Prediction of Road Visibility Based on Meteorological Parameters by Machine Learning Methods

Abstract

One of the important parameters in ensuring traffic safety is road visibility. Road visibility depends on the geometric design of the road, lighting conditions, as well as the climatic conditions in the area where the road passes. Visibility depends on meteorological parameters such as temperature, humidity, wind speed, pressure, fog, precipitation type. In this study, it is aimed to predict road visibility to ensure traffic safety. Machine learning methods were used for road visibility estimation. Machine learning models were developed with Random Forest, Extra Tree and Gradient Boosting methods. In the models, 96453 meteorological data sets such as temperature, humidity, wind speed, pressure, precipitation types, visibility were used between 2006 and 2016 in Szeged, Hungary. Developed models were evaluated with coefficient of determination (R2) and Root mean squared error (RMSE). As a result of the evaluation, the random forest method gave the best result.