## 4 Experimental Studies
### Results and Discussions  
In the simulation experiments, we applied the proposed TICA method to resolve conflicts in the intersection area. Examples of optimal spatio-temporal trajectories for vehicles generated by TICA are illustrated in Figure 14. As shown in Figure 14, the x-axis and y-axis represent the location of vehicles \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Traffic Flow**} & \multicolumn{2}{c}{**1080 veh/h**} & **1440 veh/h** & **1800 veh/h** & **2160 veh/h** \\  & **(90 Vehicles)** & **(120 Vehicles)** & **(150 Vehicles)** & **(180 Vehicles)** \\ \hline \multirow{3}{*}{Group} & A & (1) 90A & (4) 120A & (7) 150A & (10) 180A \\  & B & (2) 90B & (5) 120B & (8) 150B & (11) 180B \\  & C & (3) 90C & (6) 120C & (9) 150C & (12) 180C \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Code names for 12 test scenarios. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Parameter** & **Value** \\ \hline Time & Time Step & dt = 0.01 (s) \\ Simulation Time Span & tMax = 300 (s) \\ \hline \multirow{4}{*}{Road} & Width of Lane & widLane = 3.5 (m) \\  & Width of Midian Strips & widMedia = 5 (m) \\  & Radius of Left-turning Circle & rLeft = 22.75 (m) \\  & Radius of Right-turning Circle & rRight = 7.25 (m) \\  & Size of Intersection & lenInter = 37 (m) \\  & Length of Entrance & lenEntra = 100 (m) \\  & Total Length of Intersection Area & lenArea = 237 (m) \\ \hline \multirow{4}{*}{Traffic} & Traffic Flow & tf = 1080–2160 (veh/h) \\  & Degree of Traffic Balance & pDirect = [0.25 0.25 0.25 0.25] \\  & Probability of Turning & pTurn = [0.3 0.5 0.2] \\  & Default Speed & vDef = 30 (km/h) \\  & Maximum Speed & vMax = 50 (km/h) \\  & Safe Buffer Distance & safeDisit = 2 (m) \\  & Initial Speed of Vehicle & vInit = 10–50 (km/h) \\  & Length of Vehicle & lenVeh = 3 or 5 (m) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters setup. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Traffic Flow**} & \multicolumn{2}{c}{**1080 veh/h**} & **1440 veh/h** & **1800 veh/h** & **2160 veh/h** \\  & **(90 Vehicles)** & **(120 Vehicles)** & **(150 Vehicles)** & **(180 Vehicles)** \\ \hline \multirow{3}{*}{Group} & A & (1) 90A & (4) 120A & (7) 150A & (10) 180A \\  & B & (2) 90B & (5) 120B & (8) 150B & (11) 180B \\  & C & (3) 90C & (6) 120C & (9) 150C & (12) 180C \\ \hline \hline \end{tabular}
\end{table} 
Table 5: An example of a sign-in sheet of vehicles in the 90A test scenario. in the intersection area, and the z-axis represents the timeline of the simulation. The velocity sequence of each vehicle can be obtained from the spatio-temporal trajectories. The results of conflict detection and resolution for the 12 test scenarios are illustrated in Figure 15. As shown in Figure 15a, the number of potential collision accidents increased with the volume of traffic, which demonstrates the necessity for conflict resolution. As shown in Figure 15b, the number of vehicles that had to adjust their design speed also increased with the volume of traffic. Figure 14: Optimal spatio-temporal trajectories for vehicles in different test scenarios. (**a**) 90A; (**b**) 120A; (**c**) 150A; (**d**) 180A. Figure 15: Results of conflict detection and resolution for different test scenarios. (**a**) The number of potential collisions; (**b**) the number of regulated vehicles. Next, we take a multi-vehicle collision accident in the 150A test scenario as an example to illustrate the detailed resolution process of the proposed method. As shown in Figure 16a, if all the vehicles follow a default design speed (30 km/h), there will be collision accidents occurring at the fourth, 10 th, and 12 th conflict points, of which motion information is shown in Table 6. As shown in Figure 16b, if vehicles follow the individual instructions given by the intersection manager, including a modified design speed and a driving strategy, all these collisions can be resolved.