Intelligent Traffic Signal Control System Based on Machine Learning Algorithm

Abstract

Urban traffic signal control is an important part of modern intelligent transportation system. Traffic lights play an important role in regulating traffic flow at intersections and major road sections. How to use advanced control technology to achieve reasonable time allocation optimization of traffic lights, maximize the use of traffic time for traffic lights, and ensure smooth roads is one of the key contents of intelligent transportation research. This paper aims to study the intelligent traffic signal control system (ITSCS) based on machine learning algorithm. This paper analyzes the support vector machine algorithm in the machine learning algorithm, and analyzes the advantages and disadvantages of the support vector machine algorithm. It also analyzes the hardware structure of the ITSCS and the communication process between the devices of the ITSCS. Finally, the experimental results are obtained. The empirical analysis results show that the traditional ITSCS becomes more concise and convenient after the machine learning algorithm is adopted, and we conducted a questionnaire and found that more than 87% of the people feel that the design of the ITSCS applies machine learning algorithm, and found that the design efficiency has become higher.