基于广义最小二乘模型的动态交通OD矩阵估计

GLS Model Based Dynamic Origin-Destination Matrix Estimation for Traffic Systems

基于广义最小二乘模型,建立了一种带滑动窗的动态OD矩阵估计算法,可通过对路段交通量和行程时间的检测来估计时变的OD数据.对模型中关键的交通分配矩阵,给出了解析的计算公式.算法是一种递推的估计过程,仅需较少的先验信息,且估计过程不会发散;滑动窗的引入可充分利用量测信息,抑制量测噪声.

Based on Generalized Least Square (GLS) model, a dynamic origin-destination (OD) matrix estimation algorithm with sliding window is proposed. The OD matrix can be estimated through the surveillance of traffic counts and traveling time on links in a traffic network. An analytical formula to calculate the key assignment matrix is also presented. The algorithm is a recursive procedure with few apriori data, and there exists no divergence in the estimation. With the sliding window, more surveillance data can be utilized effectively, and measurement noises can be restrained efficiently. A lot of simulation tests show that the estimation accuracy of the proposed method is much higher than that of Cascetta's recursive algorithm, and there is only a little increase in computation cost.