Citation

J. Transp. Res. (Seoul) 2017; 24(1): 1-15.

Copyright

(Copyright © 2017, Korea Transport Institute)

DOI

unavailable

PMID

unavailable

Abstract

An automated collision notification system installed in vehicles could inform emergency response teams of the crash and its' severity. Tracking information and recognition of the extent of injuries would assist in prompt arrival of appropriate teams to the crash site and transfer to corresponding medical facility. Traffic collision injury severity is dependent on crash attributes, driver characteristics, and usage of seat belts. Noting injures differ according to principal direction of force, the probabilities of severe injury for eight body regions are estimated by logistic regression analysis. NASS-CDS data including crash attributes and medical records are used for logistic regression model estimation. The effect of injury severity varies with principal direction of force, body region, and gender. Accuracy of injury severity predictions are based on a model. Such a model incorporated in the Automatic Crash Notification System can produce injury information related to crash attribute data assisting in emergency response.

Language: ko

Keywords

Automatic Crash Notification; Delta V; Driver Injury Severity; Logistic Regression; PDOF(Principal Direction of Force)