Smart Toll Collection: Study of AI-Driven Approaches

Abstract

Smart Toll Collection systems are transforming conventional toll operations by addressing challenges such as traffic congestion, long queues, manual errors, and high operational costs. This survey paper reviews AI-Driven approaches that integrate real-time license plate detection using deep learning models like YOLO, Optical Character Recognition (OCR) techniques such as Tesseract and EasyOCR for vehicle identification, and IoT-based automation for seamless toll processing. These systems leverage edge computing platforms such as Raspberry-Pi to enable real-time image processing, database verification, automated payment deduction, and intelligent gate control using sensors and actuators. By minimizing human intervention and enhancing accuracy under varying environmental conditions, AI-Driven toll collection frameworks significantly improve efficiency, transparency, and scalability within modern intelligent transportation systems.