Predicting nationwide road fatalities in the US: a neural network approach
by Gokhan Egilmez; Deborah McAvoy
International Journal of Metaheuristics (IJMHEUR), Vol. 6, No. 4, 2017

Abstract: Road crashes are among the top five leading causes of deaths in the US although the national trend in fatal crashes has reached to the lowest level since 1949. Therefore, this paper introduces a non-parametric prediction models, artificial neural network (ANN), to assist policy-makers in minimising fatal crashes across the United States. Seven input variables from four safety performance input domains while fatal crash was utilised as the single output variable for the scope of the research. ANN was utilised and the best neural network model was developed out of 1,000 networks. The proposed neural network model predicted data with 84% coefficient of determination. In addition, developed ANN model was benchmarked with a multiple linear regression model and outperformed in all performance metrics including r, R-square and the standard error of estimate.

Online publication date: Tue, 03-Oct-2017

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Metaheuristics (IJMHEUR):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email