# Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing
## 4 Performance Evaluation
### Results and Discussion  
Figure 5 shows the results with different sliding windows of the proposed method. It achieves the best F1 score with a window size of 300. However, the accuracy with the window size of 300 is slightly lower than that of the window size of 200. It indicates that when the size of the sliding window is 300, there are graphs detected incorrectly. In terms  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Simulator & SUMO 1.9.2 \\ Communication range & 200 m \\ Vehicle size & 5 m \\ Vehicle safety gap & 2.5 m \\ Vehicle speed limit & 16.67 m/s \\ Simulation time & 3600 s \\ Incident period & 300 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
of the F1 score, the results show that if the window size of the method equals the incident duration period, it achieves the best performance. This is because the large window sizes contain not only the abnormal interval but also some normal intervals. Therefore, the large window sizes affect the performance of the method. In contrast, the small window sizes cannot contain the whole abnormal time interval, so some abnormal patterns are ignored.  
With the selected window size of 300, we conducted experiments to observe the performance with different traffic volumes. As shown in Figure 6, with the decrease in traffic volume, the performance shows a downward trend. This is because in low traffic volume scenarios, the traffic flow is hardly interrupted, and traffic congestion may not occur even if traffic incidents happen. In other words, the mean speed, mean waiting time, the number of vehicles, and their correlation will not be significantly affected in the low traffic volume scenarios. Therefore, the accuracy of the incident detection in the low traffic volume scenarios is lower than that in the higher traffic volume scenarios.  
Figure 5: The performance with different window sizes.  
Figure 6: The performance with different traffic volumes.  
Table 2 presents the results of the proposed method against other dynamic graph embedding methods. In all traffic volume scenarios, the proposed method has better results than the baselines by 14.5% and 18.1% on average with respect to F1 score and accuracy, respectively. GCN is a node embedding method that considers the neighbor structure of the vertices in the graph. In order to apply the GCN method to our research, we use two GCN models to build an autoencoder for calculating the embedding vector of each graph. According to the result, the performance of the GCN method is lower than the proposed method but outperforms the other methods. Dyn2 vecAE model does not exploit the temporal information of the dynamic graph so that its performance is lower than that of the Dyn2 vecRNN model. The combined model, Dyn2 vecAERNN, considers the spatial and temporal information so that it achieves better results than the other two Dyn2 vec-based models. Our method uses entropy as a measurement to embed the dynamic graph. Thereby, the graph with an abnormal entropy can be highly distinguished.  
Table 3 shows the comparison results of the proposed method with different window sizes on the high traffic volume with respect to F1-score. According to the results, the proposed method exhibited the best performance by comparing with the baselines on all of the window sizes. The standard deviation of the proposed method is 0.012. It indicates that there is little affection with different window sizes. In addition, the window sizes of 300 and 350 achieved the best F1-score on our method. It indicates that we cannot find a window size to get the best performance of the proposed model. However, we can get a window size by calibrating the assigned time window, and the model can achieve a relatively good performance.