AI-Driven Vehicle Classification Framework for Modern Electronic Toll Collection

Abstract

This paper presents an artificial intelligence and machine learning-based framework for achieving highly accurate vehicle classification in high-speed electronic toll systems. The proposed Adaptive Vehicle Classification System (AVCS) addresses critical operational challenges where misclassification can lead to revenue loss, customer disputes, and inefficiencies. Leveraging advanced deep learning models, the system processes high-throughput traffic data to classify vehicles into precise categories for differential toll pricing, even under diverse environmental conditions and peak traffic flows. A novel adaptive learning algorithm continuously refines classification performance by analyzing historical traffic patterns, vehicle speed, and operational context, enabling real-time adjustments without manual intervention. The methodology incorporates preprocessing techniques to handle adverse weather, low-light conditions, and occlusions, while supporting simultaneous multi-lane processing with individual vehicle tracking. Experimental evaluation across multiple highway sites demonstrates classification accuracy exceeding 99%, with substantial reductions in operational errors, customer disputes, and revenue leakage. The system integrates seamlessly with existing toll infrastructure, offering scalable deployment, auditing capabilities, and continuous learning for evolving vehicle types and traffic patterns.