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Development of Traffic Accidents Prediction Model With Fuzzy and Neural Network Theory  

Kim, Jang-Uk (서울시립대학교 교통공학과)
Nam, Gung-Mun (원광대학교 토목환경도시공학부)
Kim, Jeong-Hyeon (한국철도기술연구원 미래기술실용화센터)
Lee, Su-Beom (서울시립대학교 교통공학과)
Publication Information
Journal of Korean Society of Transportation / v.24, no.7, 2006 , pp. 81-90 More about this Journal
Abstract
It is important to clarify the relationship between traffic accidents and various influencing factors in order to reduce the number of traffic accidents. This study developed a traffic accident frequency prediction model using by multi-linear regression and qualification theories which are commonly applied in the field of traffic safety to verify the influences of various factors into the traffic accident frequency The data were collected on the Korean National Highway 17 which shows the highest accident frequencies and fatality rates in Chonbuk province. In order to minimize the uncertainty of the data, the fuzzy theory and neural network theory were applied. The neural network theory can provide fair learning performance by modeling the human neural system mathematically. Tn conclusion, this study focused on the practicability of the fuzzy reasoning theory and the neural network theory for traffic safety analysis.
Keywords
Traffic Accidents; Multiple Linear Regression; Quantification I Theory; Fuzzy Reasoning; Neural Network;
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