Abstract
In order to optimize the existing control subarea division methods, a new dynamic subarea division method based on node importance evaluating is proposed for regional coordinated control. Firstly, considering the characteristics of road network and traffic flow between adjacent intersections, correlation degree model is established by calculating the correlation coefficients of traffic flow, signal cycle, and traffic state. Then, road traffic network is abstracted into a network topology structure graph. From the global perspective of the road network, intersection position information and the importance contribution between adjacent intersections are taken into account, and the correlation degrees between adjacent intersections are taken as the edge weights to construct intersection importance evaluation matrix. Finally, an actual road network is selected, and an improved Newman algorithm is employed to verify and analyze the method proposed in this paper. Results show that, compared with other methods, the control subarea division result of the new proposed method is more elaborate and more in line with the actual traffic flow characteristics. Moreover, dynamic subarea division can be realized according to the traffic characteristics of different periods, which can provide a good basis for the formulation of the signal control schemes in the next stage.
Highlights
In recent years, there have been many researches in the field of dynamic control subarea division
E main objective of this paper is to propose a new dynamic control subarea division method based on node importance evaluating, combined with improved Newman algorithm. e novelty of the proposed method is highlighted in the following aspects: (1) e correlation degree model of adjacent intersections is established by adding traffic flow correlation coefficient, signal cycle correlation coefficient, and traffic state correlation coefficient together
Using the idea of graph theory, the intersections in the urban road traffic network can be abstracted as points, and the road sections between adjacent intersections can be abstracted as edges. en, the road network can be abstracted as the network topology structure graph composed of nodes and edges. erefore, the importance evaluation of intersections can be transformed into importance evaluation of nodes in complex networks
Summary
As it is known that the factors affecting the relevance of adjacent intersections mainly include the distance between adjacent intersections, traffic volume, signal timing parameters, and traffic states, the distance between the adjacent intersections is a static influencing factor, of which the influence degree on intersection relevance remains unchanged. Where Iρ(i↔j) denotes traffic state correlation coefficient of road section between intersection i and intersection j. Ρ(i⟶j) (pcu/m) represents the traffic flow density of the road section from intersection i to intersection j, which can be calculated by formula (6). The correlation degree Iij of adjacent intersections can be obtained by comprehensively considering the traffic flow correlation coefficient, signal cycle correlation coefficient, and road traffic state correlation coefficient and adding them together, as shown in the following formula: Iij αIq(i↔j) + βIC(i,j) + cIρ(i↔j),. Where Iq(i↔j) represents the traffic flow correlation coefficient between intersection i and intersection j, which can be obtained by formula (3). Iρ(i↔j) denotes road traffic state correlation coefficient, which can be calculated by formulas (5) and (6). The greater the information entropy of an index, the smaller the weight
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