## 4 Discussion
By identifying high demand lodging areas, transportation agencies can modify evacuation routes and collaborate with hotel operators to manage the influx of evacuees. Future integration with real-time GPS, social media monitoring, and sensor-based traffic data will facilitate adaptive disaster response, helping to reduce congestion and prevent shelter overflow. Government agencies can leverage spatiotemporal demand insights to advocate for funding for emergency shelters, evacuation infrastructure, and real-time monitoring systems. By implementing these insights, policymakers can develop more flexible, data-driven disaster response frameworks, ultimately enhancing evacuation efficiency and improving public safety. This approach not only improves the precision of predictions but also provides emergency managers with actionable insights that can be used to optimize shelter locations, capacities, and operational logistics during concurrent disasters like hurricanes and pandemics, as was the case for Hurricane Sally and COVID-19. _Challenges and Limitations:_ Several challenges and limitations were encountered during the study. This study relies on connected vehicle data, covering approximately 11% of Florida's total vehicles, introducing potential sampling biases. Although the dataset provides broad spatial coverage, some vehicle types may be over- or under-represented. Future analyses could incorporate vehicle registration databases or traffic sensor data to validate fleet composition. Additionally, the lack of unique vehicle identifiers due to privacy restrictions prevents full trip-chain reconstruction, limiting the ability to track multi-stage evacuations. To address this, future research could integrate additional mobility data sources, such as mobile phone location tracking, toll booth records, or survey-based evacuation reports, to improve the resolution of trip chain reconstruction and account for missing intermediate travel stages. Another critical assumption that may impact the accuracy of the model is that each room in a lodging facility accommodates four people. This assumption does not fully capture the variability in actual occupancy, which can be influenced by factors such as family size, individual preferences, and the nature of the emergency, potentially leading to overestimations or underestimations of lodging demand and so available capacity. Future studies should consider incorporating additional data sources, such as mobile phone location data, social media activity, and survey data, to calibrate and improve the accuracy of the predictions. _Comparative Analysis of Shelters and Lodging Facilities:_ The study revealed significant differences in demand patterns and model performance between shelters and lodging facilities. The model showed higher accuracy for shelters, likely due to their more regulated and centralized operations during disasters, unlike the decentralized nature of lodging facilities. Uniform vehicle occupancy assumptions may not capture the variability in lodging facility demand, influenced by factors like family size, stay duration, and travel purpose. Additionally, the assumption that each room accommodates four people complicates accurate demand prediction, emphasizing the need for refined assumptions and more comprehensive data inputs to improve model accuracy for lodging facilities. Potential for Future Research and Model Refinement:The study opens avenues for future research and model refinement, including integrating behavioral models to capture evacuees' decision-making processes such as route choices, evacuation timing, and shelter preferences. Aggregating trips into areal units like census block groups could help infer evacuees' demographic characteristics and explore factors affecting the model's predictive variance for lodging facilities. Incorporating advanced machine learning techniques, such as ensemble methods (Random Forest, XGBoost) and hybrid ML-GAM approaches, could enhance predictive accuracy by identifying complex data patterns.