## 1 Introduction
The approaches used to construct these models are those of the ABM and cellular automata (CA) [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35], which have the ability to explain the step-by-step status changes that take place within spatial cells. Both methods are capable of reflecting the emergent and complex characteristics associated with complex phenomena [21] and have significant impacts on the determination of optimum traffic flows. With this kind of a framework, it is also possible to model and simulate the complex interactions that take place between homogeneous agents, such as vehicles in road traffic [17; 35]. Several mathematical models are available to simulate traffic jams and provide a clear understanding about their occurrence and consequences [23]. The ones based upon the principles of CA have received a lot of attention [8; 9; 23; 36]. One of the practical applications of cellular automata is in the simulation of street traffic control. The CA model is an effective approach to explain the principles of traffic jams while building the theory from fundamentals. Cellular automata simulations of complex network dynamics provide excellent assistance and add a higher level of efficiency into the design of transportation facilities. The CA model is sufficiently advanced and complex that it has been widely used as a mathematical tool to study systems wherein there are a high numbers of agents that are constantly changing their states [23; 36]. An example of such a scenario is traffic on highways or places where there are too many vehicles present, which limits their movement [9]. In addition, the CA modeling approach has proven useful when the rate of change in a system begins slowly and then increases over time [8], e.g., when drivers apply their brakes and cause a chain-reaction slowdown of the traffic behind them. A large number of studies have been conducted in order to get a clear understanding of the dynamic routing-problem, which is referred to as online or real-time vehicle routing problem (VRP) [37]. The problem has been analyzed from many different angles to determine if a solution can be derived. One of the most significant decisions in the dynamic routing solution is to understand how certain decisions will be made, as well as the impact of those decisions [19; 37]. The primary objective of understanding the decision impact is to provide context-sensitive data and information to the drivers (e.g., via mobile phones and global positioning systems). The drivers can then prevent delays that they would otherwise experience due to a traffic congestion. During an accident, most of the drivers on the road tend to look for alternative routes. At the time the driver seeks to select an alternative route, having a clear understanding of the existing traffic conditions on the road is extremely useful [2; 5; 9; 23]. Most drivers seek the shortest route, and the available GPS navigation applications and systems provide excellent assistance. However, during times of heavy congestion when drivers are using the same GPS navigation systems, their usefulness is mitigated because everyone is trying to follow the same detour, which results in the transference of the traffic congestion [3; 6; 8]. To prevent this, drivers need to be alerted about alternative routes by the exchange of data among other drivers involved. As such, the decision on where the vehicle should reroute needs to be made with data gathered through sensors from the existing network. Assisting drivers in finding the shortest possible alternative routes after an accident is just one of many objectives of traffic information systems. After the required information is made available, the drivers have an easier task deciding the most efficient route to take because they collaboratively look for alternative routes. When collaborating with each other through the same system, the group is not directed to just one specific route as an ordinary GPS system does.