## 4 Experiments and Results
### Abnormal Detection Results
Specifically, Figure 13a shows the traffic flow from the first to the fifth day in the PEMS04 dataset, while Figure 13b shows the traffic flow from the seventh to the eleventh day in the PEMS04 dataset. Figure 14 shows the traffic flow from the fifth to the ninth day in the PEMS08 dataset. The data points in the figures represent the coordinates of the data points that were not detected by our method, as well as the coordinates of the two days before and after each of these points. Traffic flow data exhibits certain periodic patterns, such as differences in flow during peak periods (morning and evening rush hours) and off-peak periods. It may also display seasonal variations on a daily, weekly, or monthly basis. Additionally, traffic flow between adjacent road sections or intersections is often correlated, with upstream traffic conditions influencing downstream ones. The flow typically changes within a reasonable range. This characteristic can be leveraged to further assess whether the undetected data points are truly anomalies. The undetected data points in the figures, however, do not show any significant abnormalities and remain within a reasonable range of variation. An analysis of the anomalies detected by the proposed method but missed by the aggregation method reveals that the proposed detection method identifies more anomalies with clear abnormal characteristics that do not align with short-term traffic flow changes, as shown in Figure 15. The reason for this result, aside from the aggregation method causing the loss of a few anomalies, primarily lies in the limitations of the aforementioned anomaly detection methods when handling high-dimensional and time-series data. For instance, the Isolation Forest method assumes that features are independent, but in time-series data, temporal dependencies (such as trends or seasonality) are crucial, and it fails to model these temporal relationships. LSTM-VAE, while effective, requires complex training  
Figure 14: Visualization of traffic flow from the 5 th to the 9 th day in the PEMS08 dataset. Figure 13: Partial traffic flow visualization of the PEMS04 dataset. and substantial time and computational resources, especially for long time series or high-dimensional data. Its training efficiency is relatively low, and when the difference between anomalies and normal data is small, the model may struggle to differentiate between the two. LSTM-NDT assumes that the prediction errors of normal data are small, while those of anomalies are larger. However, by modeling only temporal features, it fails to adequately learn the data's full characteristics, leading to poor performance of the prediction model and reduced error differentiation ability, which negatively impacts anomaly detection. Additionally, the proposed detection method in this paper also identifies cases where the difference between two anomalous data points within a short time span is too large, and it classifies the data between these anomalies as abnormal, as shown in Figure 11.0 The dataset in this study is collected every 5 min, and based on the analysis of traffic flow variation characteristics, significant changes in traffic flow within a short period may be caused not only by anomalies in data collection but also by the occurrence of unexpected events. To verify whether the drastic fluctuations in traffic flow during this time period were caused by a sudden event, we analyzed the basic relationship between upstream and downstream traffic flow. The relationship between upstream and downstream traffic flow is highly dynamic and coupled. Upstream flow affects downstream flow through a transmission effect, while downstream flow can also influence upstream flow through a feedback effect. We observed the flow changes of nodes connected to this particular node during this time period, as shown in Figure 11.1 From Figure 17, there were no significant changes in the upstream and downstream traffic flow during this period, making the likelihood of a sudden event minimal.