一种利用UKF的高速公路实时交通状态估计方法

把无轨迹卡尔曼滤波器（UKF）和宏观随机交通流模型结合在一起，可以实现对高速公路交通状态的实时估计。高速公路被看作是由等距离的路段首尾相接而成的系统，每个路段中交通变量的更新不光与其自身有关，还受到相邻路段的影响。交通传感器通常设置在路段的交界处，而且数量远少于所需估计的交通状态。采用压缩状态空间的形式，将模型参数也作为交通状态而非常量进行估计。仿真结果表明UKF方法能够有效地估计和跟踪交通状态的变化，并且与扩展卡尔曼滤波方法相比具有更高的精确度。

Real-time motorway traffic state estimation based on unscented Kalman filtering

An approach to the real-time estimation of the traffic state in motorway is developed based on unscented Kalman filtering and macroscopic stochastic traffic flow model.The motorway stretch is divided into several segments one by one with the same length and the evolution of the traffic variables are influenced by the states of the neighbor segments.Electronic sensors are usually placed between some segments and the measurements are less than states estimated.This paper uses the compact state-space method and treats the model parameters as the traffic states.Simulation results prove that unscented Kalman filter can predict and track the state efficiently.It is also more accurate than EKF method.