Urban traffic control sub-area division method based on fast global K-means spectral clustering

Abstract

The invention discloses an urban traffic control sub-area division method based on fast global K-means spectral clustering, and relates the field of the urban traffic sub-area division. The basic thought thereof is taking traffic correlation between adjacent signal intersections in a road network as a division evidence, the method comprises the following steps: 1) selecting a to-be-researched roadnetwork to acquire a road topology structure and traffic flow data of the road network, and establishing a road network undirected graph; 2) computing a road network density Laplace matrix L and feature vectors corresponding to front k minimum feature values to construct a clustering feature matrix; 3) implementing the global K-means clustering algorithm on the clustering feature matrix X, and dividing the intersections in the same cluster into the same sub-area; and 4) judging whether the amount (absolute value of Vi) of the intersection in the sub-area after the clustering satisfies a constraint condition, performing boundary adjusting on the sub-area dissatisfying the constraint condition , and performing traffic control sub-area division on the road network G by adopting the fast global K-means spectral clustering. The time required by the clustering is shortened, and the traffic sub-area division efficiency can be improved.

Description

technical field [0001] The invention relates to the field of division of urban traffic sub-districts, in particular to a division method of urban traffic control sub-districts based on fast global K-means spectrum clustering. Background technique [0002] With the development of society and economy, the number of motor vehicles in cities is increasing year by year, resulting in frequent traffic jams at some signalized intersections in the urban road network during peak hours, resulting in a corresponding increase in average travel time, and may also affect traffic related to it. Congestion in adjacent road sections or intersections leads to traffic paralysis, which greatly affects people's travel and life. Due to different geographical locations and special traffic characteristics, the urban road network presents a large number and complex structure. It is difficult and unreasonable to implement signal coordination control on the road network in the entire region. Existing r...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem that the existing urban traffic control sub-area division is inefficient and cannot achieve the best clustering results, the present invention provides a division method of urban traffic control sub-area based on fast global K-means spectral clustering

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