Complex weighted traffic network key node identification method based on semi-local centrality

Abstract

The invention provides a complex weighted traffic network key node identification method based on semi-local centrality, comprising the following steps: S1, constructing a complex weighted traffic network: constructing a traffic network by adopting an original method, and taking road sections as nodes and road sections as edges; generating a corresponding adjacent matrix; according to the road grade, obtaining a weighted adjacency matrix through the adjacency matrix; S2, processing the weighted adjacency matrix, and analyzing network characteristics: calculating degree distribution of nodes, calculating an average clustering coefficient, and calculating an average path length; analyzing network characteristics according to the degree distribution of the nodes, the average clustering coefficient and the average path length; S3, identifying key nodes by adopting a semi-local centrality algorithm; and S4, sorting the key nodes of the traffic network: sorting the nodes in a descending order according to the importance degree to obtain the key nodes in the traffic network. The road grade is used as the weight, and the semi-local centrality algorithm is adopted, so that the problems thatthe key node identification calculation complexity of the existing traffic network is high and the traffic network characteristics are not considered are solved.

Description

technical field [0001] The invention belongs to the technical field of identification of key nodes of traffic networks, and in particular relates to a key node identification method of complex weighted traffic networks based on semi-local centrality. Background technique [0002] With the substantial increase in the number of vehicles, the urban traffic system is becoming larger and more complex, and urban traffic problems are becoming more and more serious. Studies have shown that the traffic network has small-world characteristics or scale-free characteristics, and the traffic network has the structure and function of a complex network. Using complex network theory to study urban transportation network is of great significance to improve its accessibility and operational efficiency. The determination of transportation hubs or key nodes in urban transportation network is one of the research topics on the complexity of urban transportation network. The position of the nodes...

AI Technical Summary

Problems solved by technology

At present, the identification of key nodes in the transportation network mainly has the following problems: (1) The unique traffic characteristics of the transportation network are not considered, and the identified results are quite different from the actual ones; (2) The identification of key nodes is mainly concentrated in the unauthorized network. The weight plays an important role in the performance of the network. The weighted traffic network can more accurately describe the actual complex network, and can better reflect the role of nodes on the network; (3) Key node identification algorithms based on global centrality, such as node betweenness, Katz centrality and other complexities are high and cannot be applied to large-scale transportation networks

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