Identification of significant factors in fatal-injury highway crashes using genetic algorithm and neural network-Reference-Cited by-同舟云学术

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Identification of significant factors in fatal-injury highway crashes using genetic algorithm and neural network

Published:2018-02 Issue: Volume:111 Page:354-363
ISSN:0001-4575
Container-title:Accident Analysis & Prevention
language:en
Short-container-title:Accident Analysis & Prevention

Author:

Li Yunjie,Ma Dongfang,Zhu Mengtao,Zeng Ziqiang,Wang Yinhai

Funder

State Scholarship Fund of China Scholarship Council

National Natural Science Foundation of China

Shenzhen Science and Technology Planning

International Postdoctoral Exchange Fellowship Program of China

Publisher

Elsevier BV

Subject

Public Health, Environmental and Occupational Health,Safety, Risk, Reliability and Quality,Human Factors and Ergonomics

Reference43 articles.

1. Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections;Abdelwahab;Transp. Res. Rec.: J. Transp. Res. Board,2003

2. Analysis of traffic accident severity using Decision Rules via Decision Trees;Abellán;Expert Syst. Appl.,2013

3. Factors affecting injury severity of vehicle occupants following road traffic collisions;Abu-Zidan;Injury-Int. J. Care Injured,2014

4. Characteristics and prediction of traffic accident casualties in Sudan using statistical modeling and artificial neural networks;Ali;Int. J. Transp. Sci. Technol.,2012

5. Unique NSGA-II and MOPSO algorithms for improved dynamic cellular manufacturing systems considering human factors;Azadeh;Appl. Math. Modell.,2017

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