A Completion Method for Traffic Missing Data Based on Spatiotemporal Attention Mechanism

Abstract

A traffic missing data completion method based on the spatio-temporal attention mechanism. First, through the attention mechanism, capture the degree of influence of all road sections in the road network on the traffic state of the road network at the current moment, and recapture the spatial data at different times. Relevance information to improve the accuracy of data completion. Secondly, considering the timing of traffic data, traffic data at different times have different influences on the data at the current moment. This inconsistent time correlation information is captured through the time attention mechanism and retained when completing the current missing data. The most effective information improves the completion effect of the model. Finally, while using the spatiotemporal attention mechanism to capture the spatiotemporal correlation of traffic data, considering that the correlation between data is attenuated by the increase of spatial distance and time interval, adding a spatiotemporal attenuation matrix improves the completion accuracy. The present invention not only greatly improves the completion accuracy in the case of low data missing rate, but also improves the completion accuracy in the case of high data missing rate.

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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