Data cleaning and recovering algorithm of floating car related to multi-threshold space

Abstract

The invention relates to a data cleaning and recovering algorithm of a floating car related to a multi-threshold space. The algorithm comprises the processes of: merging data of a road section into a data matrix, wherein the data is collected by the floating car within a plurality of days; further removing abnormal data in the data matrix according to a three-sigma filter rule after taking a day as a unit to primarily screen missing data and abnormal data in the data matrix; then recovering the missing data by respectively combining space similar characteristics of the road section by a weighted average method and an exponential smoothing method according to the type of the missing data; and finally, recovering high-frequency noise disturbance in data of the floating car by a principal component rebuilding method, thereby fully excavating regularity speed information of the floating car under the condition of not increasing the quantity of the floating car and additional handling equipment, and further, effectively improving the data quality of the floating car. The data cleaning and recovering algorithm can be used for cleaning and recovering work of regularity data of the floating car, and can provide reliable data for subsequent transportation planning and traffic identification.

Description

technical field [0001] The invention relates to a floating car data processing method, in particular to a multi-threshold space-correlated floating car data cleaning and repair algorithm. Background technique [0002] Floating vehicles are widely used in the collection of dynamic traffic data because of their advantages such as wide coverage, high accuracy of data collection, and strong real-time performance. However, due to the high mobility of the floating car, it cannot cover all road sections during non-peak traffic hours, and due to the phenomenon of data packet loss in wireless communication data transmission, it is easy to cause errors or missing data collected by the floating car, thus affecting the accuracy of traffic state measurement. sex. Some literature points out that about 50% of the current traffic data collected by floating vehicles has the problem of data error or loss, and the direct application of these data with problematic quality will bring unstable s...

AI Technical Summary

Problems solved by technology

However, the floating car data only contains vehicle speed information, so the above traffic flow mechanism cannot be used to detect wrong data

At present, the processing of floating car data mostly adopts linear interpolation method or historical average method to repair, and the repair accuracy is low

In addition, there are also methods to improve the data quality of floating vehicles by combining sensor data such as coils and cameras. However, since the current sensors cannot cover all road sections, this method needs to increase the investment in large infrastructure, and the cost is high.

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