Development of probabilistic pedestrian fatality model for characterizing pedestrian-vehicle collisions

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 counte...

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Published in:International journal of automotive technology 2008, 9(2), , pp.191-196
Main Authors: Oh, C., Kang, Y. S., Youn, Y., Konosu, A.
Format: Article
Language:English
Subjects:
Automobile safety
Automotive Engineering
Dependent variables
Engineering
Fatalities
Independent variables
Neural networks
Pedestrians
Regression analysis
Studies
Traffic accidents & safety
Variables
자동차공학
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cited_by cdi_FETCH-LOGICAL-c379t-9125fa48f333706107791f67147bd5cb1828f33d272aff0eb0f06daa26d82fc13
cites cdi_FETCH-LOGICAL-c379t-9125fa48f333706107791f67147bd5cb1828f33d272aff0eb0f06daa26d82fc13
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container_title International journal of automotive technology
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creator Oh, C.
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Youn, Y.
Konosu, A.
description 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.
doi_str_mv 10.1007/s12239-008-0024-7
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source ABI/INFORM Global; Springer Nature
subjects Automobile safety
Automotive Engineering
Dependent variables
Engineering
Fatalities
Independent variables
Neural networks
Pedestrians
Regression analysis
Studies
Traffic accidents & safety
Variables
자동차공학
title Development of probabilistic pedestrian fatality model for characterizing pedestrian-vehicle collisions
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