基于机器学习的车辆加速度数据轨迹质量指数预测

IF 0.4 Q4 ENGINEERING, GEOLOGICAL
C. Choi, Hunki Kim, Young Cheul Kim, Sang-su Kim
{"title":"基于机器学习的车辆加速度数据轨迹质量指数预测","authors":"C. Choi, Hunki Kim, Young Cheul Kim, Sang-su Kim","doi":"10.12814/JKGSS.2020.19.1.045","DOIUrl":null,"url":null,"abstract":"There is an increasing tendency to try to make predictive analysis using measurement data based on machine learning techniques in the railway industries. In this paper, it was predicted that Track quality index (TQI) using vehicle acceleration data based on the machine learning method. The XGB (XGBoost) was the most accurate with 85% in the all data sets. Unlike the SVM model with a single algorithm, the RF and XGB model with a ensemble system were considered to be good at the prediction performance. In the case of the Surface TQI, it is shown that the acceleration of the z axis is highly related to the vertical direction and is in good agreement with the previous studies. Therefore, it is appropriate to apply the model with the ensemble algorithm to predict the track quality index using the vehicle vibration acceleration data because the accuracy may vary depending on the applied model in the machine learning methods.","PeriodicalId":42164,"journal":{"name":"Journal of the Korean Geosynthetic Society","volume":null,"pages":null},"PeriodicalIF":0.4000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of Track Quality Index (TQI) Using Vehicle Acceleration Data based on Machine Learning\",\"authors\":\"C. Choi, Hunki Kim, Young Cheul Kim, Sang-su Kim\",\"doi\":\"10.12814/JKGSS.2020.19.1.045\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"There is an increasing tendency to try to make predictive analysis using measurement data based on machine learning techniques in the railway industries. In this paper, it was predicted that Track quality index (TQI) using vehicle acceleration data based on the machine learning method. The XGB (XGBoost) was the most accurate with 85% in the all data sets. Unlike the SVM model with a single algorithm, the RF and XGB model with a ensemble system were considered to be good at the prediction performance. In the case of the Surface TQI, it is shown that the acceleration of the z axis is highly related to the vertical direction and is in good agreement with the previous studies. Therefore, it is appropriate to apply the model with the ensemble algorithm to predict the track quality index using the vehicle vibration acceleration data because the accuracy may vary depending on the applied model in the machine learning methods.\",\"PeriodicalId\":42164,\"journal\":{\"name\":\"Journal of the Korean Geosynthetic Society\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.4000,\"publicationDate\":\"2020-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the Korean Geosynthetic Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.12814/JKGSS.2020.19.1.045\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, GEOLOGICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Korean Geosynthetic Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12814/JKGSS.2020.19.1.045","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, GEOLOGICAL","Score":null,"Total":0}
引用次数: 0

摘要

在铁路行业,越来越多的人尝试使用基于机器学习技术的测量数据进行预测分析。本文采用基于机器学习的方法,利用车辆加速度数据预测轨道质量指数(TQI)。XGB (XGBoost)在所有数据集中准确率最高,为85%。与单一算法的SVM模型不同,具有集成系统的RF和XGB模型被认为具有较好的预测性能。在Surface TQI的情况下,可以看出z轴的加速度与垂直方向高度相关,这与之前的研究结果很好地吻合。因此,由于机器学习方法中所应用的模型的精度可能会有所不同,因此将集成算法的模型应用于利用车辆振动加速度数据预测轨道质量指标是合适的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Track Quality Index (TQI) Using Vehicle Acceleration Data based on Machine Learning
There is an increasing tendency to try to make predictive analysis using measurement data based on machine learning techniques in the railway industries. In this paper, it was predicted that Track quality index (TQI) using vehicle acceleration data based on the machine learning method. The XGB (XGBoost) was the most accurate with 85% in the all data sets. Unlike the SVM model with a single algorithm, the RF and XGB model with a ensemble system were considered to be good at the prediction performance. In the case of the Surface TQI, it is shown that the acceleration of the z axis is highly related to the vertical direction and is in good agreement with the previous studies. Therefore, it is appropriate to apply the model with the ensemble algorithm to predict the track quality index using the vehicle vibration acceleration data because the accuracy may vary depending on the applied model in the machine learning methods.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
20.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信