道路网交通流状态变化趋势判别方法  

Identification of Traffic State Variation Trend in Road Network

作　　者：董春娇[1,2] 邵春福[2] 谢坤[1] 李慧轩[2] 

机构地区：[1]田纳西大学交通研究中心,田纳西诺克斯维尔37996 [2]北京交通大学城市交通复杂系统理论与技术教育部重点实验室,北京100044

出　　处：《同济大学学报（自然科学版）》2012年第9期1323-1328,共6页Journal of Tongji University:Natural Science

基　　金：国家自然科学基金(50778015);国家"九七三"重点基础研究发展规划(2006CB705500)

摘　　要：根据城市快速路交通流参数实测数据所表征的交通流状态特性,结合基本图和三相交通流理论,将道路网交通流状态划分为自由流状态、拥挤流状态和阻塞流状态.在此基础上,采用模糊信息粒化的思想,以道路网检测断面数为窗口,设计三角形模糊隶属函数,将同时段的道路网交通流状态映射为含有低边界值L、中值R和高边界值U三参数的模糊信息粒.以模糊信息粒为输入,建立Elman网络模型预测交通流状态变化趋势.依据预测结果计算道路网交通流状态综合指数,判别未来时段道路网交通流状态,并以北京市某一区域路网为例进行实证性研究.研究结果表明:所提出的方法能够实现道路网交通流状态变化趋势判别,准确率为93.33%,同等条件下支持向量机模型判别准确率仅为86.67%.Under the theoretical frameworks of both the traditional fundamental diagram approach and newly-developed three-phase traffic theory, with regard to the characteristics of traffic flow based on detection data, traffic flow was splitting into three traffic states, which include free traffic , congested traffic and jam traffic. In the light of traffic states definition, firstly, traffic flow parameters of road network at the same instant is transformed to fuzzy information granulation which is made up by L, R and U parameters. Then, Elman neural network is employed to realize traffic states prediction with three parameters of fuzzy information granulation as inputting. Subsequently, traffic states composite index is calculated by the prediction result to identify the traffic states. Finally, the empirical researches proceed by taking a region in Beijing urban expressway network, the research results show that the proposed methodology can realize identification of traffic states variation in road network, the identification accuracy is 93.33%, however, the identification accuracy of SVM method on the same condition is 86.67 %.

关 键 词：交通流状态 趋势判别 模糊信息粒 ELMAN网络 

分 类 号：U491[交通运输工程—交通运输规划与管理]