Clustering method of traffic control sub-areas based on multi-source data fusion and snmf

Abstract

Based on multi-source data fusion and SNMF traffic control sub-area clustering method, first, according to the actual traffic environment, select two kinds of characteristic parameter data for correlation analysis, and on the basis of data normalization processing, the two kinds of characteristic parameters The data is processed by linear weighted data fusion to obtain new combined feature parameters; then, according to the combined feature parameters, a hierarchical clustering algorithm is used to generate a “snake” array for all road sections that can represent the surrounding traffic flow situation; finally, the calculation of all road sections Similarity matrix, and SNMF optimization solution to obtain the final traffic control sub-area clustering division results. The present invention is suitable for small and medium-sized urban traffic networks. Based on the combined characteristic parameters of multi-source data fusion, traffic control sub-area division results that are more in line with actual traffic conditions can be obtained. At the same time, the good decomposition characteristics of SNMF also avoid the existing clustering division methods lack of.

Description

technical field [0001] The invention relates to a traffic control sub-area division method for intelligent traffic signal control. The traffic control sub-area is used for arterial coordinated control and regional coordinated control of urban traffic signals. Background technique [0002] In urban traffic signal control, coordinated control can effectively improve the traffic efficiency of the entire system, reduce parking delays and travel time per vehicle. However, in the urban traffic road network, there are different degrees of differences in the dynamic traffic flow characteristics of each intersection and road section. If it is used as the same area to implement a unified control strategy, it will not achieve a good control effect, or even Aggravate congestion or cause traffic accidents. The division of traffic control sub-areas is mainly to divide adjacent intersections or road sections into several traffic control sub-areas for coordinated control. It is the coordin...

AI Technical Summary

Problems solved by technology

[0005] At present, in practical applications, the existing traffic control sub-area clustering division methods have the following main problems: 1) Most methods use characteristic parameters from a single source, which cannot accurately and comprehensively represent the traffic flow situation, resulting in inaccurate division results. It meets the actual needs; 2) Although a few methods use multi-source characteristic parameters, there are problems such as data collection difficulties or large collection errors; 3) Traditional clustering methods (that is, the first type of methods) have many deficiencies

K-means clustering has problems such as difficulty in selecting the initial cluster center and inaccurate division, while the effect of spectral clustering is overly dependent on the eigenvalues ​​of the Laplacian matrix, and non-negative matrix decomposition requires the data to have a good linear structure;4 ) Other clustering methods (that is, the second method) also have certain defects

Heuristic algorithms often can only obtain locally optimal subregion division results. Although modeling optimization methods can obtain optimal results, the calculation is very time-consuming

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