Traffic Congestion Monitoring Approach For a Targeted Area Using Blob Analysis with ThingSpeak

Abstract

Congestion problem is becoming a major problem in many developing countries due to the high populations and uncontrolled on the road vehicles issues. The conventional systems for traffic control mostly apply sensors and timer. Initiatives has been taken including using an under-loop sensor, fuzzy logic and also Artificial Intelligence (AI) to solve the issue. The main objectives of this project are to detect and count the vehicles using an image processing technique. AI method is implemented in the traffic light system. The AI system is very precise in detecting the vehicles in the traffic. Besides, the data of the traffic condition can be stored and monitored by using the ThingSpeak in a real-time. The result shows the system can control the traffic light condition by turning the priority lane to green and also monitoring the traffic condition in the ThingSpeak. As the result, the transportation expenses, time, and reduce air pollution can be reduced.