## 7 Conclusions  
In this paper, an integrated platform for modeling a complete range of real-world traffic problems has been proposed. The problem of achieving a system-optimal traffic flow has been addressed. One possible criteria to evaluate our model is the minimization of the total travel time of vehicles in a network by influencing the traffic distribution over open roads in a network. It is a difficult task to direct specific vehicles to specific paths in real time to avoid congestion or traffic jams when a massive number of vehicles are simultaneously involved. The proposed model was adapted for mass traffic congestion-scenarios by using concepts of static and dynamic floor fields. Additionally, a transition function of agent movement has been developed and adapted according to specific representations of individuals. Hypothetical and a real-world traffic networks based on data from the CDOT were investigated, and the results from both scenarios were compared to ensure that the data captured between both road networks for the different situations were consistent. The results presented here demonstrate the applicability of our models to accurately simulate the events that occur during traffic congestion. The achieved results include both macroscopic and microscopic indicators. Various simulation scenarios were run that demonstrate different features of the proposed model. Considerable changes of traffic dynamics were detected during the simulations. These included avoiding obstacles and high-density areas and then selecting the best alternative route. A significant improvement of traffic flows during simulations was observed when compared to those observed with a non-ITS approach. These observations could influence multiple aspects of how traffic management is planned. Additionally, the MLMS-ABM model and the included decision-making sub-models were able to address the behaviors and intended behaviors of all agents. Taken together, the results show that the proposed multi-leveled multi-staged agent-based model has the ability to improve traffic dynamics during high density simulation. Traffic modelling basically includes two observation levels: \"Micro,\" which represents individual vehicles, and \"macro,\" which represents groups of vehicles. The effectiveness of the model was confirmed by its ability to simulate large-size heterogeneous road network-coupling micro and macro models based on the ABM and CA techniques. The model also ensures the consistency of simulations in this coupling. The proposed dynamic hybrid simulation of traffic flow can dynamically partition a macro space into a set of micro agents. Additionally, the model enables dynamical switching between a macro representation and a micro representation of a traffic system. The behaviors of macro level components in the hybrid model are defined as the result of the behaviors of micro agents; at the same time, it is possible to define feedbacks from macro agents to micro. The generality of the proposed model shows in how it handles agents in a homogeneous way. It allows the same agents, in the same simulation, to belong to different observation levels or different organizations. Whether the agents are \"normal\" or (as the result of an upgrading process) \"intelligent,\" they are all managed by the model in the same way. The model provides all agents with appropriate and consistent behaviors. The multi-level multi-stage modeling experiments confirmed the applicability of the model to for simulations of very complex systems. Communications, interactions, and information exchanges in the model are bidirectional between agents from a micro-macro couple, as well as between agents from each of these levels. Additionally, the upgrading/downgrading process simultaneously involves agents from both the micro and macro levels. The structural characteristics of the MLMS-ABM model achieved the desired functional goals.