## 4 Evacuation Model Design
### Traffic Modeling (Evacuation Routes Selection)  
This stage of evacuation modeling forecasts the routes individuals choose during an evacuation process to reach safety. Evacuees might use major highways, rural backroads, and a mix of these routes to depart an impacted region and seek safety. While people might probably leave a wildfire-affected region on foot, it is assumed that WUI fires are mostly evacuated by automobiles. The data available to create a model specific for WUI wildfires are low since all the data in this scope are restricted to flood or hurricane evacuation. Research shows that in case of an emergency, individuals choose the routes already familiar to them rather than the fastest or shortest routes to their destination [64]. Moreover, compared to the individuals' past experience, factors such as media and news, emergency protocols, or maps have smaller effects on route choice [41]. In addition, factors like the type of route (highway, interstate roads, detours), route availability, route length, and services along the route (gas stations, rest areas, network coverage) all affect the choice of route [65].  
Driving behavior can be influenced by the chosen evacuation route, among other social and environmental variables. In non-emergency situations, research from [PERSON] et al. [66] demonstrated that as the time of passing through a specific road by a certain individual increases, the driving speed increases, which corresponds to familiarity.  
A research work carried out by [PERSON] and [PERSON] [67] on traffic data for evacuation under the threat of a hurricane found that there are considerable differences in the dynamics of the traffic during an evacuation and a non-emergency situation. To restrict outward flow rates utilized in macroscale models, they established two quantities: \"maximum evacuation flow rates\" (MEFR) and \"maximum sustainable evacuation flow rates\" (MSEFR). There was minimal data on individual vehicle driving habits, such as the gaps between vehicles, following other cars, and changing lanes under emergency settings at greater levels of refinement. Since scientists believe that the data extracted from non-emergency situations could not be applied to emergencies, this result could not be applied to evacuation modeling [15].  
[PERSON] et al. investigated the relationship between road network connectivity and wildfire fatalities over the past two decades [68]. This work developed a graph-based connectivity index model on different scales and investigated the impact of wildfires on different road networks. The results from this work showed a strong negative relation between the connectivity index and fatalities, which confirms the fact that a well-interconnected network with various alternative routes provides safer roads and lesser traffic and increases the possibility of safe evacuation. The authors identified the inapplicability of this model to cases with several communities and dense population distribution since these cases have higher numbers of fatalities, which this model underestimates. Also, the small sample size and inconsistency in evacuation policies were mentioned as limiting factors to this model, which limits its usage at this stage [68]. Figure 1 demonstrates the overall impacting factors and required data for designing an evacuation model.