Traffic control sub-area clustering and partitioning method based on multi-source data fusion and MILP (Mixed Integer Linear Programming)

Abstract

The invention provides a traffic control sub-area clustering and partitioning method based on multi-source data fusion and MILP (Mixed Integer Linear Programming). The method comprises the following steps of firstly, selecting two types of characteristic parameter data for correlation analysis according to an actual traffic environment, and fusing two types of data on the basis of data normalization to form new combined characteristic parameters; then, using a hierarchical clustering algorithm to generate an ordered sequence capable of representing surrounding traffic flow situations for all road segments; after that, performing primary modeling and software solving based on an MILP thought to obtain a primary partitioning result; and finally, performing secondary MILP modeling and solving for segments that need to be reassigned to obtain a final partitioning result. The traffic control sub-area clustering and partitioning method based on the multi-source data fusion and the MILP provided by the invention is suitable for large urban traffic networks, and a traffic control sub-area partitioning result which is more in line with the actual traffic condition can be obtained based on the combined characteristic parameters of the multi-source data fusion; and meanwhile, an MILP model has good solvability and can obtain a globally optimal traffic control sub-area partitioning result in the effective time.

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 subdivision results, which do not meet actual needs; modeling optimization methods can obtain optimal results, but they are often difficult to solve and cannot be applied to large-scale urban transportation networks

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