## 4 Evacuation Model Design
### Pedestrian Evacuation Modeling (Trip Generation)  
The number of persons who will evacuate and when they leave the residence are predicted using trip generation modeling [15; 45]. [PERSON] et al. [11] and [PERSON] et al. [13] published in-depth assessments of studies on the decision process of individuals in case of a WUI fire. Both analyzed the characteristics that were discovered to impact evacuation decision-making to determine which households were more likely to leave in the event of a WUI fire. [PERSON] et al. [11] supplemented the findings from fire studies with findings from storm evacuation studies where data was lacking. Many elements were recognized as important in predicting the choice to evacuate in both assessments, including sociodemographic characteristics, received clues from the society or environment, experience and preparedness, duties as a member of a family or responsibilities in society, whereabouts, and perceived risk or danger. Furthermore, in more recent wildfire evacuation research, sentiments of self-efficacy have been discovered as a major influence [12; 52]. Another important consideration is the evacuation policy of the country, state, or municipality. Before the fire of 2009 in Australia, which had severe damage to assets and many casualties, individuals preferred to stay and defend their assets against the fire. Nevertheless, this strategy was greatly undermined after the incident, and the authorities suggested evacuation as the preferred option. Agencies, on the other hand, realize that evacuation may not always be practical and advise citizens to make arrangements for safe shelter [12]. The authorities in the United States and Canada, believing that in case of a WUI fire, the evacuation of the community is the best option, would evacuate certain zones based on the severity of the threat imposed by the wildfire [6]. Nevertheless, there are several towns in the United States where residences are designated \"shelter-in-place\" as well as places in Canada where provincial agencies cannot force evacuations, such as indigenous territories [12]. Data on the decision-making of individuals in each household during an evacuation are required to produce an appropriate decision model based on the type of incident. This model anticipates the probability of each household evacuating under a certain incident. The number of families in a specific area that will evacuate in that scenario may then be estimated using discrete choice models [31]. Generally, these data are used to define the number of participants in the evacuation process on a macro level and the decision of each individual to leave the house on a micro level. Research on hurricanes gathered a considerable amount of information on the participation rate, which is the percentage of families in a certain area that would evacuate. Nevertheless, these pieces of information were regardless of the cause of the incident. Data from many hurricanes were utilized to create participation rate models based on the type of hurricane, its movement speed, the number of individuals, and the type of residency in the area [15]. Nevertheless, data on the start time of evacuation in case of a WUI fire is pretty scarce. This aspect demonstrates the time or period when individuals leave their houses to begin the evacuation process. Normally, the departure time for a family or a neighborhood is considered the time at which the evacuation alarm from the authority is issued. This literature analysis uncovered no research that expressly conducted research on evacuation timing in the case of wildfire. However, because the timings of these occurrences may be closer to wildfires than hurricanes, US research on departure timing in no-notice disasters [53] may provide some insight. [PERSON] et al.