## 2 Literature Review  
### Prediction of Incident Duration with ML Models  
Several studies have been conducted in recent decades to model and analyze incident duration [12; 13; 14; 15]. The first objective of this study is to predict incident duration, which is typically the primary focus of ML methods. Due to the flexible framework of these ML algorithms, they can handle the complex and highly nonlinear relationship between input and output features. The most commonly used ML methods are the tree-basedmethod [16], neural networks method [17], distance metric learning method [18], and hybrid methods [19]. Ensemble learning methods have also been used to predict incident duration in the previous literature [20; 21; 22]. The majority of ensemble learning models use tree-based models. Examples of tree-based models include decision trees (DT), random forests (RF), and gradient-boosting decision trees (GBDT). In contrast to other ML techniques, tree-based models are able to rank explanatory variables according to importance and predict duration with a high degree of accuracy [16; 23]. [PERSON] et al. developed an incident duration prediction model based on gradient-boosting decision trees (GBDT) using incident and weather data [24]. The results showed that the GBDT model performed better than the random forest (RF), backpropagation neural network (BPNN), and support vector machine (SVM) methods in terms of prediction accuracy and model interpretation power. Another study by [PERSON] et al. for incident duration prediction utilized tree-based modeling, such as RF, XGBoost, LightGBM, and GBDT, along with K-nearest neighbors (KNN) and linear regression (LR) [25]. They found that XGBoost outperformed the other models. [PERSON] et al. developed an M5P tree algorithm for the prediction of lane clearance time [16]. The results showed that the M5P model outperformed both the decision tree model and a traditional regression model created with the same data. [PERSON] et al. applied XGBoost along with LightGBM and CatBoost for the prediction of incidence clearance time [12]. The comparative analysis showed that XGBoost outperformed the other two models. Neural networks have been used in the past for incident duration prediction [14; 26; 27; 28]. A study conducted by [PERSON] and [PERSON] developed an ANN model to predict ICT and used the sequential model to update the clearance time phase. The mean absolute percentage error (MAPE) for the predicted models was almost below 40%, indicating that the suggested models have a satisfactory level of prediction ability [17]. [PERSON] et al. developed an SVM and ANN model using 235 incidents. They found that for predictions of a longer duration, ANN performed better than the SVM model [29]. Additionally, [PERSON] et al. developed an incident prediction model employing ANN and KNN [30]. They found that the ANN model accurately predicted incident duration with an MAE of less than 30%. Another study by [PERSON] et al. in Florida developed a mixture density network (MDN)-based model by utilizing 4 years of data (2014-2017) from 58,167 incidents. ANN and XGBoost models were employed to assess the performance of the developed MDN model in predicting incident duration. The comparison indicated that the MDN model had superior performance when compared to the other two models because it achieved the lowest errors [31]. Distance metric learning methods work by finding the most efficient classifier by using a selected distance function [32]. Common distance metric learning methods include the support vector machine (SVM), K-nearest neighbor (KNN), and principal component analysis (PCA). [PERSON] et al. developed incident prediction models employing various machine learning techniques, including DT, RF, logistic regression (LR), naive Bayes (NB), SVM, discriminant analysis (DA), Gaussian process regression (GPR), MLP, stochastic gradient descent (SGD), and extra trees (ET). The model was trained on 110,000 incidents collected from the Houston TranStar incidents archive.