速度梯度模型的高速公路交通流状态估计方法

为改进高速公路交通流状态估计方法,采用速度梯度模型作为交通流的系统状态方程构建交通流状态估计模型.通过对速度梯度模型参数的敏感性分析,发现模型估计误差对自由流速度和阻塞传播速度两参数较为敏感,需在线估计.然后分别给出了速度梯度模型与扩展卡尔曼滤波以及无迹卡尔曼滤波相结合的高速公路交通流状态估计方法,并应用实测数据对两类交通流状态估计方法的性能进行了评估.结果发现:两类交通状态估计的精度均可达85%左右,无迹卡尔曼滤波算法精度略好于扩展卡尔曼滤波,但计算时耗大.基于速度梯度模型的交通流状态估计方法能有效估计和跟踪交通流状态的变化,且相较于同类方法,所需标定的模型参数更少.

Freeway traffic state estimation by using speed gradient model

This paper presents an approach of freeway traffic state estimation based on speed gradient model. Under the sensitivity analysis of model parameters, it is found that error of model estimation is sensitive to the free flow speed and jam propagation speed, which are recommended to be calibrated online. Moreover, the extended Kalman filter and the unscented Kalman filter methods are introduced combined with the speed Gradient model to solve traffic state estimation problems. The real traffic data were used to evaluate the methods. The results indicate that the accuracies of both extended Kalman filter and the unscented Kalman filter are around 85%, while the latter has a slight vantage in estimation accuracy and disadvantage in computing efficiency. The speed gradient model based traffic state estimation method can estimate and track the traffic dynamics effectively, with less model parameters when compared with similar methods.