## 1 Introduction  
A significant amount of research has addressed ways of incorporating technology to help avoid vehicle collisions and reduce road accidents [1; 2; 3; 4; 5]. Traffic congestion takes place when the road system capacity is insufficient to handle the traffic flow and/or when drivers collectively fail to communicate with each other in real time. Current solutions brought in to improve traffic conditions often make life easier for a small percentage of the population while making life more difficult for others [2; 3; 4], such as when considerable amounts of vehicular emissions contribute to air pollution in urban networks [4]. The high frequency of starting and stopping at traffic signals and intersections contributes to a higher volume of fuel consumption during rush hours as well as uncongested times when the flow of vehicles is interrupted by cyclists and pedestrians. Therefore, waiting time is created in all traffic scenarios, and this plays a crucial role in any flow analysis. Road authorities in urban areas interested in reducing emissions need to understand the overall total time that people are spending on the road and the optimum speed at which the traffic should flow [3; 4; 5]. A reduction in emissions occurs when the number of accelerations and decelerations decreases, and the emissions that take place in urban areas are heavily impacted by these two factors (accelerations and decelerations). Obviously, they both need to be minimized [3; 4; 6]. Traffic flow creates a dynamically complex system since it involves a nonlinear interaction between many independent vehicles with largely autonomous behavior. These interactions can lead to situations that produce different kinds of traffic problems. For example, traffic jams can occur when a group of vehicles are stuck behind one that is driving particularly slowly. A number of models have been developed and applied to simulate the process of vehicular dynamics causing traffic jams caused by slow-moving vehicles. The results of these studies have helped researchers develop new ways to integrate and incorporate the increasing presence of vehicles [7; 8; 9; 10]. One particularly effective computational simulation method is an agent-based model (ABM) [11; 12; 13; 14; 15; 16; 17], which considers the complex system as a decentralized, multi-agent system (MAS) [11], where each agent can communicate only with its immediate neighboring agents. The ABM approach is a form of optimization of individual solutions, and it applies to systems of interacting, autonomous, and individual agents. The ABM is used to model and simulate complex systems ranging across various contexts, including biological and social systems. While agents in the decentralized system have no direct information about the global condition of the network, they do have local information about their immediate neighbors and environment [11; 14]. This way, they can use this knowledge to collectively construct a cooperating system. In an ABM, agents are described as unique and autonomous entities that embody the origin of the system [15; 16]. Instead of describing the state of whole systems, an ABM represents the system's individual components and their behaviors. Their generalized nature is able to capture complex dynamics and structures [14; 15; 16; 17]. It can be observed in the literature that both ABMs and complex networks have their roots in the theory of complex systems. The ABM approach has been successfully applied to a wide range of scenarios including military training, building evacuation, and the analysis of digital games [15; 17]. The implementation of agent-based frameworks for the analysis of other complex social systems, in addition to traffic dynamics, has now become more common [10; 13; 16]. Spatial-temporal processes, such as traffic dynamics and congestions, typically require complex models.