## 3 Materials and Methods
### Methodology and Algorithms
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Category** & \multicolumn{1}{c}{**Involving Factors**} & **Code** \\ \hline \multirow{8}{*}{Collision} & Year & C\_YEAR \\  & Month & C\_MNTH \\  & Day of week & C\_WDAY \\  & Collision hour & C\_HOUR \\  & Collision severity (At least one fatality, Non-fatal injury) & C\_SEV \\  & Number of vehicles involved in collisions & C\_VEHS \\  & Collision configuration (Single Vehicle in Motion, Two Vehicles in Motion-Same or Different & C\_CONF \\  & Direction of Travel, Two Vehicles-Hit a Parked Motor Vehicle) & C\_RCFG \\  & Roadway configuration (e.g., Non-intersection, Intersection, Railroad level crossing, Bridge,...) & C\_WTHR \\  & Weather condition (e.g., Clear and sunny, Overcast cloudy, Raining, Snowing, Freezing Rain,...) & C\_WTHR \\  & Slee, Hail, Visibility limitation, Strong wind) & C\_RSUR \\  & Road surface (e.g., Dry, normal, Wet, Snow, Slush, Icy, Muddy, Flooded,...) & C\_RSUR \\  & Road alignment (e.g., Straight and level, Straight with gradient, Curved and level,...) & C\_RALN \\  & Traffic control (e.g., Traffic signals, Stop sign, Yield sign, Warning sign,... ) & C\_TRAF \\ \hline \multirow{3}{*}{Vehicle} & Vehicle sequence number & V\_ID \\  & Vehicle type (e.g., Light Duty Vehicle, Panel/cargo van, Bicycle,...) & V\_TYPE \\  & Vehicle model year & V\_YEAR \\ \hline \multirow{8}{*}{Person} & Person sequence number & P\_ID \\  & Person sex (Female, Male, Unknown) & P\_SEX \\  & Person’s age & P\_AGE \\  & Person position (e.g., Driver, Front row, Second row, Third row, Position, Sitting on someone’s & P\_PSN \\  & Iap, Outside passenger compartment, Pedestrian) & P\_ISEV \\  & Medical treatment required (e.g., No Injury, Injury, Fatality) & P\_SAFE \\  & Safety device used (e.g., No safety device, Helmet worn, Reflective clothing worn,...) & P\_SAFE \\  & Road user class (e.g., Motor Vehicle Driver, Motor Vehicle Passenger, Pedestrian, & P\_USER \\  & Bicyclist, Motorcyclist) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Factors in National Collision Database of Canada [29]. rescale the data and make all of them consistent. Classification needed a two-step process: the learning step and the prediction step. The dataset was divided into 70% for training and 30% for testing and passed to the Auto-Sklearn classification model. This model was run for different total and single run time limits up to 24 h and 30 min, respectively. Table 3 exhibits the performance of 8 classifiers and corresponding metrics for the fastest models (outcome of total and single run time limits up to 120 and 30 s, respectively). Because the balanced dataset was used for the analysis, the accuracy metric could be used to evaluate the preferred classifier. For all examined models, the maximum test score and balanced accuracy were 0.67 and for the top 82 classifiers, these values remain above 0.6. Among the top high score classifier, two models of Decision Tree (DT) and Random Forest (RF) were used to find the dependency of the influencing factors of the collision with the first responders. These models are intuitive and interpretable which priories them when they have acceptable accuracy for the highly regulated domains [36]. In the DT, a model that predicts the value of a target variable is developed. It presents the rules that are inferred from the data criteria (or features in machine learning terminology). DT was used here to indicate the rules connecting the factors to the dependent variable. The RF is a supervised learning algorithm that determines the importance of factors in a dataset. It was employed to highlight the most important collision factors and rank their relative importance in the collision severity. To obtain the performance in the range of high-ranked classifiers in the Auto-Sklearn process, a grid-search algorithm was applied to the dataset to tune the hyperparameters of each classification model. By using the k-fold cross-validation and grid-search methods, the best models and tuning conditions for their hyperparameters were found.