An mfd-based iterative learning control method for road network traffic signals

Abstract

In view of the large urban traffic volume, large city scale, complex structure and other characteristics of China, the present invention provides a road network traffic signal iterative learning control method based on MFD. The road network traffic signal iterative learning control method based on MFD includes the steps: S1, 1.1) acquiring traffic data of a subarea MFD; 1.2) performing subarea MFDfitting; and 1.3) determining the ideal occupancy of roads, based on MFD; and S2, 2.1) performing an opened and closed loop iterative learning control strategy; 2.2) establishing a state space equation; and 2.3) optimizing signal time assignment of each intersection. The road network traffic signal iterative learning control method based on MFD enables the overall structure of the road network toachieve relative balance, can improve the outflow vehicles of the subarea so as to improve the traffic volume of the road network, provides an effective urban road network control means for the traffic manager, and improves the traffic service level of the urban road network.

Description

technical field [0001] The invention relates to the traffic signal control problem of urban road network, in particular to MFD (traffic macro basic diagram) and an iterative learning control strategy. Background technique [0002] Due to the limitation of road resources and infrastructure, traffic congestion in modern cities is still one of the major problems in society. Signal control, as the most important means of traffic control, has been greatly developed with the continuous in-depth research of traffic scholars. [0003] Since the urban traffic system is an uncertain complex system with a large scale and difficult to determine the parameters of the system model, N. Geroliminis et al. found that there is a specific pattern in the cumulative number of vehicles and traffic flow in the urban area through the analysis of traffic data in Yokohama, Japan. On this basis, MFD (Macro Fundamental Diagram) is proposed, which avoids the defects in the traffic flow modeling and ana...

AI Technical Summary

Problems solved by technology

[0005] The present invention aims to overcome the disadvantages of large cities, complex structures, and difficulty in modeling in traditional ways, and proposes an iterative learning signal control based on a layered control structure to balance vehicles in the road network and make the road network in a macroscopic basic map The optimal operating state of the road network, thereby increasing the outflow of vehicles on the road network and improving the traffic capacity of the road network

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