## 3 Related Work  
A traffic distribution and routing problem can been improved dramatically by involving both a decrease in road accidents and time spent in network, as well as an increase in level of service (LOS) [9]. A number of approaches and algorithms have been introduced to solve traffic routing problems (see Table 1). For example, [PERSON] et al. [3; 4] introduced an approach to minimize the total time spent in a network by influencing the traffic distribution over available roads in a network. The problem has been modeled by the means of a novel reverse Stackelberg game approach. [PERSON] and [PERSON] [5] conducted a preliminary study of blockchain technology and designed an ITS-oriented model for building parallel transportation management systems. [PERSON] et al. [7] studied and compared the performance of different route-planning algorithms in real road networks and classified them according to the mechanisms used for searching the best routes. Compared to the proposed model, these approaches did not apply an agent-based modeling approach to solve the vehicle routing problem. Because of the emergent phenomena of traffic complex systems, the ABM is an effective approach to address the question of how a system's behavior connects to the behaviors and characteristics of its individual components. Employing the adjustable ABM approach helps to simulate human perception and decision-making in complex scenarios such as traffic accidents. Therefore, the proposed agent-based model is able to accurately simulate the vehicles' behaviors and phenomena, allowing for improved decisions taken to enhance the traffic systems. \begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|} \hline
**ITS Models** & **Agent-Based Approach** & **Cellular Automata Approach** & **Hierarchical/Hybrid Structure** & **Leader/Decision** & **Minimize total Travel Time** & **Collision** & **Trustworthiness Evaluation** \\ \hline MLMS-ABM model & Yes & Yes & Yes & Dynamic & Yes & Yes & Yes \\ \hline  & [30] & [20] & - & - & - & Static & Yes & – & – \\ \hline [PERSON] and [PERSON] [20] & Yes & - & - & - & & & – & – & – \\ \hline [PERSON] et al. [20] & Yes & – & Yes & Static & Yes & – & – & – \\ \hline [PERSON] et al. [20] & - & – & - & - & Yes & – & – \\ \hline [PERSON] et al. [20] & – & Yes & Yes & – & Yes & – & – \\ \hline [PERSON] et al. [20] & Yes & Yes & – & – & – & Yes & – \\ \hline [PERSON] et al. [20] (2017) & Yes & – & – & – & – & Yes & – \\ \hline [PERSON] et al. [2006] & Yes & – & Yes & – & – & Yes & – \\ \hline [PERSON] et al. [2016] & – & Yes & Yes & – & Yes & Yes & – \\ \hline [PERSON] et al. [2017] & Yes & Yes & – & – & Yes & – & – \\ \hline [PERSON] et al. [2004] & Yes & - & Yes & Static & – & – & – \\ \hline \end{tabular}
\end{table} 
Table 1: Comparing the MLMS-ABM model and other selected intelligent transportation systems (ITS) models. On the other hand, the work presented in [6] examined and compared the effect of ITS centralized and decentralized architectures on transportation mobility when accidents occur. However, this work presented only a conventional multi-agent approach to implement a general solution for vehicles in a distributed architecture. [PERSON] et al. [10] utilized an ABM to model collision detection and avoidance under congested urban road traffic. Additionally, [PERSON] et al. [14] used MAS technology to build a vehicular information ad-hoc network. Likewise, the works in [9] and [36] introduced a model for the simulation of non-signalized road crossings where there is a direct interaction between pedestrian and vehicular traffic. The traffic was modeled by applying a continuous car following model by using the Gipps equations, and a CA model was used to simulate pedestrian motion. [PERSON] et al. [22] proposed a multiscale approach to predict traffic and crowd flow during public events. [PERSON] et al. [40] proposed a hierarchical model to address the coordination issue for a platoon of vehicles by considering it as an MAS.