Yoshiaki Takasaki , Miguel Saldana , Jun Ito , Kazushi Sano
{"title":"Development of a method for estimating road surface condition in winter using random forest","authors":"Yoshiaki Takasaki , Miguel Saldana , Jun Ito , Kazushi Sano","doi":"10.1016/j.eastsj.2022.100077","DOIUrl":null,"url":null,"abstract":"<div><p>Because road surface snow conditions are mainly monitored by road patrols, if road surface conditions can be estimated based on meteorological conditions and traffic volume, winter road management can be performed more efficiently. Therefore, this study focuses on estimating road surface snow conditions. The relationship between weather conditions, traffic volume, and road surface conditions was analyzed, and a road surface condition estimation model was constructed using random forest. In addition, because there is a relationship between road surface conditions and tire noise, we estimated the road surface condition by adding tire noise to the weather and traffic volume. As a result, we constructed a model for estimating the road surface condition from the weather and traffic volume, with an accuracy of approximately 95%. The accuracy was slightly lower when the tire noise was added.</p></div>","PeriodicalId":100131,"journal":{"name":"Asian Transport Studies","volume":"8 ","pages":"Article 100077"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2185556022000232/pdfft?md5=234621bbe36427c6a2be8d40cdab198e&pid=1-s2.0-S2185556022000232-main.pdf","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Asian Transport Studies","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2185556022000232","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Because road surface snow conditions are mainly monitored by road patrols, if road surface conditions can be estimated based on meteorological conditions and traffic volume, winter road management can be performed more efficiently. Therefore, this study focuses on estimating road surface snow conditions. The relationship between weather conditions, traffic volume, and road surface conditions was analyzed, and a road surface condition estimation model was constructed using random forest. In addition, because there is a relationship between road surface conditions and tire noise, we estimated the road surface condition by adding tire noise to the weather and traffic volume. As a result, we constructed a model for estimating the road surface condition from the weather and traffic volume, with an accuracy of approximately 95%. The accuracy was slightly lower when the tire noise was added.
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