Evaluation of road traffic congestion using fuzzy techniques
Panita Pongpaibool, Poj Tangamchit, Kanokchai Noodwong · 2007
This paper presents a road-traffic evaluation system from image processing data using manually tuned fuzzy logic and adaptive neuro-fuzzy techniques. The system is designed to emulate human's expertise on specifying three levels of traffic congestion within Bangkok Metropolitan Area. The traffic information comes from a vehicle detection and tracking software, which takes a road-traffic video signal as an input and computes vehicle volume and velocity. We verify accuracy of our system by comparing outputs of the system with opinions of volunteers who watch the same traffic video. Results show that manually tuned fuzzy logic achieves 88.79% accuracy, while the adaptive neuro-fuzzy technique achieves only 75.43% accuracy.