# Identification of Critical Road Links Based on Static and Dynamic Features [PERSON]
## 5 Experimental Results
### Performance and Comparison  
To evaluate the effectiveness of the proposed method, we compared four road link importance evaluation methods, including the sorted by traffic flow method (SF), degree and clustering coefficients index (DCI), Deepwalk, and Node2 vec. A directed dual traffic network model was constructed using urban AVI data with different time periods and different time interval sizes, which is shown in Table 1. All data were collected on 12 January 2022. Figure 4: Parameter sensitivity of \(p\) and \(q\). Figure 5: Parameter sensitivity of \(\alpha\) and \(\beta\). This study selected the road links in the top 33% of importance as destructible road links for the importance evaluation using different methods. Moreover, for the destructible road links, the same proportion (10%, 30%, 50%, 70%) of link nodes were removed using random selection. The performance of the method was evaluated by comparing the magnitude of the traffic network efficiency ratio \(\eta\) of the urban road network before and after the road link removal. Where the smaller road network efficiency ratio \(\eta\) means that its method performs better. Each experiment was repeated 10 times to ensure the validity of the results in this paper. For the selection of the set of destructible road links, the study obtained them in two ways:  
1. For the method based on importance ranking (SF, DCI), the study used the nodes ranked in the top 33% as the set of destructible road links. 2. For the graph representation learning model (TraLink2 vec, Deepwalk, Node2 vec), the selection was based on the clustering results. If the number of link nodes in the category of critical links was greater than 33% of the total number of link nodes, the link nodes were randomly removed from it. If the number of link nodes was less than 33% of the total number of link nodes, the remaining link nodes were randomly selected from the category of normal road links after all the link nodes in the category of critical road links were selected. For TraLink2 vec, we set parameter \(\alpha\) to 0.5, parameter \(\beta\) to 1, parameter \(p\) to 1, parameter \(q\) to 0.25, and maintained the other parameter settings as previously defined. For Deepwalk and Node2 vec, the same parameter settings were used as in TraLink2 vec. Tables 2-4 present the performance of various methods for different time intervals during different time periods. The bold numbers indicate the optimal performance for each condition. The results show that the proposed methods in this study demonstrate superior performance in most cases, indicating that the integration of dynamic and static features of road links can effectively identify critical road links. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multicolumn{3}{c}{**During Time**} \\ \cline{2-4}  & **5 min** & **15 min** & **30 min** \\ \hline Morning Rush & 8:30–8:05 & 8:30–8:45 & 8:30–9:00 \\ Hollow & 10:30–10:05 & 10:30–10:45 & 10:30–11:00 \\ Evening Rush & 18:30–18:05 & 18:30–18:45 & 18:30–19:00 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Illustration of different time periods and different time intervals. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multicolumn{3}{c}{**Proportion**} & \multicolumn{3}{c}{**The Traffic Network Efficiency Ratio**} \\ \cline{2-7}  & \begin{tabular}{c} **of Links** \\ **Removed** \\ \end{tabular}  & **DCI** & **SF** & **Deepwalk** & **Node2 vec** & **TraLink2 vec** \\ \hline \multirow{4}{*}{Morning Rush} & 10\% & 0.883 & 0.912 & 0.836 & 0.861 & **0.785** \\  & 30\% & 0.727 & 0.74 & 0.635 & 0.611 & **0.604** \\  & 50\% & 0.392 & 0.568 & 0.452 & 0.477 & **0.406** \\  & 70\% & 0.288 & 0.467 & **0.26  
Compared to SF and DCI, the TraLink2 vec model outperforms both methods in all cases, demonstrating that relying solely on traffic travel or network topology indicators cannot effectively identify critical road links in urban transportation systems.