Estimation Model of Two-Lane Rural Roads Safety Index According to Characteristics of the Road and Drivers’ Behavior

Vehicle crashes are amongst the major causes of mortality and results in losses of lives and properties. Alarge number of the vehicle crashes occur on rural roads. Accidents become more noteworthy in twolaneroads due to going and coming traffic. Therefore, prediction of crashes and their causes are considerablyimportant to reduce the number and severity of the accidents. The safety index is a suitable quantity fordetermination of road safety degree. It informs us to study the number of accidents in a specific road andtime. In this study, safety index of twolane rural roads is predicted by Artificial Neural Network (ANN),Radial Basis Function Neural Networks (RBFNN) and Adaptive NeuroFuzzy Inference System (ANFIS)algorithms using MATLAB software. The number of causes which ends to an accident is related to someparameters. We chose seven new parameters as inputs to the ANN, RBFNN and ANFIS methods that aregeometric and statistical values of the roads and one output variable that is the safety index of segments oftwolane rural roads. 5 roads in Ilam Province, Iran, were selected for the case study to train, validate andtest the proposed estimation models. Finally, the results show that, it is possible to predict the safety index oftwolane rural roads with a high correlation coefficient and a low mean square error (MSE) in relation to realvalues. The ANN method has a higher correlation coefficient and lower MSE in comparison to RBFNN andANFIS methods. The achieved correlation coefficient and MSE for validation of the ANN approach are 0.94and 0.0086 respectively, and correlation coefficient of 0.845 and MSE of 0.019 for all data.