## 6 Results and Discussion
The travel time histogram is shown in Figure 12.  
Figure 13 illustrates the model's capability to improve the vehicular traffic flow. The experiment ran on the maximum traffic load case with 1600 VPH in the arterial roads and \(\sim\)4400 VPH on the main highway. The result shows the ability of our model to improve overall traffic flow during accidents. Figure 13 shows that the traffic flow rate at the beginning of the simulation was the worst in the non-MLMS-ABM, whereas the difference between the three MLMS-ABM model cases was insignificant. The vehicular speeds for the same vehicle from the same distance during different traffic loads on arterial roads were compared, as shown in Figure 7.0 The vehicle was randomly chosen for this experiment. The histogram shows that the agent's average speed was higher during the application of the MLMS-ABM model compared to the non-MLMS-ABM application for all arterial traffic loads. However, the difference between speeds was modest during the maximum arterial traffic loads. Additionally, vehicles' average speeds were significantly higher during the application of all cases of the MLMS-ABM model with no or minimum arterial loads than during the medium and maximum load scenarios. As shown in Figure 14, the highest average vehicle's speed was recorded in the application of the deterministic case of the MLMS-ABM model. This result denotes the ability of the proposed model to improve the speed of vehicles. Figure 7.1 Total travel time through the I-25 freeway in different methods with different traffic loads. Figure 7.2 Traffic flow during simulation time (real-world network). Tables 2-4 show the proportionality of the vehicular travel time, flow rates, and average speed values, respectively, between the hypothetical road network and the I-25 road network with different loads on arterial roads. It can be seen that the average values are 0.9 in the first and second tables, and the average value is 1.5 in the third table except for the simulations with maximum arterial load. It can be concluded from the three tables that there was consistency between the two simulations. As expected, the results, in general, show that the two versions of the MLMS-ABM routing model (deterministic and nondeterministic cases) always overcame the non-MLMS-ABM model under all experiments in the two traffic network scenarios (hypothetical and a real-world networks). The deterministic case of the MLMS-ABM routing model provided the quickest route times \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Traffic** & **MLMS-ABM** & **MLMS-ABM** & **MLMS-ABM** & **Non\_** \\
**Loads** & **(Deterministic)** & **(Non-Deterministic)** & **(Non-Deterministic)** & **MLMS-ABM** \\ \hline
0 VPH & 1.4 & 1.4 & 1.4 & 1.4 \\
400 VPH & 1.3 & 1.8 & 1.5 & 1.0 \\
800 VPH & 1.6 & 1.7 & 1.5 & 1.9 \\
1600 VPH & 3.4 & 3.0 & 3.0 & 3.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparing the average vehicle speed for the hypothetical road network and the I-25 road network. Figure 14: Vehicle’s average speed with different arterial loads (real-world network). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Traffic** & **MLMS-ABM** & **MLMS-ABM** & **MLMS-ABM** & **Non\_** \\
**Loads** & **(Deterministic)** & **(Non-Deterministic)** & **(Non-Deterministic)** & **MLMS-ABM** \\ \hline
0 VPH & 0.9 & 0.9 & 0.9 & 0.9 \\
400 VPH & 0.8 & 0.9 & 0.8 & 0.9 \\
800 VPH & 1.0 & 1.0 & 0.9 & 0.9 \\
1600 VPH & 0.9 & 0.9 & 0.8 & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparing the total travel time for the hypothetical road network and the I-25 road network. and best performance of improving the throughput. On the other hand, the non-deterministic cases of the proposed model overcame the deterministic one in improving the traffic flow rate. Additionally, the overall results show that the difference between the two cases of the MLMS-ABM model was insignificant when considering the vehicles' average speed for all arterial traffic loads.