A Reinforcement Learning Variable Duration Signal Light Control Method Based on IoT Devices

Abstract

The invention proposes a reinforcement learning variable-duration signal light control method based on the Internet of Things equipment, which mainly includes the following aspects: a reinforcement learning method based on the concept of "intensity" of the intersection is designed, and the data collected by the Internet of Things equipment is collected. Various real-time traffic information (such as vehicle position, speed, etc.) to control the phase selection of signal lights. At the same time, a most reasonable green light duration can be selected according to the number of vehicles in each lane. The invention can quickly converge to an excellent signal light control strategy under the condition of dynamic traffic changes, greatly shorten the learning time of the strategy and improve the control quality of the strategy.

Description

technical field

[0001] The invention belongs to the field of computer technology, relates to deep reinforcement learning algorithms and signal light control problems, and particularly relates to learning and generating an effective signal light control strategy according to real-time traffic data obtainable by Internet of Things devices in a highly complex real-time traffic environment. Background technique

[0002] In recent years, with the rapid increase of car ownership in our country, more and more road traffic problems have appeared frequently, such as traffic planning problems, road safety problems, road congestion problems, traffic control problems and so on. Traffic congestion has always been a key issue in designing efficient infrastructure, but it has become a prominent problem due to the rapid growth in traffic demand. In addition, traffic congestion has also brought about a series of problems such as traffic environment pollution and traffic chaos, which have ser...

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