Road Traffic Forecasting using AirPollution and Atmospheric Data

In this paper, we propose a novel machine learning approach integrating environmental data into road traffic forecasting models. By leveraging historical data on road traffic, air pollution, and atmospheric parameters, we develop predictive models capable of capturing the intricate interplay between traffic patterns and environmental conditions. Our experimental results demonstrate the effectiveness of our approach in improving traffic forecast accuracy compared to conventional methods. This research contributes to advancing understanding of how environmental factors impact traffic dynamics, offering practical insights for enhancing urban transportation management strategies.