Urban road dynamic traffic network structure information system

Abstract

The invention provides an urban road dynamic traffic network structure information system. According to the system, characteristics of division of a road network are taken into consideration, the whole traffic road network is divided into multiple network subareas on the basis of the unique characteristics of the network, structure information of the different network subareas is calculated through an information entropy formula of Shannon by utilizing the network, the number of edges of the respective subareas and the weight-containing degree of access, the degree of access weight between onesubarea and the adjacent subarea is added by taking consideration of connections among the subareas, and then structure information containing subarea boundary characteristics is calculated. Therefore, dynamic analysis is carried out on an urban road network on the basis of the subarea structure information and the structure information containing subarea boundary characteristics.

Description

technical field [0001] The invention relates to the evaluation field of dynamic and complex traffic network structure information of urban roads. Background technique [0002] The structural information characteristics of urban traffic road network traffic flow distribution is an important factor affecting urban traffic. The topology of the road network determines many properties of the road network, and the structural information of the road network is a physical quantity that can measure the distribution of traffic network traffic. The more chaotic the network distribution, the higher its structural information entropy. The current dynamic and complex network analysis methods mainly use previous classical models, such as the early ER classical model, WS small-world model, BA scale-free model, first forest model, stochastic model and adjacency model. But real networks are complex and cannot be captured by existing models. Existing models only consider a certain kind of ne...

AI Technical Summary

Problems solved by technology

But real networks are complex and cannot be captured by existing models

Existing models only consider a certain type of network characteristics, and lack of consideration of the importance of the characteristics obtained by considering the boundary effects of each partition and between partitions in the entire network

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