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Development of Pedestrian Fatality Model using Bayesian-Based Neural Network  

O, Cheol (한양대학교 교통시스템공학과)
Gang, Yeon-Su (한국교통연구원)
Kim, Beom-Il (한국교통연구원)
Publication Information
Journal of Korean Society of Transportation / v.24, no.2, 2006 , pp. 139-145 More about this Journal
Abstract
This paper develops pedestrian fatality models capable of producing the probability of pedestrian fatality in collision between vehicles and pedestrians. Probabilistic neural network (PNN) and binary logistic regression (BLR) ave employed in modeling pedestrian fatality pedestrian age, vehicle type, and collision speed obtained from reconstructing collected accidents are used as independent variables in fatality models. One of the nice features of this study is that an iterative sampling technique is used to construct various training and test datasets for the purpose of better performance comparison Statistical comparison considering the variation of model Performances is conducted. The results show that the PNN-based fatality model outperforms the BLR-based model. The models developed in this study that allow us to predict the pedestrian fatality would be useful tools for supporting the derivation of various safety Policies and technologies to enhance Pedestrian safety.
Keywords
보행자 안전;보행자-차량 충돌사고;베이지안 신경망;로지스틱 회귀분석;보행자 사망확률모형;
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