# Analysis of Conflict Distribution Characteristics between Motor Vehicles and E-Bikes at Roundabouts
## 2 Materials and Methods
### Conflict Severity Classification  
The type of conflict that may occur in trajectory interweaving areas was determined with the analysis of the trajectory of vehicles and e-bikes. However, the trajectory analysis was based on the spatial view that could occur synchronously, and among the data of conflict discrimination results, some of the small traffic disturbances were included \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number** & **Video** & **Frame** & **T** & **X** & **Y** & **TTC** \\ \hline
1 & 1 & 125 & 5.325 & 32.872 & \(-\)13.531 & 0.632 \\
2 & 1 & 482 & 25.237 & 46.821 & \(-\)19.175 & 0.714 \\
3 & 1 & 993 & 40.589 & 75.126 & \(-\)15.935 & 0.77 \\
4 & 1 & 1065 & 42.116 & 67.619 & \(-\)21.039 & 0.877 \\
5 & 1 & 1152 & 44.991 & 81.013 & \(-\)56.945 & 1.102 \\
6 & 1 & 1599 & 62.357 & 48.761 & \(-\)18.375 & 1.188 \\
7 & 1 & 1721 & 70.454 & 90.023 & \(-\)43.076 & 1.401 \\
8 & 1 & 1833 & 74.332 & 43.361 & \(-\)22.366 & 1.393 \\
9 & 1 & 3025 & 138.693 & 85.217 & \(-\)16.46 & 0.761 \\
10 & 1 & 3451 & 156.448 & 66.603 & \(-\)43.767 & 1.143 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Conflict data information. Figure 7: The trajectory of vehicles and e-bikes during a green light period at Chenyangzhai. too. Therefore, firstly it is necessary to further classify the conflict data into different severity scales. In this paper, the 85% percentile cumulative curve analysis method was used to classify the severity of the conflicts and divide them into serious conflicts or general conflicts [39] and then eliminate the small traffic disturbances counted in the conflict data before. The cumulative frequency histograms are shown in Figures 8 and 9. Through the video observation by professionals, the judgment of serious conflicts was based on obvious braking, large turns, sudden deceleration, etc. of motor vehicles or e-bikes, and general conflicts are judged based on their slight braking, small turns, slow deceleration, etc. If inconsistent results of judgment came forth, a further discussion was carried out to get the number of serious conflicts and general conflicts finally determined. Thus, Serious Conflict could be defined as when motor vehicles or e-bikes make obvious braking, large turns, sudden deceleration, etc. to avoid a potential collision. General conflict could be defined as when motor vehicles or e-bikes make slight braking, small turns, slow deceleration, etc. to avoid a potential collision. Based on the conflict cumulative frequency distribution shown in Figures 8 and 9, a 85% percentile cumulative frequency value was used to determine the boundary of serious  
Figure 8: Cumulative frequency distribution of serious conflicts. Figure 9: Cumulative frequency distribution of general conflicts. and non-serious conflicts. From Figures 8 and 9, we could obtain the threshold of Serious Conflict is 1.7 s, and the threshold of General Conflict is 2.9 s. Traffic Disturbance could be defined when TTC is larger than 2.9 s. The severity of conflict classification result is shown in Table 4.  
Based on the classification criteria in Table 4, the total conflict between vehicle and e-bikes at three roundabouts was 871 (the traffic disturbance data could be eliminated from previous conflicts data). Serious Conflict was 168 times, accounting for 19.3%. General Conflict was 703 times, accounting for 80.7%. The 871 conflicts were classified into four types in Table 5. The conflict times and ratios of 4 conflict types were marked in Figure 10.  
As shown in Figure 9, the number of Conflict Type 1 was 426, accounting for 48.9%, Type 2 was 295, accounting for 33.9%, Type 3 was 34, accounting for 3.9%, and Conflict Type 4 was 116, accounting for 13.3%, which was conflicts that occurred under anomie behaviors that could be eliminated by traffic management. Therefore, this research mainly targeted Conflict Type 1 and Type 2.