信号控制交叉路口车辆排队长度  

Vehicle queue length at signal controlled intersection

作　　者：祁宏生[1] 王殿海[1] 

机构地区：[1]吉林大学交通学院,长春130022

出　　处：《吉林大学学报（工学版）》2009年第6期1457-1462,共6页Journal of Jilin University:Engineering and Technology Edition

基　　金：'973'国家重点基础研究发展计划项目(2006CB705500)

摘　　要：建立了一个能够估计市内信号交叉路口车辆实时排队长度的模型。分析了路段交通流之间的流向关系,根据流向关系建立了两种路段交通流影响模型:神经网络模型和贝叶斯网络模型,并描述了模型的结构。为了方便模型的实际应用,分别用主成份对输入变量降维,用EM算法和高斯混合分布函数来表达模型和训练模型参数。基于实际路网设计了一个仿真路网,并用不同的实验场景对模型进行有效性验证。仿真实验的结果表明,由于城市路网中存在的随机性,贝叶斯网络模型能够更好地把握交通流变化的趋势。A study was performed to estimate the real-time vehicle queue length before the stop line at the urban signal controlled intersection.The relationship among traffic flow directions in road sections was analyzed,and two models,i.e.,an artificial neural network model and a Bayesian network model were established to deal with the effects of traffic flows in the road sections,and the structure of the models was specified.To facilitate the practical application of these models,the principal component analysis was used to decrease the input dimensions,the EM algorithm and GMM were used to represent the parameters of the train models.A simulation road network was designed based on the real world data to validate the proposed models under various experiment scenarios.Simulation results showed that the Bayesian network model can grasp the traffic flow tendencies better than the artificial neural network model because of the randomness in the urban road network.

关 键 词：交通运输系统工程 交叉路口 排队 神经网络 贝叶斯网络 主成份分析 

分 类 号：U491.1[交通运输工程—交通运输规划与管理]