基于交通状态估计的快速路交通联合控制

摘要

Expressway traffic state is divided into unimpeded,slight smooth and congested sub-states by fuzzy C-means clustering method.Combining expressway second-order macroscopic traffic flow model with particle filter algorithm,the traffic state parameters are estimated.By clustering the estimation values of the parameters to one of the three kinds of traffic sub-states,the corresponding traffic state estimation is obtained.Based on the traffic state estimation and the characteristics of each traffic sub-state,an expressway joint control model is established,which combines variable speedlimit control and on-ramp control.The traffic data of Changchun East-expressway is used to validate the model.Results show that the total time spend of the expressway is reduced by 4.55％ and the traffic flow density is also reduced.The traffic congestion is eased to certain extent.%采用模糊C均值聚类方法将快速路交通状态划分为畅通、轻度拥挤和拥挤状态.将快速路二阶宏观交通流模型与粒子滤波算法相结合,实现对快速路交通状态参数的估计,并将交通状态参数估计结果划分到对应状态中,得到交通状态估计结果.在交通状态估计的基础上,考虑3种交通状态下的交通运行特性,结合可变限速控制和入口匝道控制建立快速路联合控制模型.采集吉林省长春市东部快速路交通数据进行实例验证,结果表明:联合控制模型的应用减少了快速路交通流总时间费用消耗的4.55％,降低了交通流密度,在一定程度上缓解了交通拥挤.