城市快速路多尺度交通数据融合方法
发布时间：2018-12-25 19:49
【摘要】：为了从原始数据层面保证动态交通数据的质量,针对多检测器异步采样中非等采样率同时采样的情况,首先构建快速路多检测器动态系统,并对多检测器动态系统进行小波变换,提出基于小波和卡尔曼滤波的多尺度交通数据融合方法.最后,采用上海市南北高架快速路实测数据进行实验验证和对比分析.实验结果表明:对于添加噪声强度为2.5%、5.0%、7.5%和10.0%随机噪声的观测数据,该方法的数据融合效果均优于对比方法.
[Abstract]:In order to guarantee the quality of dynamic traffic data from the original data level, aiming at the case of asynchronous sampling of non-equal sampling rate with multiple detectors at the same time, an expressway multi-detector dynamic system is constructed. A multi-scale traffic data fusion method based on wavelet and Kalman filter is proposed. Finally, the experimental verification and comparative analysis are carried out by using the measured data of Shanghai North and South Expressway. The experimental results show that the data fusion effect of this method is better than that of the contrast method for the observation data with a noise intensity of 2.5% and 10.0% random noise.
【作者单位】： 青岛理工大学汽车与交通学院;吉林大学交通学院;
【基金】：“十二五”国家科技支撑计划资助项目(2014BAG03B03)
【分类号】：TP202;U491
[Abstract]:In order to guarantee the quality of dynamic traffic data from the original data level, aiming at the case of asynchronous sampling of non-equal sampling rate with multiple detectors at the same time, an expressway multi-detector dynamic system is constructed. A multi-scale traffic data fusion method based on wavelet and Kalman filter is proposed. Finally, the experimental verification and comparative analysis are carried out by using the measured data of Shanghai North and South Expressway. The experimental results show that the data fusion effect of this method is better than that of the contrast method for the observation data with a noise intensity of 2.5% and 10.0% random noise.
【作者单位】： 青岛理工大学汽车与交通学院;吉林大学交通学院;
【基金】：“十二五”国家科技支撑计划资助项目(2014BAG03B03)
【分类号】：TP202;U491
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