# A Comparative Study on Traffic Modeling Techniques for Predicting and Simulating Traffic Behavior
## 5 Traffic Simulation Models  
In spite of the fact that traffic analytical models are helpful in giving insights into traffic status, traffic simulation systems play a significant role in representing and evaluating traffic behavior under a number of circumstances [54]. Traffic simulators are also considered a key enabler in the effective implementation of smart mobility services. Extensive simulation to evaluate and test the impact of such services will be essential prior to real-world testing. Hence, traffic analysis and modeling of _'what if'_ scenarios assist policymakers and traffic planners with making informed decisions regarding infrastructure planning and investments. The ability of these traffic simulators to model various levels of traffic complexity and city-wide scales ranging from a single detailed intersection to a specific region will provide valuable insights into traffic modeling and analysis [55]. This provides different levels of granularity among commercial and open-source traffic simulators which can vary extensively. Hence, these traffic simulators can be classified into three categories based on their level of representation, which are macroscopic, microscopic, and mesoscopic [7]. Macroscopic models formulate the relationships between traffic flow, traffic speed, and traffic density. These models adopt an abstracted level of traffic details, and the simulation occurs on a segment basis approach rather than individual vehicle tracking [56]. The travel demand models associated with the macroscopic-based simulators have a prime focus on the traffic flow of vehicles and the vehicles' routing choices that are selected based on algorithms that optimize the vehicles' travel time. While microscopic models capture traffic, dynamic factors are processed in more detail [54]. Therefore, microscopic models are suitable when simulating traffic in large network areas. In these simulators, vehicles' movements are simulated according to car-following and lane-changing algorithms. Due to the high level of traffic details, these simulators are considered efficient in modeling and evaluating complex scenarios such as rush hour traffic congestion cases, complicated geometric traffic configurations, and many others [57]. Even with the aforementioned benefits offered by these simulators, microscopic models are considered time-consuming and expensive, and they suffer from calibration challenges [56]. The third model that represents some of the features of both microscopic and macroscopic models is the mesoscopic model [6]. All three of these different types of traffic simulation models are used to simulate the driving experiments and utilize their results in order to enhance the facilities and intelligent transportation systems (ITS). However, these traffic simulation systems are limited to specific scenarios, where they simulate the traffic status and interaction of vehicles under specific conditions [58]. To simulate the traffic status, simulation models primarily focus on the number of input and output parameters. The trip description is an input used to specify the destination and departure time. The second input is the network geometry layout, which describes the network's length, the number of lanes, etc. The third input is traffic flow, which indicates the number of vehicles on the network [59]. In terms of the simulation model output parameters, the outputs can be defined as the travel cost of the simulated scenario and the updated traffic flow value when the network layout has been changed. For instance, it simulates the traffic flow behavior when vehicles travel from location A to location B. The simulation's output will show the road capacity as well as how traffic congestion breaks down or spreads across different networks [60]. Therefore, traffic simulation models may have various adjustable parameters that can detail underlying traffic behavior such as vehicles' routing choices, the selection of a shorter planned path, and driving behavior.