Presentation of artificial neural network models based on optimum theories for predicting accident severity on rural roads in Iran

Abstract

This study focuses on predicting the number and severity of rural accidents using nine accident-related variables by the multi-layer perceptron (MLP) approach as a type of artificial neural network modeling method. The models were developed using the input parameters of shoulder width, roadway width, roadside hazard, access density, passing zone ratio, speed limit, pavement condition index, shoulder rumble strips, and centerline rumble strips. MLP models were created based on accident data that occurred from 2019 to 2020 on the Tehran–Qom and Tehran-Saveh rural roads in Iran. In order to achieve the highest accuracy, twenty MLP models have been built with various structures. The MLP model, consisting of a multilayer feedforward network with hidden sigmoid and softmax output, has been used in this study. In this regard, an attempt was made to present an optimum theory to select the best model. The most accurate model was chosen based on the R-value and root mean square error (RMSE), mean absolute error (MAE), f, SD, and R2. Results indicated that the R-value obtained from the optimum model was 0.912, representing the accurate performance of the selected model. In addition, access density, roadside hazard, and roadway width were identified as the most significant variables.