A Control Subarea Division Method Considering the Correlation of Traffic Flow in Road Sections

Abstract

The present invention provides a control sub-area division method considering the correlation of traffic flow in road sections. Firstly, the road network is abstracted and the road adjacency matrix is ​​established, and then the user equilibrium model is used to allocate traffic to the road network and obtain the traffic flow of each road section. , divide the traffic flow of the road section by the actual length of the road section to obtain the pseudo-traffic flow density of each section, establish a similarity model for the pseudo-traffic flow density of the section, take the section as the node, and use the similarity value as the edge weight between each node, by introducing The edge weight parameter improves the traditional Newman community rapid division algorithm, so that it can be applied to the traffic road network with authority, and the traffic control sub-area finally divided is more in line with the needs of traffic control. The division method is simple to use, realizes the optimization of the division of the road network control sub-area from a macro perspective, ensures that the traffic flow in the same control sub-area has good homogeneity, and facilitates the coordinated control within the sub-area and the sub-interval. Theoretically, the maximum capacity of the road network is increased.

Description

technical field [0001] The invention relates to the technical field of traffic road network management, in particular to a method for dividing control sub-areas considering the correlation of road section traffic flow. Background technique [0002] According to the topological structure of the traffic network and the network distribution characteristics of traffic flow, the huge and complex road network is divided into several independent sub-areas, and the effective control of the signals in each sub-area is achieved, which makes the entire road network system become The key to more efficiency, reliability and flexibility. [0003] When dividing the traffic control sub-areas, most of the intersection correlation degree models are used at present, and several intersections with similar traffic characteristics are divided into the same control sub-area by combining various partition algorithms. There are also some partitioning algorithms that take the road section as the clu...

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Problems solved by technology

[0004] However, there are two main disadvantages in the spectral clustering method: one is that the partition results are highly sensitive to parameter values; the other is that the eigenvalues ​​of the solution matrix are required, and for applications with large clustering scales, the pressure of calculation and storage is relatively high

In addition, the clustering-based traffic control area division algorithm, such as the k-means algorithm, also has the following disadvantages: the data to be classified—the number of clusters k, needs to be specified in advance, and in order to determine whether a value of k is appropriate, the user often needs to select several Experiment with different k values; due to the existence of singular value objects, the distribution of data may be distorted to a certain extent. The k-means algorithm is sensitive to noise and cannot solve the problem of data clustering of arbitrary shapes

[0005] To sum up, the existing division methods of traffic control sub-areas have certain limitations.

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