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Factors Influencing Injury Severity in Alcohol-Related Crashes: A Neural Network Approach Using HSIS Crash Data

Product Type

Journal Article

Author

Snehanshu Banerjee and Nashid Khadem

Date

Jan-19

Full citation

Banerjee, S., & Khadem, N. K. (2019). Factors Influencing Injury Severity in Alcohol-Related Crashes: A Neural Network Approach Using HSIS Crash Data. ITE Journal, 89(3), 42–49.

Abstract

This study applies artificial neural networks to examine North Carolina motor vehicle crash data with the aim of identifying the factors influencing crash injury severity in alcohol-related crashes. A 3-Class neural network model and a binary (injury and non-injury) neural network model were developed using 49 variables with 63.58% and 68.22% validation accuracies respectively. Overturning was found to be the topmost factor influencing fatal and severe injuries in both the models while wearing a lap belt and deployment of airbags were found to be important factors contributing to a non-injury. Interestingly, the results suggest that driving a passenger car while intoxicated possibly leads to less severe injuries than driving a SUV under the same conditions. Driving in a residential area was found to be an important factor in a non-injury crash compared to driving in a commercial area. The accuracy of the 3-Class and binary models was improved using multiple neurons, and the optimum validation accuracies of 65.33% and 69.65% were obtained with the addition of six neurons in both the models

Available From

ITE Journal

Link To Journal Article

Link not available.


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HSIS Summaries

HSIS Summary Reports are two to eight pages in length and include a brief description of the issue addressed, data used, methodology applied, significant results, and practical implications.

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Research Reports

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