Dynamic graph convolution traffic speed prediction method

A speed prediction and dynamic map technology, applied in traffic flow detection, forecasting, traffic control systems, etc., can solve the problem of ignoring the real-time dynamic correlation between road sections with non-European map structure space relationship, so as to ensure travel safety and efficiency, high Accurate traffic forecast results and the effect of accurate data support

Inactive Publication Date: 2020-09-22
CENT SOUTH UNIV
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[0005] The present invention provides a dynamic graph convolution traffic speed prediction method, the purpose of which is to solve the problem that the traditional tr

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  • Dynamic graph convolution traffic speed prediction method
  • Dynamic graph convolution traffic speed prediction method
  • Dynamic graph convolution traffic speed prediction method

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[0046] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will describe in detail with reference to the drawings and specific embodiments.

[0047] The present invention provides a dynamic graph convolution traffic speed prediction method aiming at the problem that the existing traffic prediction method ignores the non-European graph structural spatial relationship between the road sections and the real-time dynamic correlation between the road sections.

[0048] Such as Figure 1 to Figure 5 As shown, the embodiment of the present invention provides a dynamic graph convolution traffic speed prediction method, comprising: step 1, matching the vehicle GPS track data to the urban road network, and obtaining the traffic speed time series of each road section; step 2 , regard the road sections of the urban road network as graph nodes, and regard the intersections of the urban road network as the ...

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Abstract

The invention provides a dynamic graph convolution traffic speed prediction method which comprises the steps: step 1, matching vehicle GPS trajectory data into an urban road network, and obtaining a traffic speed time sequence of each road section; step 2, regarding road sections of the urban road network as graph nodes, regarding intersections of the urban road network as connecting edges of a graph, constructing a road network graph, and obtaining an adjacent matrix between the road sections; step 3, calculating the traffic speed similarity between adjacent road sections according to the traffic speed time sequence of each road section, and obtaining a real-time adjacent road section similarity matrix; and step 4, inputting the traffic speed time sequence of each road section and the adjacent road section similarity matrix into a graph convolution network for training to obtain a future road section traffic speed prediction result. According to the invention, spatial dependence and time dependence between road sections can be learned in real time, the change rule of the traffic speed can be captured, the speed of future urban roads can be predicted more accurately, and the methodcan be applied to intelligent traffic and smart city construction.

Description

technical field [0001] The invention relates to the technical field of traffic forecasting, in particular to a dynamic graph convolution traffic speed forecasting method. Background technique [0002] In the face of increasingly prominent traffic congestion, urban residents and traffic planning managers need to obtain the traffic status of the urban road network in a timely manner to avoid travel congestion and ensure travel safety and efficiency. Therefore, real-time and accurate traffic forecasting on large-scale urban road networks has great practical significance and application value. [0003] Currently, traffic flow forecasting can be divided into two categories: model-driven and data-driven. The model-driven approach is based on the simulation of traffic flow and driver decision-making process, which can capture the complex characteristics of the traffic network and simulate real traffic conditions, provided that the current traffic volume is known. By obtaining the...

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Application Information

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IPC IPC(8): G08G1/01G08G1/052G06N3/04G06N3/08G06Q10/04G06Q50/30
CPCG08G1/0125G08G1/0129G08G1/0145G08G1/052G06N3/08G06Q10/04G06Q50/30G06N3/045
Inventor 刘启亮袁浩涛杨柳邓敏
Owner CENT SOUTH UNIV
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