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Pedestrian-related accidents are considered to be the most serious of traffic accidents due to the associated high fatality rates. In Korea, pedestrian fatalities accounted for approximately 40% of all traffic-related fatalities in 2004. Significant efforts have been made to develop effective countermeasures for pedestrian-vehicle collisions. A basis for devising such countermeasures is to understand the characteristics of pedestrian-vehicle collisions. This study develops a pedestrian fatality model capable of predicting the probability of fatality in pedestrian-vehicle collisions. Binary logistic regression and a probabilistic neural network (PNN) are employed to estimate the probability of pedestrian fatality. Pedestrian age, vehicle type and collision speed are used as independent variables of the fatality model. The models developed herein are valuable tools that can be used to direct safety policies and technologies associated with pedestrian safety.

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