Intelligent Toll Collection System: A Comprehensive Review

Intelligent Toll Collection System: A Comprehensive Review

Rajasree RS

Senior Assistant Professor  Dept of Artificial Intelligence and Machine Learning New Horizon College of Engineering, Bengaluru, India.

rajasreecse@gmail.com

Sravan OK

Dept of Artificial Intelligence and Machine Learning New Horizon College of Engineering, Bengaluru, India.

sravanok2003@gmail.com

Reshab Vasanth

Dept of Artificial Intelligence and Machine Learning New Horizon College of Engineering, Bengaluru, India.

 reshabchotu2@gmail.com

Abstract—This paper presents an enhanced Intelligent Toll Collection System (ITCS) integrating RFID and ANPR technologies to address traffic congestion and operational inefficiencies in conventional toll collection. We propose a novel multi-stage image preprocessing pipeline combined with adaptive thresholding techniques, achieving a 95.8% plate detection rate and mean confidence score of 0.85. Our implementation demonstrates significant performance improvements with average processing times of 0.8 seconds per vehicle and sustained accuracy rates of 92% in daylight conditions. The system maintains robust performance across varying environmental conditions through an optimized bilateral filtering approach (11, 17, 17) and specialized morphological operations. Experimental results show substantial reductions in processing time and enhanced accuracy compared to traditional methods, while maintaining scalability for high-traffic deployments. This implementation advances the field of intelligent transportation systems, providing a foundation for future smart city infrastructure development.

Keywords—Intelligent Toll Collection System (ITCS), Automatic Number Plate Recognition (ANPR), Traffic Management, Computer Vision, Machine Learning