Traffic resource allocation method and system based on confidence map convolutional network, and medium

Abstract

The invention discloses a traffic resource allocation method and system based on a confidence map convolutional network, and a medium. The traffic resource allocation method comprises the following steps: modeling a current actual traffic network and traffic stations; carrying out feature extraction on traffic network graph data generated by modeling through a graph embedding method so as to determine a feature extraction vector; and processing the feature extraction vector based on a deep belief network, and spreading and updating the feature extraction vector through a graph convolutional neural network, thereby updating the feature representation of the node and changing the label type of each node to generate a target topological structure, and then automatically allocating traffic resources to the corresponding traffic station according to the target topological structure. Therefore, automatic distribution of one-time traffic resources is realized. According to the invention, the resource allocation problem of the traffic station can be timely responded, so that the traffic resources can be better and dynamically allocated.

Description

technical field [0001] The invention relates to the field of traffic resource allocation, in particular to a traffic resource allocation method, system and medium based on a confidence graph convolution network. Background technique [0002] In a broad sense, transportation resources refer to the basic design and combination of all transportation modes under various technical conditions in the transportation industry. The optimal allocation of transportation resources is directly considered as the optimal combination of infrastructure of various transportation modes. The intelligent traffic resource allocation system is firstly established on the basis of real sampling and data analysis, through abstract modeling of the real scene, and then based on the modeling results, it uses advanced analytical methods to analyze the optimal allocation method of traffic resources. [0003] In recent years, at the macro level, my country's transportation industry has achieved leapfrog de...

AI Technical Summary

Problems solved by technology

The graph data is a kind of non-Euclidean space data, and the local structure of each node is different, so that the translation invariance is no longer satisfied, that is, the graph data does not have translation invariance, so the basic operator in the traditional convolutional neural network ( Convolution and pooling) cannot implement the function of the application on the graph data

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