Traffic Control System Based on Neural Network

The advantages of the proposed system over competitors are the use of the latest development technologies in this field, which have already proven their superiority not only in experimental conditions, but also in real examples. The proposed system uses a neural network that uses the principle of reinforcement learning. It is proposed to take the Advantage Actor-Critic (A2C) algorithm as a basis. It is an optimization of the Deep Q-Learning (DQN) algorithm that performs just as well, but reduces the time required for computation/training by incorporating parallelism in Reinforcement Learning. The chosen method is quickly learned, which allows for quick implementation and practical application. A separate advantage can be considered easy implementation with already working systems, such as “Safe City”. To do this, you only need to get the current road map through the API. One of the main advantages of the system is the use of the best algorithm for calculating traffic light phases, which was experimentally confirmed.