融合多道面数据挖掘算法的交通安全监测系统  

Traffic Safety Monitoring System Incorporating Multipath Data Mining Algorithm

作　　者：徐荣[1] 魏莉 XU Rong;WEI Li(School of Information and Engineering, Anhui Radio and TV University, Hefei 230022, China)

机构地区：[1]安徽广播电视大学信息工程学院,安徽合肥230022

出　　处：《信阳农林学院学报》2018年第4期121-124,共4页Journal of Xinyang Agriculture and Forestry University

基　　金：安徽省教育厅高校优秀青年人才基金项目(gxyq2017163)

摘　　要：为了提高系统的交通事故监测性能,首先通过卡尔曼集进行快速事故预测,其次引入参数事故覆盖范围,利用多道面状态传感器节点数据挖掘实现对事故的有效筛选,随后结合数据挖掘实现对根源交通事故的有效监测。最后,通过仿真结果表明:该系统能准确地检测出各种交通运行参数和交通事件,为交通安全提供了有力保障。In order to improve the performance of traffic accident monitoring,this paper first carries out rapid accident prediction through Calman set.Secondly,it introduces the range of parameter accident coverage,uses multi channel state sensor node data mining to effectively screen the accident,and then combines data mining to realize the effective monitoring of the root traffic accidents.Finally,the simulation results show that the system can accurately detect various traffic parameters and traffic incidents,and provide a strong guarantee for traffic safety.

关 键 词：多道面状态传感器节点 交通事故监测 卡尔曼集 数据挖掘 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]