A method and device for link prediction in urban road network

Abstract

The invention belongs to the technical field of urban traffic, and in particular relates to a link prediction method and device in an urban road network; the method includes collecting urban road network data sets, calculating attribute values ​​of various urban locations; Divide into clusters, obtain the binary classification result of whether the link connection is established between the city nodes, and calculate the certainty of each attribute for the binary classification label; use the binary classification result to calculate the associated two attributes Frequent binomial set to build a two-layer functional network model structure; train the two-layer functional network model, and use the least squares method to minimize the target loss function; input the urban road network data to be tested into the trained two-layer functional network model. In the layer functional network model, the two-category label of whether there is a road between two urban locations in the urban road network is output; the invention overcomes the high dependence of the complex network structure, optimizes the link prediction effect, and improves its prediction. accuracy.

Description

technical field [0001] The invention belongs to the technical field of urban traffic, and in particular relates to a link prediction method and device in an urban road network. Background technique [0002] With the development of urbanization, the structure of urban traffic road network will have obvious differences due to different traffic patterns, natural conditions, regional distribution of population, evolution stage and development level. The evolution of the network (hereinafter referred to as "road network") will become more and more complex. The complex evolution of the road network increases the difficulty of decision-making in urban road planning. If the road planning is unreasonable, it will lead to the unreasonable allocation of road network resources, and may even hinder the development of urbanization. [0003] Since the 20th century, with the rise of "small world network" and "scale-free network" research, urban road network can be used as a network modelin...

AI Technical Summary

Problems solved by technology

[0004] At present, it can be roughly divided into two methods of link prediction: the first is the link prediction technology based on the traditional method, which mainly uses the similarity between nodes to predict, and uses the connection between nodes to measure their similarity. This is used to learn and predict the connection; the second method is the link prediction based on deep learning, which mainly uses the graph neural network to predict the urban road network, and describes the embedding of nodes by performing convolution operations on the embedding of nodes , for example, Chinese patent CN108108854A proposes a method, system and storage medium for urban road network link prediction. This patent constructs the adjacency matrix of the road network, obtains the Katz similarity matrix according to the adjacency matrix, and converts the Katz similarity After the matrix is ​​normalized, use a multi-layer nonlinear autoencoder to perform network representation learning on it to obtain a network representation vector, decode and reconstruct the adjacency matrix according to the network representation vector, and perform road network linking according to the reconstructed adjacency matrix. prediction; however, the deep learning model used in the above patents has uncontrollable problems, so it is necessary to comprehensively consider the model effect and model controllability to achieve effective link prediction

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