Quality control method of road continuous traffic flow data

Abstract

The invention provides a quality control method of road continuous traffic flow data, and the method comprises the following steps: firstly recognizing a fault stationary traffic data acquiring device by using a pattern recognition algorithm which seals cross sectional flow conservatism and a suspicious stationary traffic data acquiring device; then recognizing road real-time traffic flow data byusing a road traffic flow judgment rule; and finally, completing invalid and missing data by using a distribution value in the same period in the history and the adjacent coil data. The quality control method has the advantages that mass data stored in the existing database can be fully utilized to recognize and repair the fault device and effectiveness of the quality of the real-time data by virtue of a statistic method and a traffic flow theory by using space relation among traffic data, therefore, the effectiveness and integrality of the real-time data can be guaranteed, and a data supportcan be provided for various off-line and real-time applications such as the maintenance of the acquiring device, the traffic monitoring and guidance.

Description

Technical field [0001] The invention relates to a method for quality control of road continuous traffic flow data. Background technique [0002] Due to equipment failures, road traffic conditions and environmental factors, road traffic data collected by stationary traffic data collection equipment (currently commonly used equipment is coils) will inevitably be mistaken and lost. In order to accurately reflect the actual traffic conditions and meet the user's data application needs, the quality of the original data must be controlled to achieve the validity and integrity of the data, and provide good data for application fields such as traffic planning, management and control, and traffic guidance. basis. [0003] Traditional data quality control methods include: 1) historical data correction method; 2) traffic flow theory correction method; 3) adjacent time point linear interpolation method. [0004] The historical data correction method is to use the accurate data of the same fixe...

AI Technical Summary

Problems solved by technology

But when the data to be quality controlled is not in the modeling section, then it will need to be re-modeled, and the workload will be heavy

It can be seen that the adaptability of the traffic flow correction method is poor

[0009] The linear interpolation method of adjacent time points uses the effective data of adjacent time points to correct the data with linear interpolation method, but the larger the time interval between adjacent time points, the greater the similarity and trend deviation between the data, and the method must It is not suitable for large-scale and real-time data quality control because the current data and later data are required to be trend-discriminated

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