Traffic data make-up method

Abstract

The invention discloses a traffic data make-up method. The traffic data make-up method comprises the following steps that S1, random deficiency processing is carried out on a complete traffic data set according to a set traffic data deficiency rate, and a complete traffic data set subjected to deficiency processing is obtained; S2, normalization processing is carried out on the complete traffic data set subjected to deficiency processing and the complete traffic data set, and normalized traffic data are obtained; S3, a traffic data make-up model based on a noise reduction pile type automatic encoder deep-layer network structure is trained, and a make-up model is built; S4, input incomplete traffic data containing missing data are made up for by calling the make-up model, and traffic data values obtained after make up are obtained. According to the traffic data make-up method, the missing data and observed data are treated as a whole, the traffic data are made up for on the aspect of data recovery, the structural correlation among the traffic data is deeply excavated, the missing data are made up for at one time, the efficiency is high, and robustness is good.

Description

Technical field

[0001] The invention belongs to the field of intelligent transportation systems, and in particular relates to a method for compensating for traffic data based on a deep network structure of Denoising Stacked Autoencoders. Background technique

[0002] Traffic data is the basis of applications and research in the field of transportation. The travel planning of individual travelers, the traffic control and management of researchers and government departments all need sufficient traffic data as support. However, the traffic data collected from the actual traffic system is often incomplete data and contains some missing data. The existence of these missing data brings many inconveniences to traffic analysis and research. Traffic data compensation aims to fill these missing data as accurately as possible and provide sufficient data support for applications and research in the transportation field.

[0003] The existing traffic data compensation methods mainly include ...

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