Predicting Injury Severity Levels in Traffic Crashes: A Modeling Comparison
Publication: Journal of Transportation Engineering
Volume 130, Issue 2
Abstract
This paper investigates the use of two well-known artificial neural network (ANN) paradigms: the multilayer perceptron (MLP) and fuzzy adaptive resonance theory (ART) neural networks in analyzing driver injury severity. The objective of this study is to investigate the viability and potential benefits of using the ANN in predicting driver injury severity conditioned on the premise that a crash has occurred. The performance of the ANN was compared to a calibrated ordered probit model. Modeling results showed that the testing classification accuracy was 73.5% for the MLP, 70.6% for the fuzzy ARTMAP, and 61.7% for the ordered probit model. This result indicates a more accurate prediction capability of injury severity for ANN (particularly the MLP) over other traditional methods. The results of the models showed that gender, vehicle speed, seat belt use, type of vehicle, point of impact, and area type (rural versus urban) affect the likelihood of injury severity levels.
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Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 130 • Issue 2 • March 2004
Pages: 204 - 210
Copyright
Copyright © 2004 American Society of Civil Engineers.
History
Received: Mar 18, 2002
Accepted: Apr 17, 2003
Published online: Feb 19, 2004
Published in print: Mar 2004
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