## 6 Results and Discussion  
The MLMS-ABM model provides runtime analytics that provide users with a wealth of feedback about what is happening inside the model as the simulation takes place. Runtime analytics help query speed, density, travel time, and LOS [9; 47], which is used to classify the comfort of urban facilities from the perspective of drivers. The level of service can be considered a mechanism to determine the effectiveness of a transport facility from the perspective of a traveler. It determines how a specific service is being offered. In general, six different levels of service are defined. They range from **A** to **F**, with **A** representing the best operating conditions and **F** representing the worst. It can be used as an effective measure to evaluate the overall quality of service [47]. The goal of the model is to divert the traffic flow around a freeway accident onto the nearby city road so that the congestion is reduced while each vehicle takes its best individual route. The primary objective of this approach is to minimize traffic congestion, thus improving travel times. In addition, the MLMS-ABM model provides output statistics that enable users to store data from many simulations and do comparisons between multiple findings. Comparing different scenarios also enables users to get the average of many runs. In order to validate the proposed model, implementation was performed using a custom imperative programming approach. Figure 6 shows a screenshot of the simulation illustrating the agents' movement in the hypothetical road network. It also shows the freeway and city arterial roads and where an accident occurs on the freeway (the red circle). Figure 5: The segment of the real road network in Denver County. In order to investigate the performance of the model, many experiments were conducted. The values of the weight sensitivity parameters in all experiments were set to: \(k_{T}=2.0\), \(k_{O}=0.3\), \(k_{D}=0.5\), \(k_{den}=0.02\), and \(k_{I}=0.5\) for the target, obstacles, dynamic, density floor fields, and inertia, respectively. Simulations for both the hypothetical and the real I-25 road were performed and captured the time of travel for the vehicles to reroute around the accident. Travel time variability is an important measure of model performance. Every agent's decision regarding the optimal route is based on minimizing the desired travel time to reach the desired destination. The first simulation was related to demonstrating the model's efficiency in promoting the overall traffic travel time during the accident in the hypothetical road network. This experiment was run with 5000 VPH on the main highway with varying arterial traffic loads. In this experiment, the total travel time was calculated for all agents, as shown in Figure 7. The results illustrate that the MLMS-ABM routing model provides the quickest route times regardless of the value of the arterial road loads. With non-deterministic behavior, some agents conflict in their paths with others, as some of them do not follow intelligent agents. These collisions increase the travel time because agents sometimes stop due to the participation of some agents who do not follow intelligent agents. As a result, travel time increases despite having a portion of traffic following the intelligent agents. The non-MLMS-ABM spent the longest time rerouting. The results show that vehicles in the baseline case were either remaining on the freeway or exiting the freeway using the first exit. This result confirms the ability to rely on an intelligent agent to improve the overall travel time. The second finding illustrates the model's ability to improve vehicular traffic flow. The experiment was run on a minimum traffic load with 400 VPH in the arterial roads and 5000 VPH on the main highway.