Citation

Niebuhr T, Junge M, Rosen E. Accid. Anal. Prev. 2015; 86: 121-128.

Affiliation

Autoliv Research, Sweden.

Copyright

(Copyright © 2015, Elsevier Publishing)

DOI

10.1016/j.aap.2015.10.026

PMID

26547018

Abstract

Older adults and pedestrians both represent especially vulnerable groups in traffic. In the literature, hazards are usually described by the corresponding injury risks of a collision. This paper investigates the MAIS3+F risk (the risk of sustaining at least one injury of AIS 3 severity or higher, or fatal injury) for pedestrians in full-frontal pedestrian-to-passenger car collisions. Using some assumptions, a model-based approach to injury risk, allowing for the specification of individual injury risk parameters for individuals, is presented. To balance model accuracy and sample size, the GIDAS (German In-depth Accident Study) data set is divided into three age groups; children (0-14); adults (15-60); and older adults (older than 60). For each group, individual risk curves are computed. Afterwards, the curves are re-aggregated to the overall risk function. The derived model addresses the influence of age on the outcome of pedestrian-to-car accidents. The results show that older people compared with younger people have a higher MAIS3+F injury risk at all collision speeds. The injury risk for children behaves surprisingly. Compared to other age groups, their MAIS3+F injury risk is lower at lower collision speeds, but substantially higher once a threshold has been exceeded. The resulting injury risk curve obtained by re-aggregation looks surprisingly similar to the frequently used logistic regression function computed for the overall injury risk. However, for homogenous subgroups - such as the three age groups - logistic regression describes the typical risk behavior less accurately than the introduced model-based approach. Since the effect of demographic change on traffic safety is greater nowadays, there is a need to incorporate age into established models. Thus far, this is one of the first studies incorporating traffic participant age to an explicit risk function. The presented approach can be especially useful for the modeling and prediction of risks, and for the evaluation of advanced driver assistance systems.

Language: en