Estimation of Gas Emission Values on Highways in Turkey with Machine Learning

Due to its geographical location, Turkey has been home to many civilizations for centuries. It has always acted as a bridge between west and east and will continue to do so. The development of road networks in Turkey and the difference in transportation methods are increasing the number of national and international traveling vehicles day by day. In this study, gas emission (CO 2 , CH 4 , N 2 O) value changes have been predicted according to vehicle types of vehicle mobility on highways using machine learning (Linear Regression, Bayesian Ridge, Random Forest Regressor, MLP Regressor, SVR) algorithms. Based on these results, the gas emission value and environmental impact that may occur in the future are estimated—each method evaluated with MAE, MSE, RMSE, and R2 statistical metrics. As a result, we obtain R square scores of 0.963231 for CO 2 , 0.9856 for CH 4 , and 0.982404 for N 2 O from the random forest regressor, random forest regressor, and MLP regressor, respectively.