摘要
在雾区路段严重影响高速公路交通安全的情势下,为了更加准确地检测出交通事件,通过对传统的交通异常状态识别算法的分析,提出了一种适用于雾区低流量情况下的交通事件识别算法,该算法通过对稀疏的雨雾浓度信息的处理,对传统交通异常状态识别算法的有关参数进行修正,极大地提高了雾区环境的识别精度。最后利用MATLAB仿真软件对稀疏雨雾浓度信息的处理效果进行了验证比较。
In order to detect traffic incidents more accurately in the situation that the fog section seriously affects the traffic safety of expressways,a traffic incident suitable for low-traffic conditions in fog areas is proposed through the analysis of traditional traffic abnormality recognition algorithms.The recognition algorithm,which corrects the parameters of the traditional traffic abnormality recognition algorithm by processing the sparse information of the rain and fog concentration,greatly improves the recognition accuracy of the fog area environment.Finally,using MATLAB simulation software to verify the comparison of sparse rain and fog concentration information processing results.
作者
李文钊
李明
张奔牛
LI Wen-zhao;LI Ming;ZHANG Ben-niu(School of Information Science and Engineering,Chongqing Jiaotong University,Chongqing 400074;CSCEC Xinjiang Construction&Engineering Group Co.,Ltd.,Urumqi Xinjiang830047;School of Civil Engineering,Chongqing Jiaotong University,Chongqing 400074)
出处
《数字技术与应用》
2018年第2期129-131,共3页
Digital Technology & Application
基金
重庆市社会民生科技创新项目(cstc2016shmszx30026)
重庆高校创新团队建设计划项目(CXTDG201602013)
乌鲁木齐市科学技术计划项目(Y161320008)
关键词
雾区
最小二乘法
雨雾浓度
交通事件识别算法
MATLAB
fog area
least square method
rain fog concentration
traffic incidentidentification algorithm
MATLAB
