## 5 Experimentation and Analysis  
The above described modeling framework was executed independently in three different versions of the proposed model. First, in the case of deterministic behavior, the MLMS-ABM model was applied to a scenario where all agents should follow the intelligent agent in their region. Thus, in this case, there was no implementation of the trustworthiness sub-model. Second was the non-deterministic technique, where agents in a region have a choice to follow or not to follow the intelligent agent in that region. That means some of the agents are in the solitary mode. Here, two cases are examined: In the first sub-case, it has been specified that only 50% of the vehicles are able to communicate with each other and intelligent agents in their region, while in the second sub-case, the percentage of agents who have the ability to communicate is specified based on the trustworthiness of the sub-model. In order to perform an analysis of the different forms of the agent-based distributed routing algorithms, the dynamics of the road network are simulated without any form of ITS. In other words, the non-MLMS-ABM approach was implemented as a baseline of the experiments, where only the traditional agent-based simulation was implemented without involving the tactical level. This means that in this simulation, there was no application of status upgrading and trustworthiness sub-models. In this case, there was no knowledge of the condition of the road network, and the only best route was considered the shortest distance path toward the destination. As such, the agents do not communicate and have no information about the alternative routes. Comparing the proposed MLMS-ABM approaches and the non-MLMS-ABM approach helps us to see the advantages of developing the MLMS-ABM and distributed environment approaches to solve the traffic issues. In order to investigate the performance of the proposed decentralized routing model, it was analyzed in two different simulation scenarios: (i) Hypothetical simulation and (ii) real-world simulation based on real road network data. Many experiments were performed on those two simulation scenarios to test the ability of the presented model to improve the vehicular traffic flow during congestion. In order to perform the hypothetical analysis, a typical virtual road network was constructed. A road network was considered in which several road segments were shared by different routes associated with the same, as well as different origin and destination pairs. In this simulation, the accident was generated at a predefined time. For instance, in the experiment, the accident was generated at the tenth timestamp in the simulation. Additionally, the location of the accident was predefined in the input file of the simulation environment. In the simulation, it was assumed that the freeway accident did not completely close all lanes; one lane remained open. Additionally, it was assumed that average daily traffic (ADT) on the main freeway was approximately \(\sim\)100,000 total across all lanes, or about 5000 VPH (vehicles per hour) on the main freeway. In order to validate the previous hypothetical network, the proposed model was tested on a real-world network. The traffic in Denver County was selected for this study, since it has the highest number of total crashes according to the Colorado Department of Transportation (CDOT) [42]. A very typical road network is the I-25 Interstate Freeway in Denver County, which often exhibits traffic accidents [42; 45; 46]. Data obtained from the CDOT database were used to model the routes in the I-25 freeway. The segment that was investigated includes arterial roads that intersect with the I-25 State Freeway and play an important role as different alternatives. In addition, these arterial alternative roads themselves can have local traffic congestions. Using the data provided by CDOT, the simulation was performed at approximately \(\sim\)4400 VPH traffic load on the freeway across all five lanes.