Abstract
Traffic accidents occur due to a combination of factors that lead to casualties and injuries. By identifying the most effective factors, it is possible for safety authorities to provide appropriate solutions for decreasing the accident severity and implementing preventive measures. The aim of this research was to present models to predict the accident severity on two-lane two-way (TLTW) rural highways in Iran over a one-year period from 2019 to 2020. Therefore, the occurrence probability of any type of accident was determined by artificial neural network (ANN)-based prediction models using nine independent variables affecting the accident severity. This study developed numerous ANN structures using back-propagation to model the potential nonlinear relationship between the accident severity and accident-related factors. Results indicated that among the models, the multilayer perceptron neural network (MLPNN) model with 6-2-2 partition had the best performance and prediction power. This model was developed using the standardized rescaling method for covariates and batch for training. Also, 9, 5, and 2 units were considered automatically for the input, hidden, and output layers, respectively, and the hyperbolic tangent and softmax were used as an activation function in the hidden and output layers, respectively. This model had the lowest cross-entropy error of 39.6 and the highest correct percentage of 82.5%, and the area under the receiver operating characteristic (ROC) curve was 0.852. Moreover, among all the effective variables, pavement condition index, roadside hazard, shoulder width, and passing zone ratio had the greatest impact on the accident severity. Finally, safety strategies were proposed to increase safety and reduce accidents along these roads.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.