Road network node importance evaluation method based on knowledge graph

A technology of knowledge graph and importance, which is applied in the field of evaluating the importance of nodes in the road network, and can solve problems such as reduced efficiency and limited data volume

Active Publication Date: 2020-05-15
DALIAN UNIV OF TECH
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Problems solved by technology

This method can consider various influencing factors of traffic from many aspects, but because it is essentially a data-driven method, it is still limited by the bottleneck of data volume, and when the amount of data is insufficient, the efficiency of the data-driven method will drop significantly

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  • Road network node importance evaluation method based on knowledge graph
  • Road network node importance evaluation method based on knowledge graph
  • Road network node importance evaluation method based on knowledge graph

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Embodiment Construction

[0052] The implementation method of the present invention will be described in detail below.

[0053] A method for predicting the importance of road network nodes based on graph neural network, such as figure 1 , the method is mainly divided into two modules, which are the scoring module based on the congestion propagation model and the evaluation module of the importance of road network nodes based on the graph network. The first layer is the feature propagation layer based on the graph attention network, and the third layer is the link-trajectory score adjustment layer. The implementation methods of the two modules are:

[0054] (1) Scoring module based on congestion propagation model

[0055] The main goal of this module is to score road segments with sufficient data in the road network. The definition of important nodes in the present invention can be reflected from this module, and since other nodes are predicted according to the results obtained by this module, this m...

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Abstract

The invention discloses a road network node importance evaluation method based on a knowledge graph, and belongs to the field of intersection of a graph neural network and an urban traffic planning technology. By means of the technology, the problem that importance evaluation cannot be conducted on all nodes in a road network due to the fact that data in the road network are not comprehensive enough can be solved. According to the method, firstly, frequently congested road sections and congested propagation modes thereof in a road network are mined from existing trajectory data, and each roadsection is scored according to a congested propagation probability graph of each road section; secondly, a traffic knowledge graph of a corresponding region is constructed, a graph neural network-based method is used on the knowledge graph, scores and features of known nodes are propagated, and importance of the nodes is adjusted by using trajectory data, so that importance of other nodes in the road network is predicted.

Description

technical field [0001] The invention belongs to the field where a graph neural network intersects with urban traffic planning technology, and relates to a method for evaluating the importance of nodes in a road network in a traffic knowledge map based on a graph neural network. Background technique [0002] While the rapid development of transportation has brought convenience to people, there are still many problems that cannot be ignored. The problem of people's livelihood, travel, has become a prominent social problem, and the main cause of these problems is traffic. A series of problems caused by congestion in the Congestion will lead to a series of problems such as the increase of travel time and the deterioration of the ecological environment, and it will also become the bottleneck of urban development. Therefore, it is imminent to solve the congestion problem. Although there are many methods in the transportation field to solve this problem, because the transportatio...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06F16/36G06N3/04G06N3/08
CPCG06Q10/06393G06F16/367G06N3/04G06N3/08Y02T10/40
Inventor 王璐齐恒申彦明
Owner DALIAN UNIV OF TECH
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