A Fast-Efficient Anomaly Detection Framework for State Estimation in Traffic Flow Measurement

In intelligent transportation systems (ITS), machine learning is highly effective in anomaly detection for state estimation (SE) in traffic flow measurement, but it requires resource-intensive collection of anomaly samples and overlooks spatial characteristics. Additionally, the original SE in traffic flow measurement exhibits low-frequency characteristics due to computational complexity. Considering these limitations, this paper proposes a fast and efficient anomaly detection framework for SE in traffic flow measurement. Initially, an attention-based spatiotemporal graph convolutional network (ASTGCN) model is utilized to extract both temporal and spatial features of SE. The ASTGCN model is trained using quantile regression, relying solely on normal samples during training. This approach eliminates the need for collecting anomalous samples and reduces computational demands. The model establishes a safe interval for SE that significantly improves anomaly detection capabilities. Furthermore, a teacher-student network is implemented, where the teacher network, trained offline, distills its ability to convert low-frequency SE data into high-frequency representations into a simpler student network. The student network executes real-time reconstruction of high-frequency SE, thus enhancing the precision of anomaly detection without substantial resource expenditure. Finally, the analysis conducted on the California’s 7th district highway measurement system demonstrates the proposed method’s ability to accurately detect anomalous SE in traffic flow measurement.