Traffic missing data complementation method based on space-time attention mechanism

Abstract

A traffic missing data complementation method based on a space-time attention mechanism comprises the steps of firstly capturing the influence degree of all road sections in a road network on the road network traffic state at the current moment in an attention mechanism mode, capturing spatial correlation information again at different moments, and improving the data complementation precision; and secondly, considering the time sequence of the traffic data, the influence degrees of the traffic data at different moments on the data at the current moment are different, capturing the inconsistent time correlation information through a time attention mechanism, retaining the most effective information when the current missing data is complemented, and improving the complementation effect of the model. And finally, while capturing the spatial-temporal correlation of the traffic data by using a spatial-temporal attention mechanism, considering that the correlation between the data is attenuated due to the increase of the spatial distance and the time interval, and adding a spatial-temporal attenuation matrix to improve the completion precision. According to the method, the complementation precision under the condition that the data missing rate is low is greatly improved, and the complementation precision under the condition that the data missing rate is high is also improved.

Description

technical field [0001] The invention belongs to the field of traffic, and relates to a method for complementing traffic missing data based on a spatio-temporal attention mechanism. Background technique [0002] With the rapid development of Internet technology and traffic informatization, the scale of traffic data is getting larger and larger. In intelligent transportation systems, complete and effective traffic data is of great significance to traffic management. However, when collecting traffic data in real life, due to some unavoidable events (such as equipment damage, bad weather, etc.), the data collection will be interrupted, resulting in the loss of some data, which reduces the effectiveness of the data set and restricts The development of intelligent transportation construction. It has important theoretical and practical research significance to effectively complete the missing values ​​in the traffic data set. However, the completion of traffic data is very challe...

AI Technical Summary

Problems solved by technology

However, the completion of traffic data is very challenging. On the one hand, the change of road traffic data over time is non-stationary, such as morning and evening peak hours, holidays, etc. will affect the change trend of traffic data. Strong time dependence, at the same time, the traffic data also shows a significant long-term period correlation; on the other hand, the traffic network in the real world has a complex spatial structure, and there is a spatial correlation between different road network nodes

In addition, the missing pattern of the data also has an impact on the completion of missing values

Existing completion methods do not adequately model these properties when dealing with missing data

For example, after decomposing the input vector, Li et al. combined LSTM and Support Vector Regression (SVR) to complete the time series data through a multi-view method, ignoring the dynamic changes in the spatial-temporal correlation between the data. , without considering the significant periodic correlation of traffic data

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