## 6 Results and Discussion
Figure 8 shows that traffic flow rate was the worst in the non-MLMS-ABM, whereas it was almost the same in all MLMS-ABM model cases, since the difference between the three cases was  
Figure 6: Screenshot of the simulation environment of the hypothetical road network. Figure 7: Total travel time for rerouting around accident with different arterial loads. insignificant at the first 40 time-stamps of the simulation. However, as the simulation time progressed, the non-deterministic cases of the proposed model overcame the deterministic ones in the traffic flow rate. That could be due to the independence of some agents to choose their own route. In the general, the result indicates the ability of the model to improve overall traffic flow during accidents. Figure 9 illustrates a histogram of travel time for rerouting around accidents with varying loads along the main highway and varying arterial traffic. In these simulations, the impact of the number of agents (vehicles) on the journey time was tested. The experiment was run on the MLMS-ABM model with varying traffic volumes along the main highway. Four traffic volumes were tested in this experiment: 4000, 5000, 6000, and 7000 VPH. This experiment was run for each traffic volume under four different arterial traffic loads. As can be seen from the histogram, the number of vehicles on the main road has a significant effect on the vehicles' travel time. As expected, there was a direct correlation between the travel time and the traffic volume on the main highway; when traffic volume increased on the main highway, the total travel time increased (see Figure 9). The fourth simulation performed was to examine the traffic throughput, i.e., the number of vehicles reaching their destinations as a function of time. Figure 10 shows that the number of agents exiting the simulation environment, which means reaching their destinations, was higher during the MLMS-ABM simulation than in the non-MLMS-ABM run. This confirms the ability of the proposed model to improve the throughput. Figure 8: Traffic flow during simulation time (hypothetical road network). Figure 9: Total travel time for rerouting around accident during different traffic volumes with different arterial loads. The vehicular speeds for the same agent from the same distance in different scenarios (traffic loads) were compared, as shown in Figure 11. That agent under examination was chosen randomly. The histogram shows that the agent's average speed was higher during the application of the MLMS-ABM model than during the non-MLMS-ABM application regardless of the traffic loads on the arterial roads. Additionally, as shown in Figure 11, the average vehicle speeds were significantly higher for all cases of the MLMS-ABM model with no or minimum arterial loads compared to the medium and maximum load scenarios. This result shows the impact of the arterial roads' volume of traffic on the speed of vehicles during traveling over alternative routes in the case of an accident. In the other simulation scenarios, the routes along the I-25 were modeled. In the first experiment where travel time was measured, the proposed model had the best performance among other methods in the simulation of the I-25 routes. Looking at the data from the decentralized approaches, in both the deterministic and non-deterministic cases, it can be seen that the overall travel time to reroute around an accident was improved over the non-ITS method. Additionally, in the first case of the non-deterministic agent-based model, where only 50% of the vehicles were able to communicate  
Figure 11: Average vehicle speeds with different arterial loads (hypothetical road network). Figure 10: Traffic throughput during simulation with different arterial loads. with each other and the intelligent agents in their region regarding the best alternative routes to take, the overall travel time was better compared to the second case of the non-deterministic method.