# Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing
## 5 Conclusions  
This work proposed a novel method for detecting traffic incidents on multiple traffic time series based on the dynamic graph embedding. The sliding window divides the time series into several time intervals, and the dynamic graph represents the correlation between the time series in each time interval. The graph entropy is exploited to measure the similarity between two graphs. Based on the graph similarity, we presented a dynamic graph embedding model for clustering the graphs. The object of the model is reducing the distance between two graphs, which are similar to the neighbor structure and graph entropy. In this case, if the neighbor structure and graph of two graphs are similar and their distance is short in the embedding space. The experiments were carried out with the traffic time series generated by the SUMO simulator. The experimental results demonstrated that  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Traffic Volumes** & \multicolumn{2}{c}{**Low**} & \multicolumn{2}{c}{**Medium**} & \multicolumn{2}{c}{**High**} \\ \hline
**Models** & **ACC** & **F1 Score** & **ACC** & **F1 Score** & **ACC** & **F1 Score** \\ \hline Dyn2 vecAE & 0.560 & 0.690 & 0.573 & 0.720 & 0.763 & 0.637 \\ Dyn2 vecRNN & 0.547 & 0.707 & 0.583 & 0.727 & 0.680 & 0.810 \\ Dyn2 vecAERNN & 0.587 & 0.723 & 0.597 & 0.733 & 0.667 & 0.790 \\ GCN & 0.630 & 0.737 & 0.663 & 0.790 & 0.797 & 0.843 \\ Proposed model & **0.700** & **0.843** & **0.740** & **0.850** & **0.800** & **0.880** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of different dynamic graph embedding models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Window Sizes (s)** & **200** & **250** & **300** & **350** & **400** \\ \hline Dyn2 vecAE & 0.620 & 0.717 & 0.637 & 0.677 & 0.723 \\ Dyn2 vecRNN & 0.700 & 0.700 & 0.810 & 0.650 & 0.677 \\ Dyn2 vecAERNN & 0.700 & 0.700 & 0.790 & 0.650 & 0.677 \\ GCN & 0.623 & 0.667 & 0.843 & 0.650 & 0.660 \\ Proposed model & **0.890** & **0.864** & **0.880** & **0.880** & **0.850** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of different window sizes.  
the proposed model achieved better performance than the baselines by 14.5% and 18.1% on average with regard to F1 score and accuracy, respectively.  
Some issues could be taken into account in future work. For enhancing the performance of the proposed method, we consider a hybrid model that combines the autoencoder and LSTM models to extract the temporal features for dynamic graph embedding. As the competing baselines are exclusively from the dynamic graph embedding models, the proposed method could be compared with other incident detection methods. For determining the right time window in the proposed method, we plan to utilize the wavelet transform to calculate the frequency domain of the multiple time series and divide the time series into several time intervals based on their frequency.  
Conceptualization, G.L., T.-H.N. and J.J.J.; methodology, G.L. and T.-H.N.; software, G.L. and T.-H.N.; validation, G.L. and T.-H.N.; formal analysis, G.L. and T.-H.N.; investigation, G.L. and T.-H.N.; resources, J.J.J.; data curation, G.L. and T.-H.N.; writing--original draft preparation, G.L. and T.-H.N.; writing--review and editing, J.J.J.; visualization, G.L. and T.-H.N.; supervision, J.J.J.; project administration, J.J.J.J.; funding acquisition, J.J.J.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2019K1A3A1A80113259, NRF-2020R1A2B5B01002207).  
Not applicable.  
This research was supported by the Chung-Ang University research grant in 2020. Also, this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2019K1A3A1A80113259).  
The authors declare no conflict of interest.