Traffic signal lamp control method and system based on reinforcement learning

Abstract

The invention discloses a traffic signal lamp control method and system based on reinforcement learning, and the method comprises the steps: firstly building a signal lamp control Agent model, then building a road and road intersection model, enabling the road traffic state information to be represented as a speed, position and current signal lamp state matrix, then, on the basis of traditional Q-Learning, establishing a traffic signal lamp control algorithm based on Deep Q Network (DQN) according to the road environment information; and finally, obtaining real-time road condition information through interaction between the Agent and the environment, performing search self-learning in a behavior space, estimating Q values for executing all possible actions in the current state, and selecting the action with the larger Q to be executed by utilizing an epsilon-greedy strategy. An existing traffic signal lamp control method is improved, the waiting time of vehicles at the intersection is minimized, the effective green light time of a signal control period is maximized, the vehicles are assisted to rapidly pass through the intersection, the passing time is shortest, meanwhile, the maximum traffic flow exists at the intersection, and thus the purpose of relieving traffic jam is achieved, and adaptive control of the traffic signal lamp is realized.

Description

technical field [0001] The invention relates to the technical field of intelligent traffic control, in particular to a traffic signal light control method and system based on reinforcement learning. Background technique [0002] With the rapid growth of the number of motor vehicles, the carrying capacity of urban road traffic is obviously insufficient, and the problem of traffic congestion has become increasingly prominent. In the urban road traffic system, the traffic flow at each intersection is interrelated and affected, and the traffic congestion at a certain phase of any intersection will cause the adjacent phase to also be congested, and then lead to adjacent intersections and Areas can also be congested. In recent years, traffic congestion has attracted more and more people's attention. There are two main ways to alleviate traffic congestion: one is to strengthen infrastructure construction, such as improving the traffic capacity of the road network, expanding bridge...

AI Technical Summary

Problems solved by technology

[0005] In view of the fact that the existing adaptive traffic signal light control method and system rely heavily on historical data and models and have large human intervention factors, which are not real adaptive control problems, the present invention proposes an adaptive traffic signal light control method and system based on reinforcement learning

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