## 1 Introduction
This routing approach/method, called dynamic or distributed routing [6; 7; 14; 37], has been explored through a number of papers [6; 7; 8; 9; 14; 23; 36; 37]. It is an agent-based approach for modeling complex transportation networks in a manner that permits vehicles to communicate with other vehicles to gain a better assessment of the current state of nearby road networks. This communication provides drivers with real-time road data and an enhanced awareness of the road network to help them seek efficient alternative routes, thus reducing congestion [14]. In this research, the multi-level multi-stage agent-based model (MLMS-ABM) is presented, where traffic dynamics are divided into three main levels of decision making: Strategic, tactical, and operational. The pre-trip planning of the route and the destination is designed at the strategic level. At this level, no information is provided about the state of the system. At the tactical level, decisions for the short term, such as avoiding obstacles or changing routes depending on the real-time situation, are addressed. The operational level represents the agents' movement that includes interaction with other vehicles. The system overview is illustrated in Figure 1. The MLMS-ABM allows drivers to follow specific routes computed via a decentralized MAS. The model contributes toward the improved flow of traffic; thus, the drivers are able to reduce the total time that is spent on the road. The presented work investigates an MAS control-strategy on an autonomous microgrid that is represented through cellular automata. Figure 1: Multi-level multi-stage agent-based model (MLMS-ABM) system overview. The vehicles are modeled as agents that have the capability to communicate and exchange decisions with neighboring vehicles, thus enhancing the global pursuit of finding an optimal vehicle route that satisfies each driver's preferences. In the MLMS-ABM, vehicles have the ability to report information to their neighbors via telematic technology, which influences the arbitration between behaviors. The information between vehicles is exchanged through communications technology, such as vehicle-to-vehicle communication (point to point radio), onboard sensors, or mobile phone towers. For example, vehicles are aware of their current position based on a digital map or an onboard telematic device. The essential contribution of this work is applying the developed distributed architectures to the distributed vehicle routing-problem. The MLMS-ABM framework provides a decentralized processing approach where the key to distributed vehicle routing is the underlying interaction relationships of the vehicles themselves. The MAS contains communication constraints which make it so that agents can only communicate with their immediate neighboring agents. The vehicles share information with the other vehicles within their neighborhood. Such communication between vehicles combines the effectiveness of the macroscopic and microscopic modeling layers with the involved stages in the proposed hybrid model. As vehicles in the presented model interact and share data with one another, they choose their routes based on the selected routes of the other vehicles. By knowing the decisions of surrounding drivers, a driver will gain better situational awareness and make a more accurate decision, thereby reducing traffic congestion impact on alternative routes while improving overall travel time. This enhanced driver awareness improves decision-making about the best alternative road to take and thus ensures fewer routing delays as a result of traffic congestions. As mentioned earlier, the primary objective here is to make sure that all drivers are not taking the same alternative route.