基于小波Mallat算法的交通事件检测方法

Xiaoyuan Wang, Jinglei Zhang
{"title":"基于小波Mallat算法的交通事件检测方法","authors":"Xiaoyuan Wang, Jinglei Zhang","doi":"10.1109/SMCIA.2005.1466967","DOIUrl":null,"url":null,"abstract":"For aim applied to develop intelligent transportation system and the characteristic of traffic flow breakdown, a traffic incident detection method based on fast Mallat algorithm of wavelet analysis is presented. Utilizing the association between the wavelet coefficients and traffic flow, the condition of traffic flow can be extracted directly from the approximate coefficients and detail coefficients of wavelet decomposition from traffic flow parameters. Using data obtained from the simulation under the condition of incident and non-incident, parameters of the algorithm are calibrated and an off-line test is made. According to results of the test compared with California algorithm, low-pass algorithm and MLF algorithm, the algorithm performs better than the other algorithms in traffic incident detection.","PeriodicalId":283950,"journal":{"name":"Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications, 2005. SMCia/05.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"A traffic incident detection method based on wavelet Mallat algorithm\",\"authors\":\"Xiaoyuan Wang, Jinglei Zhang\",\"doi\":\"10.1109/SMCIA.2005.1466967\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For aim applied to develop intelligent transportation system and the characteristic of traffic flow breakdown, a traffic incident detection method based on fast Mallat algorithm of wavelet analysis is presented. Utilizing the association between the wavelet coefficients and traffic flow, the condition of traffic flow can be extracted directly from the approximate coefficients and detail coefficients of wavelet decomposition from traffic flow parameters. Using data obtained from the simulation under the condition of incident and non-incident, parameters of the algorithm are calibrated and an off-line test is made. According to results of the test compared with California algorithm, low-pass algorithm and MLF algorithm, the algorithm performs better than the other algorithms in traffic incident detection.\",\"PeriodicalId\":283950,\"journal\":{\"name\":\"Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications, 2005. SMCia/05.\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications, 2005. SMCia/05.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SMCIA.2005.1466967\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications, 2005. SMCia/05.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SMCIA.2005.1466967","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

摘要

针对智能交通系统的发展和交通流分解的特点,提出了一种基于小波分析快速Mallat算法的交通事件检测方法。利用小波系数与交通流之间的关联,可以直接从交通流参数的小波分解近似系数和细节系数中提取交通流的状态。利用事件和非事件两种情况下的仿真数据,对算法的参数进行了标定,并进行了离线测试。通过与加州算法、低通算法和MLF算法的测试结果对比,该算法在交通事件检测方面的性能优于其他算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A traffic incident detection method based on wavelet Mallat algorithm
For aim applied to develop intelligent transportation system and the characteristic of traffic flow breakdown, a traffic incident detection method based on fast Mallat algorithm of wavelet analysis is presented. Utilizing the association between the wavelet coefficients and traffic flow, the condition of traffic flow can be extracted directly from the approximate coefficients and detail coefficients of wavelet decomposition from traffic flow parameters. Using data obtained from the simulation under the condition of incident and non-incident, parameters of the algorithm are calibrated and an off-line test is made. According to results of the test compared with California algorithm, low-pass algorithm and MLF algorithm, the algorithm performs better than the other algorithms in traffic incident detection.
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
0.00%
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