典型文献
Presentation of machine learning methods to determine the most important factors affecting road traffic accidents on rural roads
文献摘要：
The purpose of this research was to develop statistical and intelligent models for predicting the severity of road traffic accidents(RTAs)on rural roads.Multiple Logistic Regression(MLR)was used to predict the likelihood of RTAs.For more accurate prediction,Multi-Layer Perceptron(MLP)and Radius Basis Function(RBF)neural networks were applied.Results indicated that in MLR,the model obtained from the backward method with the correct percent of 84.7％and R2 value of 0.893 was the best method for predicting the likelihood of RTAs.Also,MLR showed that the variables of not paying attention to the front not paying attention to the frontroad ahead,followed byand then vehicle-motorcycle/bike accidents were the greatest problems.Among the models,MLP had a better performance,so that the prediction accuracy of MLR,MLP,and RBF were 84.7％,96.7％,and 92.1％,respectively.MLP model,due to higher accuracy,showed that the variable of reason of accident had the highest effect on the prediction of accidents,and considering MLR results,the variables of not paying attention to the front and then vehicle-motorcycle/bike accidents had the most influence on the occurrence of accidents.Therefore,motorcyclists and cyclists are more prone to accidents,and appropriate solutions should be adopted to enhance their safety.
文献关键词：
中图分类号：
作者姓名：
Hamid MIRZAHOSSEIN;Milad SASHURPOUR;Seyed Mohsen HOSSEINIAN;Vahid Najafi Moghaddam GILANI
作者机构：
Civil-Transportation Planning Department,Faculty of Technical and Engineering,Imam Khomeini International University(IKIU),Qazvin 34148-96818,Iran;School of Civil Engineering,Iran University of Science and Technology(IUST),Tehran 13114-16846,Iran
文献出处：
引用格式：
[1]Hamid MIRZAHOSSEIN;Milad SASHURPOUR;Seyed Mohsen HOSSEINIAN;Vahid Najafi Moghaddam GILANI-.Presentation of machine learning methods to determine the most important factors affecting road traffic accidents on rural roads)[J].结构与土木工程前沿,2022(05):657-666
A类：
frontroad,byand,motorcyclists,cyclists
B类：
Presentation,machine,learning,methods,determine,most,important,factors,affecting,traffic,accidents,rural,roads,purpose,this,research,was,develop,statistical,intelligent,models,predicting,severity,RTAs,Multiple,Regression,MLR,used,likelihood,For,more,accurate,prediction,Layer,Perceptron,MLP,Radius,Basis,Function,RBF,neural,networks,were,applied,Results,indicated,that,obtained,from,backward,correct,percent,value,best,Also,showed,variables,not,paying,attention,ahead,followed,then,vehicle,motorcycle,bike,greatest,problems,Among,had,better,performance,accuracy,respectively,due,higher,reason,highest,effect,considering,results,influence,occurrence,Therefore,are,prone,appropriate,solutions,should,adopted,enhance,their,safety
AB值：
0.476376
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