## 3 Results  
The testing set was randomly selected from areas on the map with a high concentration of shelters and lodging facilities. Specifically, it included the following shelters: S1, S7, S17, S21, S22, S23, and S24, and the following lodging facilities: L1, L2, L3, L4, L12, L22, L23, L28, L29, L30, L53, L56, L57, L58, L60, L63, L71, and L73. The remaining data, consisting of 37 out of the 44 shelters and 105 of the 123 lodging facilities, was used for training. The data \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Model** & **Strengths** & **Limitations** \\ \hline GAM (our approach) & High interpretability, allowing policymakers to assess individual predictor contributions. & It assumes smooth effects, and may underperform for highly complex spatial dependencies. [42] \\  & Samples spatial smoothing (e.g., longitude-latitude effects). [42] & \\ \hline ML-GAM (Machine Learning-GAM Hybrid) & Retains interpretability of GAMs while leveraging machine learning for feature selection and optimization. [42] & Requires careful model tuning to balance interpretability and complexity. [42] \\ \hline Convolutional Neural Networks (CNNs) & Strong in detecting spatial patterns in image-like data. [43] & Requires large training datasets and lacks interpretability for decision-makers. [43] \\  & Useful for remote sensing and satellite-based disaster modeling. & Less suited for tabular movement data like vehicle tracking. \\ \hline Recurrent Neural Networks (RNNs)/ LSTMs & Captures time-series dependencies well. Effective for sequential mobility forecasting. [44] & Computationally expensive, it suffers from vanishing gradient issues. Requires extensive hyperparameter tuning. \\ \hline Agent-Based Models (ABMs) & Simulates individual decision-making in evacuations. Can incorporate social-behavioral dynamics. [42] & Computationally intensive for large populations. Requires fine-tuned assumptions on agent behavior. [42] \\ \hline XGBoost & High predictive accuracy and efficiency due to gradient boosting. [26] & Requires extensive hyperparameter tuning for optimal performance. [26] \\ \hline Random Forest (RF) & Robust to noise and handles high-dimensional data well. [44] & Lacks interpretability; feature importance is difficult to translate into actionable policy insights. [44] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative evaluation of modeling approaches. was analyzed using the method described in Section 2, and predictions were made using the model defined in Equation (1) for the GAM model and Equation (9) for the benchmark GLM. Table 3 presents the features used in the dataset along with their respective units of measurement. Note that the proposed GAM models evacuation and sheltering dynamics on a daily basis; therefore, vehicle counts at the facilities are aggregated daily. This daily sampling provides sufficient temporal resolution for emergency management decision-making. Tables 4-7 summarize the results of the GAM and GLM models using the training and testing datasets for lodging facilities and shelters. Numbers in brackets indicate the \(p\)-value of the correlation estimates. A \(p\)-value higher than 0.05 mean correlation is not significantly different from 0 at the 0.05 level of significance. It can be seen that the negative correlation estimates have large \(p\)-values and are not different from 0. The results of the GAM model indicate that the model performs better for lodging facilities compared to shelters, as evident from the metrics in both tables. For lodging facilities, the training and testing RMSE values are 4.034 and 3.421, respectively, showing reasonable prediction errors, with the test dataset having slightly lower errors. The correlations (0.5485 for training and 0.2096 for testing) show a moderate predictive performance for the training set and a weaker performance for the test set. However, the MAPE values (108.76% for training and 113.80% for testing) highlight room for improvement in predicting relative errors.