## 2 Materials and Methods
### Study Region and Dataset  
Hurricane Sally made landfall on 16 September 2020, with an incident period between 14 September and 28 September, causing severe damage across parts of the State of Florida, particularly in the Panhandle. Following the storm, the US president issued a major disaster declaration on 23 September 2020, enabling federal assistance to support recovery efforts in the affected areas [31]. Figure 1 shows this study's region of interest with the intersected Hurricane Sally track. Sally's track passed diagonally across Escambia County from the middle of the southwest. It brought widespread winds and heavy rainfall that seriously impacted the western Florida Panhandle. Schools in several Northwest Florida counties were closed as a precautionary measure. Santa Rosa and Okaloosa counties resumed classes on 21 September, while Escambia County schools reopened on 23 September. These closures began on September 15 and were implemented to ensure student safety amid the storm's impact. Exceptions were made for certain schools based on specific circumstances within the districts [32; 33]. Ahead of the Hurricane Sally landfall, the Florida Governor issued Executive Orders 20-224 and 20-225 order, which officially declared a state of emergency in Escambia, Santa  
Figure 1: The region of interest intersected with Hurricane Sally’s track. Rosa, Bay, Calhoun, Franklin, Gadsden, Gulf, Holmes, Jackson, Liberty, Okaloosa, Walton, Washington counties [34]. The orders mandate that all public facilities, including elementary and secondary schools, community colleges, and state universities, must be available as (congregate) shelters upon request by local emergency management agencies. In addition, due to the concurrent public health emergency due to the ongoing COVID-19 pandemic, the executive order also included provisions for the state officials to activate agreements to use hotels as non-congregate shelters to protect evacuees from potential virus exposure and to maintain social distancing. The datasets used for this study consist of GPS movement data, shelter and lodging facility wait time, and vehicle count data for 44 shelters and 123 lodging facilities in Florida's Santa Rosa, Escambia, and Okaloosa counties from 01 to 30 September 2020. The geographical location and capacity information for each of these facilities were sourced from [35]. It is important to note that the dataset covers approximately 11% of Florida's total registered vehicles, based on the data provider's fleet coverage. While this represents a subset of the population, it captures a broad spatial distribution across the study region, ensuring key evacuation routes and urban-rural movements are reflected. The dataset includes personally owned vehicles, likely including those used for Uber and ride-sharing services. It includes various vehicle types, such as 11.8% sedans, 54.5% SUVs/MPVs, and 26.5% pickup trucks, though exact breakdowns remain anonymized. The dataset does not include government and emergency vehicles. Given this limitation, potential biases in vehicle representation are acknowledged. However, it is important to highlight that similar studies have effectively utilized datasets with significantly lower rates. For example, [PERSON] et al. [36] reported penetration rates between 2.31% and 4.39% in New Jersey and found them sufficient for robust traffic analytics. [PERSON] et al. [37] observed median penetration rates of approximately 4.5% across Indiana, Ohio, and Pennsylvania, confirming their adequacy for performance evaluations. [PERSON] et al. [38] used connected vehicle data with 3-5% penetration to analyze speed compliance in work zones. Given that our dataset's 11% coverage exceeds these benchmarks, we believe it offers a robust and representative sample for modeling evacuation behaviors. Additionally, due to privacy concerns, no information can be extracted from the vehicle ID numbers provided. Figure 2 shows the lodging facility and shelter locations considered in this study.