基于异构图学习的高速公路电子收费系统车载单元状态实时监控

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Qiang Ren, Chengmingchan Yan, Fumin Zou, Yue Xing, Haolin Wang, Ying Zhang
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引用次数: 0

摘要

电子收费(ETC)系统中车载单元(OBU)的可靠运行对于保持交易的准确性和防止收入损失至关重要。然而,OBU状态的实时监测面临着技术过时、环境脆弱性和数据不一致等挑战。本研究提出了一种新的基于graphsage的OBU状态实时监测方法。首先,基于缺失数据模式建立OBU运行状态分类标准,实现异常状态的精确识别。其次,针对ETC交易数据的特点,设计了实时数据仓库架构,保证了数据的高效处理和存储。第三,我们使用GraphSAGE模型实时监控OBU状态,利用异构图学习来捕获数据中的时间和结构依赖关系。实验结果证明了该方法的有效性,在准确率、精密度、召回率和f1分数等各种性能指标上,真阳性率为99.8%,假阳性率为0.2%。在实时监控任务中,该方法优于现有的图卷积网络、GAT、XGBoost等模型,在不同数据量下具有稳定性和泛化能力。本研究为改善OBU状态监测提供了一个全面的框架,有助于加强维护策略,并有助于监管当局更有效地发现逃税行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-Time Monitoring of On-Board Unit Status in Highway Electronic Toll Collection Systems Using Graphsage-Based Heterogeneous Graph Learning

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.

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来源期刊
Concurrency and Computation-Practice & Experience
Concurrency and Computation-Practice & Experience 工程技术-计算机:理论方法
CiteScore
5.00
自引率
10.00%
发文量
664
审稿时长
9.6 months
期刊介绍: Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing.
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