Prioritization of infrastructure factors affecting on the safety of two-lane roads using proactive and reactive methods (Case study: Ahar-Tabriz road)

Document Type : Research Article

Authors

1 Assistant Professor, Faculty of Civil Engineering, Ayatollah Boroujerdi University, Boroujerd, Iran

2 Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran

Abstract

Identifying effective infrastructure factors that contributed to accidents of two-lane roads based on proactive and reactive methods is one of the ways to increase road safety. Therefore, the present study is firstly aimed to identify, and prioritize factors affecting accidents on rural road of Ahar-Tabriz using artificial neural network (ANN), TOPSIS and multinomial logistic regression (MNL) models. AHP was used for the weighting of criteria and subcriteria in the TOPSIS model. Secondly, this study compares the results of priorities in three models based on proactive and reactive methods. The results of the ANN model indicated that this model predicts accident severity via 86%, in which prioritized factors such as horizontal and vertical curves, percentage of heavy vehicles, pavement condition, road drainage condition, the volume of passing cars and use of speed control cameras, respectively. However, the TOPSIS model has shown that the priority of the infrastructure factors affecting road safety includes drainage condition, pavement condition, use of speed control cameras, horizontal and vertical curves, road signs, percent of heavy cars, road lighting condition, the volume of passing cars, and traffic calming, respectively. In addition, the MNL model indicated that this model is capable of prediction in accident severity via 74.82% in which pavement condition, use of speed control cameras, road lighting condition, the vehicle of passing cars, and road signs are ranked as the most effective factors involved with accidents on rural roads, respectively. Therefore, by making a performance comparison based on Spearman's rank correlation coefficient, T-test analysis, it is found that there is no difference between ANN and MNL models; however, there is a significant difference between TOPSIS and other models. Thus, ANN and MNL models are reactive methods, and the TOPSIS model is a proactive method in which the ANN model, due to higher accident severity prediction, is selected as the best predictive model. 

Keywords

Main Subjects

References

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Amirkabir Journal of Civil Engineering
Volume 54, Issue 4
July 2022
Pages 1419-1440

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