A Quality Diagnosis and Restoration Method for Time-series Feature Data of Road Network Traffic

Abstract

A method for diagnosing and repairing the quality of time-series characteristic data of road network traffic, the steps of which are as follows: S1. Obtain the topological structure of the regional road network and construct a traffic data prediction model; S2. Obtain historical traffic data, train the model and test the prediction accuracy of the model; S3 1. Carry out real-time detection and repair of data abnormalities according to the data collected by the traffic detector and the traffic data after production; S4. Perform incremental training on the prediction model to ensure the prediction accuracy of the model. Beneficial effects of the present invention: based on the historical data, predicting the pattern of the integrated production data of the current day's data and joint road network data and comprehensively judging the quality of the data, and finally achieve continuous output of reasonable and highly relevant high-quality data.

Description

Technical field[0001]The invention belongs to the field of traffic information processing, and relates to a method for diagnosing and repairing the quality of road network traffic time series characteristic data based on a graph neural network deep learning method.Background technique[0002]With the development of traffic technology, the use of various algorithms in traffic control has become more and more extensive, making traffic control to a large extent intelligent and automated.[0003]However, many algorithms can only be used in the laboratory or in certain pilot areas, and cannot be fully automated in a large area. The fundamental reason is that many algorithms such as KNN, CNN, and LSTN have very high data requirements. High, unqualified input data will often directly cause unqualified algorithm output results. In reality, the application of many traffic algorithms is often limited because the detector cannot be guaranteed or the data produced does not meet the actual situation...

AI Technical Summary

Problems solved by technology

[0003] However, many algorithms can only be used in laboratories or certain pilot areas, and cannot be fully automated in large areas. The fundamental reason is that many algorithms such as KNN, CNN, and LSTN have very high data requirements. High, unqualified input data often directly cause unqualified algorithm output results

In reality, the application of many traffic algorithms is often limited due to the fact that the detector cannot be guaranteed or the data produced does not meet the actual situation.

[0004] In the existing research, people focus on the quality of the data, 1. The focus is mainly on the part of the missing data and ignore the quality inspection of the produced data; 2. For the missing part of the data, researchers usually use the method of patching 3. For the data of the entire road network, researchers usually only pay attention to the data quality feedback of a certain intersection / segment / lane, and lack of adjacent or similar Comprehensive quality comparison of intersection / road section / lane data

Images