###### Abstract  
With the wide application of Electronic Toll Collection (ETC) systems, the effectiveness of the operation and maintenance of gantry equipment still need to be improved. This paper proposes a dynamic anomaly detection method for gantry transactions, utilizing the contextual attention mechanism and Graph Convolutional Network-Gate Recurrent Unit (GCN-GRU) dynamic anomaly detection method for gantry transactions. In this paper, four different classes of gantry anomalies are defined and modeled, representing gantries as nodes and the connectivity between gantries as edges. First, the spatial distribution of highway ETC gantries is modeled using the GCN model to extract gantry node features. Then, the contextual attention mechanism is utilized to capture the recent patterns of the dynamic transaction graph of the gantries, and the GRU model is used to extract the time-series characteristics of the gantry nodes to dynamically update the gantry leakage. Our model is evaluated on several experimental datasets and compared with other commonly used anomaly detection methods. The experimental results show that our model outperforms other anomaly detection models in terms of accuracy, precision, and other evaluation values of 99%, proving its effectiveness and robustness. This model has a wide application potential in real gantry detection and management. ETC gantry; dynamic anomaly detection; GCN-GRU; attention mechanism; graph data aca 2023 13 11068 11068 11068 11068 11068 11068 11068 11068 11068 11068 11068 11068 11068 11068 11068 11068 11068 11068 11068 11068 11068 11068 11068 11068 111068 11068 11068 11068 11068 11068 11068 11068 11068 1106 on the intelligent operation and maintenance of ETC gantries. [PERSON] et al. [8] improved and optimized the modules of the system control cabinet. This provides a safe and stable operating environment for the ETC gantry system in terms of front-end sensing, storage, power supply, and communication. [PERSON] et al. [9] briefly described intelligent detection and operation and maintenance, focusing on two aspects, big data fusion analysis and detection, and operation and maintenance full life-cycle management, in order to improve the detection, operation, and maintenance business capacity of highways. [PERSON] et al. [10] developed a toll system key power, network cloud detection, and warning system; the system can raise alarms for abnormalities caused by the interruption of the gantry cable or the tripping of any switch. The operation and maintenance of the gantry system presented above are based on the indicators of the hardware equipment, which are to detect faults and then realize the operation and maintenance management of the gantry. However, this method is rather one-sided and only analyzes the local anomalies in the system power and network. Moreover, most use experience and theory to design the operation and maintenance system, which have poor reliability and cannot effectively meet the needs of gantry operation and maintenance in a timely manner. Therefore, this paper summarizes the research work in the field of equipment anomaly detection in recent years and, on this basis, combines the characteristics of highway ETC transaction data, and proposes a highway ETC gantry anomaly dynamic detection method based on the combination of spatio-temporal characteristics. The main contributions are specified as follows:  
* The paper proposes, for the first time, the use of gantry transaction data for gantry anomaly detection, which provides a comprehensive assessment of gantry anomalies at the macro-level and defines four categories of gantry anomalies of different grades, and this categorization system allows for the continuous observation of potentially anomalous risky gantries. * The method uses a combination of a Graph Convolutional Network (GCN), contextual attention mechanism, and Gate Recurrent Unit (GRU) to not only consider the spatial distribution of gantries, but also capture the temporal dynamic properties of gantry transaction data.