Development of Artificial Intelligence Based Safety Performance Measures for Urban Roundabouts

Abstract

A reliable model for predicting crash frequency at roundabouts is an essential tool for evaluating the safety measures of a roundabout. This study developed a hybrid PSO-ANN model by optimizing the modeling parameters of the classical artificial neural network (ANN) model with the particle swarm optimization algorithm (PSO). The performance accuracy of the models was evaluated using the mean absolute error (MAE), root mean square error (RMSE), and determination coefficients (DC). The PSO-ANN model predicted the crash frequency with very good accuracy at the testing stage (DC = 0.7935). The hybrid model could improve the performance of the classical ANN model by up to 23.3% in the training stage and 16.9% in the testing stage. In addition to the statistical measures, graphical approaches (scatter and violin plots) were also used for evaluating the models’ accuracy. Both statistical and graphical evaluation techniques prove the reliability and accuracy of the proposed hybrid model in predicting the crash frequency at roundabouts.