Traffic signal self-adaptive control method based on deep reinforcement learning

Abstract

The invention relates to the technical field of traffic control and artificial intelligence and provides a traffic signal self-adaptive control method based on deep reinforcement learning. The method includes the following steps that 1, a traffic signal control agent, a state space S, a motion space A and a return function r are defined; 2, a deep neutral network is pre-trained; 3, the neutral network is trained through a deep reinforcement learning method; 4, traffic signal control is carried out according to the trained deep neutral network. By preprocessing traffic data acquired by magnetic induction, video, an RFID, vehicle internet and the like, low-layer expression of the traffic state containing vehicle position information is obtained; then the traffic state is perceived through a multilayer perceptron of deep learning, and high-layer abstract features of the current traffic state are obtained; on the basis, a proper timing plan is selected according to the high-layer abstract features of the current traffic state through the decision making capacity of reinforcement learning, self-adaptive control of traffic signals is achieved, the vehicle travel time is shortened accordingly, and safe, smooth, orderly and efficient operation of traffic is guaranteed.

Description

Technical field [0001] The invention relates to a traffic signal adaptive control method based on deep reinforcement learning, and belongs to the technical field of traffic control and artificial intelligence. Background technique [0002] With the increase in car ownership, traffic congestion has become a problem that plagues my country’s economic development. In 2016, the per capita economic loss caused by traffic congestion reached 8,000 yuan. At the same time, it also brings energy issues, traffic safety issues and environmental issues. There are two options for solving traffic congestion, increasing the construction of traffic infrastructure and developing advanced traffic control systems. However, urban land resources are limited and the problem cannot be solved by simply increasing the transportation infrastructure. Therefore, more attention should be paid to the development of advanced traffic control systems. Currently, mainstream traffic control systems include TRANSY...

AI Technical Summary

Problems solved by technology

On this basis, an effective traffic signal control strategy is formulated through reinforcement learning, which solves the shortcomings of the traditional traffic signal control system, such as incomplete perception of traffic conditions and inaccurate formulation of signal control strategies, and realizes adaptive control of traffic signals at intersections.

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