Predicting the Injury Severity of Pedestrians in Frontal Vehicle Crashes based on Empirical, In-depth Accident Data
For proposed pedestrian protection systems, evaluation of safety benefits is required as an integral part of the design and optimization phases. Stochastic (Monte-Carlo) simulation techniques are currently being utilized to predict safety benefits in terms of physics; however, converting physics to human benefits requires injury and fatality risk models. To this end, multivariate predictive models for pedestrian fatalities and for injury severity on the ISS scale are estimated using the US Pedestrian Crash Data Study (PCDS). In addition to collision speed, which is the most important single explanatory variable, age, pedestrian physiological characteristics and vehicle parameters are significant multivariate predictors. The in-sample as well as out-of-sample predictive quality is remarkably high. The models are intended to provide an interface to large-scale stochastic simulation and virtual testing of proposed vehicle-based active safety systems for pedestrian protection.
pedestrian active safety accident analysis injury probability automobiles statistics