## 3 Experiments and Results
### Qualitative Analysis
In addition, although we started with five features (VV, VA, PV, DVP, and DVC), only three features (VV, PV, and DVP) formed branches in this simplified decision tree. We focused on analyzing the rules \begin{table}
\begin{tabular}{c c} \hline \hline
**Cluster \#** & **Rules** \\ \hline \multirow{3}{*}{0} & VV \textgreater{} 21.6 km/h and DVP \textgreater{} 9.2 m and PV \textless{}= 10.2 km/h \\  & or \\  & 54.5 km/h \textless{} VV \textless{}= 65.4 km/h and DVP \textgreater{} 15.6 m \\ \hline
**1** & VV \textless{}= 21.6 km/h and DVP \textgreater{} 10.6 m and PV \textless{}= 7.8 km/h \\ \hline
**2** & 21.6 km/h \textless{} VV \textless{}= 54.5 km/h and DVP \textless{}= 9.2 m and PV \textless{}= 6.6 km/h \\ \hline
**3** & VV \textless{}= 21.6 km/h and DVP \textless{}= 10.6 m and PV \textless{}= 3.1 km/h \\ \hline \multirow{3}{*}{4} & VV \textless{}= 21.6 km/h and DVP \textless{}= 10.6 m and PV \textgreater{} 3.1 km/h \\  & or \\  & VV \textless{}= 21.6 km/h and 10.6 m \textless{} DVP \textless{}= 12.3 m and PV \textgreater{} 7.8 km/h \\  & or \\  & 21.6 km/h \textless{} VV \textless{}= 54.5 km/h and DVP \textless{}= 9.2 m and PV \textgreater{} 6.6 km/h \\ \hline
**5** & VV \textgreater{}= 54.5 km/h and DVP \textless{}= 15.6 m and PV \textless{}= 11.7 km/h \\ \hline \hline \end{tabular}  
* **Note:** Vehicle velocity = VV, vehicle acceleration = VA, pedestrian velocity = PV, vehicle-pedestrian distance = DVP, vehicle–crosswalk distance = DVC.  
\end{table} 
Table 4: Rules derived from decision tree. Figure 14: Result of decision tree with pruning. Figure 13: Result of decision tree (no pruning). for paths from root to leaf node, rather than what each node means within the context of the tree and subtrees. For example, if we follow the leftmost path from root node to the leaf node (VV is less than 21.6 km/h, DVP is less than 10.6 m, and PV is less than 3.1 km/h), then this rule matches cluster #3. Based on these \"_if-then_\" rules, we can interpret the patterns for each cluster. Table 4 shows all of the rules derived from this decision tree. In cluster #1, cars are driving slowly, and pedestrians are at least 10 m away, while in cluster #3, cars are driving slowly but closer to the pedestrians, who are stopped or walking very slowly. Both of these represent safer situations than the other clusters. Cluster #0 encompasses two scenarios. First, vehicles are moving at moderate to high speeds, but pedestrians are sufficiently far away and walking somewhat quickly. Alternatively, cars are moving faster than the speed limit, but their distance from pedestrians is large. We interpret cluster #0 to represent potential (but not severe) risky situations. Similarly, cluster #2 shows vehicles driving at moderate speeds within 9 m of pedestrians, who are walking slowly. Cluster #4 includes three branches that parallel situations in clusters #1, #2, and #3, but with faster-moving pedestrians. Either pedestrians are moving quickly to avoid contact, or are entering the crosswalk at higher speeds, implying greater risk to the pedestrian. We regarded cluster #5 as having the most dangerous situations, since vehicle velocities far exceeds the speed limit, at distances from pedestrians within \(\sim\)15 m.  
Comprehensively, we can match each rule (cluster) to a degree of PPRE. As a result, clusters #1 and #3 appear safe, while clusters #0, #2, and #4 are ambiguous but could represent risky situations for pedestrians. The risks in cluster #5 were much clearer.