A traffic signal adaptive control system and method

Abstract

A traffic signal adaptive control system and method. Aiming at the complexity of traffic control, fuzzy technology is used to design the reward and punishment mechanism of the learning system, which can reflect the change of traffic state more accurately and reasonably; at the same time, the state division based on experience is used to construct Q The state space of learning, and the complexity of updating the Q-learning state space is reduced under the premise of maintaining multi-parameter evaluation of the traffic state by establishing a traffic parameter fusion function. Real-time responsive control of traffic flow. The present invention can effectively reduce the response time of traffic congestion, quickly coordinate the signal control of each phase, and improve the traffic efficiency of the intersection; due to the characteristics of no model, it has strong self-adaptability and versatility; the system simplifies the parameter index in Q The storage form in the table takes into account the learning effect and response speed of the system to the traffic state, and reduces the complexity of control.

Description

Technical field [0001] The present invention belongs to the technical field of intelligent transportation, particularly, to a signal adaptive traffic control systems and methods. Background technique [0002] Currently, the city traffic is growing rapidly, along with the rapid development of transportation, the urgent need for effective management of intelligent traffic control technology. In recent years, artificial intelligence technology has made great progress in traffic control applications, provides a new technical solution for solving urban traffic control problems. Wherein the real-time adaptive control system can adjust the characteristics of the control target variable traffic managers and the intersection with the parameters when compared to an induction timing and control may be better using the whole capacity of the road network, improve the efficiency of the road network It is a city of effective means to solve traffic congestion. [0003] It is understood the appli...

AI Technical Summary

Problems solved by technology

[0004] However, there are still some deficiencies in the application of Q-learning algorithm in traffic control

The existing Q-learning intersection traffic adaptive control method does not make full use of various traffic state parameters in complex traffic conditions, and it is difficult to give accurate and reasonable feedback for changes in traffic flow; and for the variable-period Q-learning adaptive control , its state space is too large, resulting in low learning efficiency and difficult to form effective control

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