How to Better Predict the Effect of Urban Traffic and Weather on Air Pollution? Norwegian Evidence from Machine Learning Approaches

This paper uses machine learning approaches to predict the association between traffic volume, air pollution, and meteorological conditions. A key focus is on the interaction between these factors. The paper does this using hourly traffic volume, [[EQUATION]], and weather data for Oslo, Norway. I considered a total of ten datasets of the 2019 whole-year data to verify the prediction accuracy of the models. I find that the autoregressive integrated moving average model with exogenous input variables, and the autoregressive moving average dynamic linear model outperform the support vector machine and decision tree in predicting air pollution. At the same time, I also explored the effect of dividing the seasons and weather subsets on prediction accuracy. Finally, my study makes optimal policy recommendations for reducing air pollution from traffic volume, after considering the interaction and lagged effects of meteorology, time variables, traffic, and air pollution