基于比例积分观测器的城市快速路交通密度估计与拥堵识别

针对城市快速路网中只有部分路段检测器可用的情况,为了及时准确地估计整个路网中未布设检测器路段的交通密度并基于估计结果识别路段拥堵状况,提出了基于路网动态模型的比例积分观测器设计方法.首先,将元胞传输模型(cell transmission model,CTM)嵌入到动态图混杂自动机(dynamic graph hybrid automta,DGHA)框架中,对城市快速路网进行建模,并推导出了分段仿射线性系统(piecewise affine linear system,PWALS)模型;然后,基于该模型设计了切换类型的比例积分观测器,并采用线性矩阵不等式(linear matrix inequality,LMI)方法求解观测器的比例增益和积分增益,从而实现对全路网密度的估计,因此,可以通过比较路段的估计密度是否大于其临界密度来识别拥堵的时间和位置;最后,分别以京通快速路和北京东三环快速路为例进行仿真实验,结果显示所提方法是可行的.

Traffic Density Estimation and Congestion Identification of Urban Freeway Based on Proportional-Integral Observer

In order to timely and accurately estimate traffic densities of the whole road network and to identify congestion in an urban freeway network when traffic sensors do not cover the freeway network completely and only local measurement data can be utilized. A proportional-integral ( PI ) observer approach was proposed based on a macroscopic traffic flow model. Firstly, the urban freeway network was modeled by embedding the cell transmission model ( CTM ) into the dynamic graph hybrid automata (DGHA), and then a piecewise affine linear system (PWALS) model was deduced. Moreover, the PI observer was designed on the basis of this model, and the proportional gain and integral gain were computed via using the Linear Matrix Inequality ( LMI) method. Especially, the traffic congestion was identified by checking whether the estimated densities were greater than the critical ones. Finally, as examples, the Jingtong freeway and East Third Ring freeway were given to demonstrate the feasibility of the proposed approach.