关键词: 城市快速路, 行程时间预测, 自适应卡尔曼滤波, 模糊逻辑

Abstract: According to the poor adaptive ability of traditional filter algorithm in the estimation for traffic state and travel time with Kalman filter, an improved fuzzy adaptive Kalman filtering method was proposed. The mean and variance of new interest were input of fuzzy controller through online monitoring the change of new interest, and the fuzzy logic was used to adjust the importance weights of system noise and observation noise, which changed the trust and utilization degree of the model for the observation information. The method overcame the shortcomings that the traditional filter couldn’t real-time track the change of environment. It could adapt to the dynamic changes of traffic state to realize the optimization estimation. The method was tested on urban expressway in Guangzhou by using real-time detection data, and the result show that the traffic state estimation model had better tracking ability than conventional Kalman filter. There was a slight difference between the prediction result and that of actual observation in free traffic flow state and stable flow, and the relative error was under 10% in traffic congested state. The precision and applicability of this method were improved, and it can be used to provide a basis for traffic control and traffic guidance.

Key words: Urban expressway, travel time prediction, adaptive Kalam filter, fuzzy logic