【摘要】 基于小波包变换和二项分布理论,选取占有率作为检测指标,利用小波包变换的时频高分辨率特性,通过对采集信号的分解和重构,以能量方式识别占有率指标的突变和异常状况,进而识别交通拥挤,同时给出了拥挤程度的积分定义和基于二项分布理论的拥挤概率预测模型,以便分析拥挤状态的时间效应,并对某城市快速路进行VISSIM仿真以获取交通流数据。分析结果表明:小波包变换可有效识别结点能量分布的突变区间,据此可有效判别交通拥挤事件的发生;该方法原理简单,数据采集简单易操作,检测响应时间短,可为交通管理及控制提供依据。更多还原

【Abstract】 This paper is to present an automatic identification method for congestion related incident with wavelet packet decomposition and reconstruction by its high-resolution ability in noise reduction and time-frequency analysis.Moreover,the congestion degree and its occurring likelihood are measured from integral and binomial distribution perspectives to analyze the time effect of congestion.In a case study,the occupancy data as well as other variables is simulated and recorded by VISSIM along a section of urban expressway.Then,the 4-layer decomposition result highlights there exists obvious mutation in energy distribution signal and congestion position is identified.The application example proves that these proposed methods have simple working principle and make in-time response to congestion quickly and effectively,which provides a useful tool for real time traffic management.1 tab,6 figs,12 refs.更多还原