城市路网交通状态的小波分析检测方法

选取交叉口进口饱和度和路段平均行程速度作为路网状态检测的基本参数, 采用小波包变换的时频高分辨率特性, 以能量分析方法识别进口饱和度和路段平均行程速度的突变与异常状况, 并定义了交通状态系数来定量描述交通状态变化, 设计了基于小波分析的交通状态检测算法, 并采用贝叶斯算法对交通状态进行预测。仿真分析结果表明: 小波包变换可有效识别节点能量分布的突变区间, 据此可准确判别交通状态发生变化的时段; 当采样数据的模极大值点为200~243时, 此段节点能量变化比较剧烈, 信号在此出现突变, 由较平稳向非平稳状态变化, 对应的路段交通状态系数大于0.300h.km^-1, 为拥挤状态。该方法原理简单, 检测响应时间短, 检测结果可靠。

Detection method of traffic state for urban traffic network based on wavelet analysis

The import saturation degree of intersection and the average travel speed of road section were selected as the basic parameters of road network's state detection, the high time-frequency properties of wavelet packet transform was adopted, and the mutation and unusual conditions of the saturation and the speed were distinguished by using energy analysis method. In order to describe the change of traffic state, a coefficient was defined, a traffic state detection algorithm was designed by using wavelet analysis, and Bayesian Method was used to predict the traffic state. Simulation result shows that the mutation interval of energy distribution can be identified by using wavelet analysis, based on which the changing time interval of traffic state can be distinguished. While the maximal points of sampling data modulus are from 200 to 243, the energy change of the section node is intense, and the state changes from steadiness to unsteadiness. When the coefficient of traffic state is more than 0.300 h·km-1, a crowded state appears. The method with simple working principle and short time response to congestion is feasible because of its credible detection result.