Modeling and Controlling Smart Traffic Light System Using a Rule Based System

Abstract

Traffic congestion is a problem on street network that happens as automobiles upsurge. This problem is categorized by slower speeds, longer trip times, and increased vehicular queuing. It has an effects on the economic growth of a country, upsurges accidents, resource cost and environment pollution. One of the most real ways to deal with this problem is by using traffic control signals at intersections. Nowadays, most signal controls are implemented with static cycle time control or manual control. These traditional methods for traffic signal control fail to deal efficiently with situations of traffic congestion. Therefore, this study takes benefit of one of the Artificial Intelligence (AI) fields which is Expert System (ES) and which can be called Rule-Based System (RBS). The researchers designed a new expert system called Traffic Lights Expert System (TLES). TLES uses rule base as the knowledge representation and the evidential reasoning as the inference engine. This system can allocate the suitable dynamic cycle time at the intersections. The system is connected to the hardware design to control the traffic lights and monitor congestion levels at the intersection using Arduino and Infrared Radiation (IR) sensors.