Graph neural network traffic flow prediction method based on multivariate time sequence interpolation

A multivariate time series and neural network technology, applied in the field of graph neural network traffic flow prediction based on multivariate time series interpolation, can solve problems such as data not real-time, traffic flow deviation, data loss, etc., and achieve the effect of overcoming data loss

Pending Publication Date: 2021-11-19
BEIHANG UNIV
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Problems solved by technology

In practical applications, the data collected by some institutions and organizations is not real-time, and sometimes the data will be lost, w

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  • Graph neural network traffic flow prediction method based on multivariate time sequence interpolation
  • Graph neural network traffic flow prediction method based on multivariate time sequence interpolation
  • Graph neural network traffic flow prediction method based on multivariate time sequence interpolation

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[0042] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention is further described below with reference to the accompanying drawings.

[0043] In this example, see figure 1 and figure 2 As shown, the present invention proposes a graph neural network traffic flow prediction method based on multivariate time series interpolation, including the steps:

[0044] S10, use a weighted graph to describe the topology between stations in the traffic network, and take the inflow and outflow of traffic as the characteristics of the station nodes themselves;

[0045] S20, construct a traffic flow map according to the inflow and outflow of each station;

[0046] S30, using a graph neural network to impute missing values;

[0047] S40, obtain the spatial feature sequence through the graph attention network;

[0048] S50, temporal attention captures the dynamic correlation between different times, and then uses the long ...

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Abstract

The invention discloses a graph neural network traffic flow prediction method based on multivariate time sequence interpolation, and the method comprises the steps: describing the topology between stations in a traffic network through a weighted graph, and enabling the inflow and outflow of traffic to serve as the characteristics of station nodes; constructing a traffic flow graph according to the inflow and outflow conditions of each station; interpolating the missing value by using a graph neural network; obtaining a spatial feature sequence through a graph attention network; capturing the dynamic correlation between different times through time attention, and then capturing the time characteristics by using the long and short-term memory network to obtain the traffic flow characteristics as the prediction result. The method can eliminate the influence caused by the missing of the time-space relation data in the traffic flow prediction, overcomes the data missing possibly occurring in the practical application, and achieves the long-term prediction of the traffic flow.

Description

technical field [0001] The invention belongs to the technical field of traffic flow prediction, in particular to a graph neural network traffic flow prediction method based on multivariate time series interpolation. Background technique [0002] With the rapid development of various positioning technologies such as the Global Positioning System (GPS) and mobile devices, and the continuous improvement of information collection equipment such as cameras and sensors, a large amount of traffic time series data with geographic information has been accumulated for data analysis. We have entered the "big data era", which has had a major impact on human life and social governance. Therefore, how to obtain useful information from massive data has attracted widespread attention. Spatio-temporal data mining plays an important role in many applications, including intelligent transportation, urban planning, public safety, healthcare, and environmental management. Traditional data mining...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/26G08G1/01G06N3/04G06N3/08
CPCG06Q10/04G06Q50/26G06N3/08G08G1/0125G06N3/045
Inventor 彭浩刘琳刘明生冼俊宇
Owner BEIHANG UNIV
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