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City road network link prediction method, system and storage medium

A link prediction and urban road network technology, applied in the field of urban transportation, can solve the problem of unoptimistic scalability of the single-layer linear limited model, and achieve the effect of preventing overfitting.

Active Publication Date: 2018-06-01
CENT SOUTH UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] In the link prediction task of the road network, the link prediction model based on similarity information is not optimistic due to its single-layer linearity limitation and the unscalability of the model.

Method used

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  • City road network link prediction method, system and storage medium
  • City road network link prediction method, system and storage medium
  • City road network link prediction method, system and storage medium

Examples

Experimental program
Comparison scheme
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Embodiment 1

[0028] This embodiment discloses a method for predicting urban road network links.

[0029] For ease of description, some terms used in this embodiment are explained as follows:

[0030] [Link Prediction]: Define graph G=(V,E) as an undirected connected graph, V is the set of all nodes in G, and E is the set of edges. Define n=|V|, n is the number of nodes in graph G, m=|E| is the number of edges in graph G, then there are n(n–1) / 2 node pairs in the network, and U is the total number of node pairs Set, |U|=n(n–1) / 2,. Given the state of each pair of nodes in graph G at time δ, the link prediction problem can be formally described as inferring the subset of missing links that are in the current state or will be formed at time δ + t.

[0031] [Adjacency matrix]: Define matrix A as the adjacency matrix of graph G. Matrix A satisfies the following conditions,

[0032]

[0033] The adjacency matrix A is a symmetric matrix, the diagonal elements are all 0, and each row sum (co...

Embodiment 2

[0119] This embodiment discloses a system for predicting urban road network links, including a memory, a processor, and a computer program stored in the memory and operable on the processor. When the processor executes the computer program, the steps of the above method are realized. .

Embodiment 3

[0121] This embodiment discloses a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented.

[0122] In summary, the urban road network link prediction method, system and storage medium disclosed in the above-mentioned embodiments of the present invention have the following beneficial effects:

[0123] On the one hand, there is no derivation calculation of the Sigmoid function in the loss function used. The weight update of the weight matrix depends on the error. The larger the error, the faster the update, and the smaller the error, the slower the update, thus avoiding the existing cost of variance. When the function is a loss function, the weight matrix update is too slow due to the nature of the Sigmoid function.

[0124] On the other hand, in the loss function adopted, the limitation of local linear embedding is added, so that after the network performs represent...

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Abstract

The invention relates to the city traffic technical field, and discloses a city road network link prediction method, system and a storage medium, thus improving the road network link prediction accuracy, and improving the data processing efficiency; the method comprises the following steps: building a road network adjacent matrix; obtaining a Katz similarity matrix according to the adjacent matrix; normalizing the Katz similarity matrix and using a multilayer non-linear automatic encoder to carry out network symptom learning, thus obtaining a network symptom vector; using the network symptom vector to decode and reconstruct the adjacent matrix, and using the reconstructed the adjacent matrix to carry out link prediction.

Description

technical field [0001] The invention relates to the technical field of urban traffic, in particular to a method, system and storage medium for link prediction of urban road networks. Background technique [0002] Link prediction in complex networks refers to the prediction of unknown links or future links in the network. Taking the urban traffic road network (hereinafter referred to as "road network") as the background, the link prediction of the road network is essentially the prediction of the evolution direction of the road network, and it is also the data mining process of the road network topology. The link prediction of the road network has important practical significance for the scientific management and planning of the complex evolution of the urban road network, the improvement of the resource utilization of the road network, and the enhancement of the balance and reliability of the traffic network. [0003] At present, there are two main types of link prediction ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06N3/08G08G1/01
CPCG06N3/08G06Q10/04G08G1/0104
Inventor 盛津芳刘家广孙泽军王斌
Owner CENT SOUTH UNIV
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