A traffic flow data cleaning and restoration method based on spatio-temporal sequence

Abstract

The invention discloses a method for cleaning and repairing traffic flow data based on time-space sequence, comprising the following steps: storing real-time received traffic flow information in a dirty data storage module according to a fixed format; the data cleaning module is used to clean the traffic flow information in the dirty data storage module to obtain the cleaned data. The data after cleaning is modeled and repaired by the data repairing module, and the repaired data is obtained. The repair data is stored in a clean data storage module. By simplifying the traffic flow data cleaningprocess and adopting the time and space parallel analysis method, the invention applies the cell transmission model and the Kalman filter algorithm to the traffic flow data repairing, conveniently, quickly and accurately carries out the traffic flow data cleaning, processing and repairing work, and improves the accuracy of the data.

Description

technical field [0001] This patent relates to the field of data cleaning and repairing, especially a method for cleaning and repairing traffic flow data based on time-space sequences. Background technique [0002] The spatiotemporal data structure is complex and comes from various sources. On the basis of fully understanding the characteristics and attributes of temporal data and spatial data, as well as the relationship between them, we can effectively integrate, clean and repair spatiotemporal data from different sources so that they can be clearly Respond to the law of data changes. [0003] The data information obtained by the road monitoring system plays a vital role in the design and evaluation of traffic projects, and the accuracy of the monitoring statistics is directly related to the direction of future decision-making. The current road traffic monitoring system mainly uses detection coils and video surveillance equipment to make statistics on road traffic data. Du...

AI Technical Summary

Problems solved by technology

The current road traffic monitoring system mainly uses detection coils and video surveillance equipment to make statistics on road traffic data. Due to the aging of equipment, software failure and other factors, errors in statistical results often occur, which may lead to other analysis based on data. Unable to carry out or analysis results are wrong, and abnormal points have a great impact on the observation of the trend and cycle of the entire data and the establishment of the model. At the same time, the lack of traffic data is also a common data phenomenon, which has great influence on the actual scientific research. great hindrance

[0004] Traffic data is data with strong temporal correlation and spatial correlation, and the commonly used methods based purely on mathematical theory often ignore its traffic characteristics.

In the research on the method of traffic data abnormal data processing, scholars have considered the problem data from the perspective of traffic characteristics, and often use a reasonable integration of different analysis methods to weaken the instability of a single method in the data correction effect. However, most of the research results are manifested in the diversity of processing methods, while the analysis of the characteristics of traffic data is somewhat single, and the consideration of the combination of time and space correlation of traffic data is still lacking.

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