%0 Journal Article
	%A Omer F. Cansiz and  Said M. Easa
	%D 2011
	%J International Journal of Civil and Environmental Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 58, 2011
	%T Using Artificial Neural Network to Predict Collisions on Horizontal Tangents of 3D Two-Lane Highways
	%U https://publications.waset.org/pdf/2458
	%V 58
	%X The purpose of this study is mainly to predict collision
frequency on the horizontal tangents combined with vertical curves
using artificial neural network methods. The proposed ANN models
are compared with existing regression models. First, the variables
that affect collision frequency were investigated. It was found that
only the annual average daily traffic, section length, access density,
the rate of vertical curvature, smaller curve radius before and after
the tangent were statistically significant according to related
combinations. Second, three statistical models (negative binomial,
zero inflated Poisson and zero inflated negative binomial) were
developed using the significant variables for three alignment
combinations. Third, ANN models are developed by applying the
same variables for each combination. The results clearly show that
the ANN models have the lowest mean square error value than those
of the statistical models. Similarly, the AIC values of the ANN
models are smaller to those of the regression models for all the
combinations. Consequently, the ANN models have better statistical
performances than statistical models for estimating collision
frequency. The ANN models presented in this paper are
recommended for evaluating the safety impacts 3D alignment
elements on horizontal tangents.
	%P 491 - 500