Signal lamp control method and system based on deep intensive learning and storage medium

Abstract

The invention relates to an intelligent traffic lamp control method based on deep intensive learning. The method comprises the steps of selecting a center intersection, wherein multiple peripheral intersections communicated with the center intersection are arranged at the periphery of the center intersection; obtaining road condition information and signal lamp information of each intersection; building an intersection congestion and unblocking state model; modelling a traffic signal lamp control problem into a Markov decision-making process, and defining the state, motion and an immediate awarding function in the process; building a return value function model, utilizing a DQN deep intensive learning algorithm for solving an optimum strategy, and utilizing the optimum strategy for controlling traffic lights of all the intersections. By means of the method, a control strategy of the traffic lights can be self-adaptively and dynamically adjusted according to the real-time road conditioninformation. In the meanwhile, multiple intersections are synchronously adjusted, and full play can be given to the traffic capability of all the intersections.

Description

technical field [0001] The invention relates to the field of signal lamp control, in particular to a signal lamp control method, system and storage medium based on deep reinforcement learning. Background technique [0002] At the beginning of the 20th century, the first traffic lights activated by electricity appeared in the United States. In the following time, the technology of traffic lights continued to develop. Its appearance enabled traffic to be effectively controlled. The accident had a clear effect. [0003] With the rapid development of society and rapid economic growth, people's living conditions have become more favorable, and cars have basically spread to every family. This undoubtedly increases the transportation pressure on urban roads and makes urban roads congested, especially at intersections. Because the traditional traffic signal light system cannot adapt to complex and changeable road conditions in time, it often leads to congestion at intersections and...

AI Technical Summary

Problems solved by technology

[0006] Based on this, it is necessary to provide an intelligent traffic light control method based on deep reinforcement learning to solve the problem of poor adaptive adjustment ability of traditional signal light control methods.

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