Volume 9, Number 1 (March 2011)                   IJCE 2011, 9(1): 41-48 | Back to browse issues page


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Rezaie Moghaddam F, Afandizadeh S, Ziyadi M. Prediction of accident severity using artificial neural networks . IJCE. 2011; 9 (1) :41-48
URL: http://ijce.iust.ac.ir/article-1-544-en.html

Abstract:   (7177 Views)

In spite of significant advances in highways safety, a lot of crashes in high severities still occur in highways. Investigation of influential factors on crashes enables engineers to carry out calculations in order to reduce crash severity. Therefore, this paper deals with the models to illustrate the simultaneous influence of human factors, road, vehicle, weather conditions and traffic features including traffic volume and flow speed on the crash severity in urban highways. This study uses a series of artificial neural networks to model and estimate crash severity and to identify significant crash-related factors in urban highways. Applying artificial neural networks in engineering science has been proved in recent years. It is capable to predict and present desired results in spite of limited data sets, which is the remarkable feature of the artificial neural networks models. Obtained results illustrate that the variables such as highway width, head-on collision, type of vehicle at fault, ignoring lateral clearance, following distance, inability to control the vehicle, violating the permissible velocity and deviation to left by drivers are most significant factors that increase crash severity in urban highways.

Full-Text [PDF 334 kb]   (3710 Downloads)    
Type of Study: Research Paper | Subject: Transportation

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