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Methods of Real-Time Data Screening and Reconstruction for Dynamic Traffic Abnormal Data

机译:动态交通异常数据实时数据筛选与重构方法

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Traffic data are the prerequisite and foundation of management and control for traffic flow. Because of the absence and abnormity of field vehicle detector data, four approaches for data screening were designed under considering the influence of interval of sampling and the inherent relationship between traffic three parameters including preliminary screening, threshold screening, traffic theory screening and statistical quality control screening. Then four approaches for data reconstruction were also proposed based on time series, historical data, spatial location and spatio-temporal correlation. Lastly a flow chart was set up for of preprocessing traffic data. Using the field data on city expressway in Beijing, algorithm can effectively remove abnormal data and the missing data prediction accuracy is less than 10%. These methods had been proved accurately, rapidly and stability, and can be used in engineering project.
机译:交通数据是交通流管理和控制的前提和基础。由于野外车辆检测器数据的缺乏和异常,在考虑采样间隔和交通三个参数之间的内在联系的影响下,设计了四种数据筛选方法,包括初步筛选,阈值筛选,交通理论筛选和统计质量控制筛选。 。然后根据时间序列,历史数据,空间位置和时空相关性,提出了四种数据重构方法。最后,建立了用于预处理交通数据的流程图。利用北京城市高速公路的现场数据,该算法可以有效去除异常数据,丢失数据的预测准确度小于10%。这些方法已被证明是准确,快速和稳定的,可用于工程项目。

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