Title

Classifying injuries occurrence in motor vehicle collisions using artificial neural network.

SelectedWorks Author Profiles:

Leon Hardy

Document Type

Presentation

Publication Date

2011

Date Issued

January 2011

Date Available

August 2016

ISSN

1541-9312

Abstract

Vehicle collisions amount to a significant loss of life in America. This study used artificial neural networks as a means to predict the occurrence of injury of a vehicle collision. Using Neural Ware’s Predict software a neural network structure was trained, tested, and validated using data from the 2006 and 2007 Florida Traffic Crash Database. The objective was to assess whether or not properly designed neural network architecture could adequately classify the levels of the “Injury Occurrence” output variable, given certain inputs such as demographic and environmental factors involved in crashes. A Kolmogorov-Smirnov statistical analysis was employed to objectively assess whether or not the neural network properly classified the levels of Injury Occurrence and to what extent. The artificial network’s computational power was iteratively increased by adding hidden layers thus boosting its performance. A sensitivity analysis was used to find the level of contribution the input variables had on the “Injury Occurrence” output variable. Top three positive and negative most impacting factors were identified and the implications were discussed at the end of the paper.

Comments

Abstract only. Full-text article is available through licensed access provided by the publisher. Published in Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 55(1), 1808-1812. doi: 10.1177/1071181311551376. Members of the USF System may access the full-text of the article through the authenticated link provided.

Language

en_US

Publisher

Sage

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.