Development of Artificial Intelligence Based Safety Performance Measures for Urban Roundabouts

Fayez Alanazi (), Ibrahim Khalil Umar, Sadi Ibrahim Haruna, Mahmoud El-Kady and Abdelhalim Azam
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Fayez Alanazi: Civil Engineering Department, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia
Ibrahim Khalil Umar: Department of Civil Engineering, Kano State Polytechnic, Kano P.M.B 3401, Nigeria
Sadi Ibrahim Haruna: Department of Civil Engineering, Bayero University Kano, Kano P.M.B 3011, Nigeria
Mahmoud El-Kady: Civil Engineering Department, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia
Abdelhalim Azam: Civil Engineering Department, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia

Sustainability, 2023, vol. 15, issue 14, 1-17

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.

Keywords: roundabout; crash frequency; particle swarm optimization; input selection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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