Road surface conditions forecasting in rainy weather using artificial neural networks

Abstract

In general, drivers should be cautious while driving on rainy days because of risk factors such as slippery road conditions or hydroplaning. This study aims to forecast road surface conditions on rainy days using artificial neural networks (ANNs) to provide drivers with useful information that might help prevent traffic accidents arising from poor road conditions. The proposed model forecasts road condition as belonging to one of three categories—hydroplaning, wet, or moist—based on the friction coefficient. The collected data set is divided into two data sets, training and test, and all data are normalized to range from 0 to 1. In addition, the friction coefficient is also normalized. When the normalized friction values are in range of 0.67–1.00, 0.34–0.66, and 0–0.33, the road condition is categorized as hydroplaning, wet, and moist, respectively. The accuracy of the model is verified using statistical parameters such as the Correlation ratio (Cr), Mean Squared Error (MSE), and Root Mean Square Error (RMSE). Furthermore, contingency tables are constituted to evaluate the performance of the forecasts. The Chi-squared test is used to analyze the tables; results showed statistically significant. The observations indicated that the proposed ANN method predicts the road condition with high accuracy for each factor, specifically, 92%, 100%, and 78% for hydroplaning, moist, and wet, respectively. Implementing this model would help reduce traffic accidents on rainy days by providing drivers with road condition information through on-board equipment using V2X technology.