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Investigation on risk prediction of pedestrian head injury by real-world accidents

    Fan Li Affiliation
    ; Honggeng Li Affiliation
    ; Fuhao Mo Affiliation
    ; Sen Xiao Affiliation
    ; Zhi Xiao Affiliation

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

How to Cite
Li, F., Li, H., Mo, F., Xiao, S., & Xiao, Z. (2019). Investigation on risk prediction of pedestrian head injury by real-world accidents. Transport, 34(3), 394-403. https://doi.org/10.3846/transport.2019.10410
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Jun 11, 2019
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This work is licensed under a Creative Commons Attribution 4.0 International License.

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