## 2 Literature Review
### Prediction of Incident Duration with ML Models
Results suggest that SVM accuracy outperformed the other models [33]. [PERSON] et al. developed models based on SVM, KNN, DT, ANN, gradient boosting, and multivariate linear regression (MLR) prediction of incidents [34]. Among these, gradient boosting, followed by ANN and MLR, was found to be the best model for better prediction accuracy. [PERSON] et al. employed four machine learning models: SVM, K-nearest neighbor (KNN), RF, and BPNN [18]. They found that the RF achieved better results compared to other models. In another study, [PERSON] developed eight statistical and machine learning-based incident prediction models, including LR, Bayesian logit regression, DT, KNN, ANN, and gradient boosting, using 11,418 incidents. The results revealed that the RF performs better than the other models [35]. Additionally, [PERSON] et al. developed four machine learning models, namely BPNN, SVM, long short-term mem ory (LSTM), and RF, using 550 incidents. They found that LSTM demonstrated superior performance [36]. The above existing studies are primarily focused on urban interstates. Despite their significant effectiveness in urban interstates, a significant research gap exists concerning rural interstate scenarios. Rural interstates are characterized by longer incident durations and slower emergency responses, presenting unique challenges that are not typically encountered in the urban datasets used in prior studies. This gap highlights the need to adapt and refine ML models to suit the specific conditions of rural interstates. Neural networks have been found to be an effective methodology for classification and regression tasks. However, they are prone to overfitting when using a limited dataset. Additionally, neural networks have been observed to have limited external validity when employed in analyzing diverse datasets. Also, it is difficult to determine the optimal architecture of neural networks and the number of hidden layers in the structure. Distant learning methods are difficult to interpret when used for complex data classification tasks. Overfitting, underfitting, model interpretation, and model training time are computationally expensive and time-consuming. To address these issues inherent to neural networks and distant learning methods, it is essential to provide adequate attention to tree-based ensemble-boosting approaches. For instance, XGBoost is scalable and precise and extends the computational capacity of tree algorithms. LightGBM offers several benefits, such as efficient model training and low memory consumption. Furthermore, the CatBoost model is employed as a series of decision trees that iteratively minimize loss. These advantages of the tree-based ensemble boosting algorithms led this study to consider these models for predicting and classifying incident duration. In addition, a comparative analysis of the accuracies of these models will be beneficial to agencies in accurately predicting incident duration to put in place measures to adequately respond to incidents. Several statistical parameters were used to evaluate the performance of the developed models, and the results showed that XGBoost outperformed the other models in the prediction of ICT.