data

Urban traffic congestion continues to be a serious problem that compromises environmental sustainability, economic productivity, and public health. Accurately predicting patterns of congestion and implementing appropriate actions are essential for managing traffic flow. However, the influence of environmental elements such as weather and air pollution on traffic patterns is largely overlooked by standard traffic prediction algorithms.Predictive models that may better comprehend the intricate relationships between traffic patterns and environmental factors are developed by compiling historical data on air pollution levels and atmospheric conditions. Our trials show that this methodology outperforms previous methods in terms of traffic forecast accuracy. This study adds to our understanding of how traffic flow is influenced by environmental factors and provides useful information for improving urban transportation management techniques.