Real-Time Monitoring of On-Board Unit Status in Highway Electronic Toll Collection Systems Using Graphsage-Based Heterogeneous Graph Learning

Abstract

The reliable operation of on-board unit (OBU) in electronic toll collection (ETC) systems is critical for maintaining transaction accuracy and preventing revenue loss. However, real-time monitoring of OBU status faces challenges such as technological obsolescence, environmental vulnerabilities, and data inconsistencies. This study proposes a novel GraphSAGE-based approach for real-time OBU status monitoring. First, we establish a classification standard for OBU operating status based on missing data patterns, enabling precise identification of abnormal states. Second, we design a real-time data warehouse architecture tailored to the characteristics of ETC transaction data, ensuring efficient data processing and storage. Third, we use the GraphSAGE model to monitor OBU status in real-time, leveraging heterogeneous graph learning to capture both temporal and structural dependencies in the data. The experimental results demonstrate the effectiveness of the proposed approach, achieving a true positive rate of 99.8% and a false positive rate of 0.2% across various performance metrics, including accuracy, precision, recall, and F1-score. The proposed method outperforms existing models, such as graph convolutional network, GAT, and XGBoost, in real-time monitoring tasks, showcasing its stability and generalization ability under different data volumes. This study provides a comprehensive framework for improving OBU condition monitoring, contributing to enhanced maintenance strategies and more effective detection of fee evasion by regulatory authorities.