Review A review on computational intelligence methods for controlling traffic signal timing

As one of the most important tasks in modern urban life urban transport needs effective and effective solutions. Artificial intelligence law is popular in optimal signal control. This paper reviewed the field of traffic signal timing control, especially the most important research in Q learning, neural network and fuzzy logic system. According to the existing literature, the intelligent method shows higher performance than the conventional control method. However, studies comparing the performance of various learning methods are not published. In this paper, we set the signal time and executed the computational intelligence method and the fixed time method to minimize the total delay of the isolated intersection. Develop and compare these methods on the same platform. Intersections are regarded as smart agents and learn to propose appropriate green times for each phase. Based on the received traffic information, the appropriate blue time for all intelligent controllers is estimated. Comprehensive comparison of performance of neural network and fuzzy logic system control unit for Q learning in two different scenarios In both cases, the three intelligent learning controllers perform high performance with multiple copy instructions. The average Q learning accounted for 66%, the neural network accounted for 71%, and the fuzzy logic improved the performance by 74% compared with the fixed time controller

The traffic signal control device is an electronic device arranged at the intersection of the control light string. In addition to computers, communication devices and detectors for calculating and measuring traffic volume, the control device often controls a number of traffic signals whether it is a city intersection or ramproad close to highways and highways To combine. Detailed brands and device types vary widely, but the functions performed by the system are usually consistent.

As one of the most important tasks in modern urban life urban transport needs effective and effective solutions. Artificial intelligence law is popular in optimal signal control. This paper reviewed the field of traffic signal timing control, especially the most important research in Q learning, neural network and fuzzy logic system. According to the existing literature, the intelligent method shows higher performance than the conventional control method. However, studies comparing the performance of various learning methods are not published. In this paper, we set the signal time and executed the computational intelligence method and the fixed time method to minimize the total delay of the isolated intersection. Develop and compare these methods on the same platform

According to the earliest fixed signal timing and offline delay calculation method proposed by Webster, the traffic signal control system has evolved from fixed time to online control, from point to network control, fixed time to adaptive control. Along with the development of Intelligent Transportation Systems, research on a new generation of traffic control technology based on multi source heterogeneous data has gradually begun. In recent years, signal control research based on a joint infrastructure vehicle system has become a frontier field of traffic control theory and domestic and foreign applications. Research on intelligent connected vehicles focuses on traffic safety optimization methods such as collision warning and lane change support.