Chapter
Jul 2, 2019
A Model of Injury Severity Prediction in Traffic Accident Based on GA-BP Neural Network
Authors: Shuang Wang [email protected], Chong Wei [email protected], Yansha Wei [email protected], Wenzhe Wang [email protected], and Fei Wu [email protected]Author Affiliations
Publication: CICTP 2019
Abstract
Understanding the non-linear relationship between traffic injury severity and factors in accuracy can help decrease accident occurrence and improve driving safety. This paper uses a GA-BP neural network to model the relationship and predict injury severity in traffic accidents classified into fatality, serious crash, and slight crash. And it validates the superior performance of GA-BP with crash data from the UK in 2015, compared to the BP neural network and the logistic regression model. A sensitivity analysis is applied to find out the contribution that input variables have on injury severity. This paper indicates that the GA-BP neural network provides a reference for injury severity prediction in traffic accident.
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Published In
CICTP 2019
Pages: 2470 - 2481
Copyright
© 2019 American Society of Civil Engineers.
History
Published online: Jul 2, 2019
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Affiliations
MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong Univ., Beijing 100044, China. E-mail: [email protected]
MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong Univ., Beijing 100044, China. E-mail: [email protected]
College of Civil Engineering, Shenzhen Univ., Shenzhen 518000, China. E-mail: [email protected]
School of Transportation and Logistics, Southwest Jiaotong Univ., Chengdu 610031, China. E-mail: [email protected]
China Design Group Co., Ltd., Nanjing 210014, China. E-mail: [email protected]
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Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.