How to better predict the effect of urban traffic and weather on air pollution? Norwegian evidence from machine learning approaches
使用挪威奥斯陆的每小时交通、空气污染物和天气数据，比较多种模型预测空气污染的准确性，发现传统时间序列模型优于机器学习模型，并基于交互和滞后效应提出减排政策建议。
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, NOx,PM2.5, and weather data for Oslo, Norway. I considered a total of six 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 machine learning models in predicting air pollution. At the same time, I also explored the effect of sampling 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.