Smart Traffic Light System Using Euclidean Distance Algorithm To Improve Traffic Efficiency In Small Villages

Abstract

The traffic management system that exists at traffic intersections on small roads does not have the ability to provide time efficiency in driving and the uncertainty of waiting time at intersections causes drivers to commit traffic violations and decrease productivity. At the intersection of the village area, it was found that vehicle drivers often queued at the intersection but there was no effectiveness in optimizing waiting time according to the volume of vehicles queuing in the next lane because the traffic light system used was still conventional. This research develops a Smart Traffic Light system that can optimize traffic flow at small road intersections on the edge of the city or connecting traffic lanes between villages using the Euclidean Distance method for vehicle density analysis. The system will utilize Infrared (IR) sensors placed at a certain distance to detect the presence of vehicles in every lane. The system can adjust the duration of the green light based on the infrared sensor readings in real-time.  Data from the sensors is analyzed using the Euclidean Distance calculation method to determine traffic density conditions based on three clusters, namely quiet, medium, and crowded conditions. Every first 1-2 infrared sensors read will produce a 10-second green light, 3-4 infrared sensors read will produce a 20-second green light, and 5-6 infrared sensors read will produce a 30-second green light.  Using this method, traffic conditions can be inferred according to the total number of infrared sensors read.