@article{ref1,
title="Investigation on risk prediction of pedestrian head injury by real-world accidents",
journal="Transport",
year="2019",
author="Li, Fan and Li, Honggeng and Mo, Fuhao and Xiao, Sen and Xiao, Zhi",
volume="34",
number="3",
pages="394-403",
abstract="Head injury is the most common and fatal injury in car-pedestrian accidents. Due to the lack of human test data, real-world accident data is useful for the research on the mechanism and tolerance of head injuries. The objective of the present work is to investigate pedestrian head-brain injuries through real car-pedestrian accidents and evaluate the existed injury criteria. Seven car-to-pedestrian accidents in China were selected from the IVAC (Investigation of Vehicle Accident in Changsha) database. Accident reconstructions using multi-body models were conducted to determine the kinematic parameters associated with the injury and were used to measure head injury criteria. Kinematic parameters were input into a finite element model to run simulations on the head-brain and car interface to determine levels of brain tissue stress, strain, and brain tissue injury criteria. A binary logistic regression model was used to determine the probability of head injury risk associated with AIS3+ injuries (Abbreviated Injury Scale). The results showed that head injury criteria using kinematic parameters can effectively predict injury risk of a pedestrians' head skull. Regarding brain injuries, physical parameters like coup/countercoup pressure are more effective predictors. The results of this study can be used as the background knowledge for pedestrian friendly car design.   Keyword : head injury, traffic accident, pedestrian, injury criteria, logistic regression<p /> <p>Language: en</p>",
language="en",
issn="1648-4142",
doi="10.3846/transport.2019.10410",
url="http://dx.doi.org/10.3846/transport.2019.10410"
}