## 6 Results
### Application of Reactive Data in the Crash Severity Model  
The unsatisfactory model performance suggest that the data used for model development is not sufficient or sufficiently informative to enable accurate prediction of crash outcomes and separation of severity classes in the crash dataset. This could be caused by excessive noise in the data, the quality of the data, or missing important features in the data. To address the potential shortcoming related to missing features, the input data was expanded by adding the reactive data, specifically the driver age and vehicle age variables. As noted in the literature review, the studies have shown that driver and vehicle characteristics have a significant impact on crash occurrence and their severity outcomes [14; 15; 16]. Despite the critical impact of the factors described by reactive data on the crash severity outcomes, the main challenge of using the reactive data for operational crash prediction is that they are not available in real-time. To overcome this problem, the crash records in the crash severity model were divided in classes based on the driver age and vehicle age, which was provided for each crash record in the NJDOT crash records database. It should be noted that as one aims to investigate the impact of driver age and vehicle age on crash outcomes, the driver injury severity level should be considered as the dependent variables rather than the most severe crash injury in the combined studies. The summary of number of records included crash classes by driver age and vehicle age characteristics is provided in Table 9. The case study dataset contained 12,566 driver records, with 11,059 (88%) records of non-injury cases and 1507 (12%) records of injury and fatality cases. The RF models was calibrated for each driver/vehicle age class to predict the driver injury severity. The RF method was selected as it outperformed other investigated models for the crash likelihood and crash severity analyses using the proactive data (see Section 5). The performance statistics for the RF models considering the driver age and vehicle age is summarized in Table 9. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Hyperparameters** & **Accuracy** & **Sensitivity** & **Specificity** & **AUC** \\ \hline RBLR & Not applicable & 0.67 & 0.41 & 0.73 & 0.59 \\ RF & mtry = 16, split rule = gini, node size = 1, sample size = full training set & 0.68 & 0.46 & 0.72 & 0.61 \\ GBM & ntree = 250, interaction.depth = 5, shrinkage = 0.1, n.minobismode = 10 & 0.80 & 0.08 & 0.96 & 0.58 \\ GNB & Not applicable & 0.67 & 0.41 & 0.73 & 0.58 \\ KNN & K = 5 & 0.61 & 0.40 & 0.66 & 0.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Crash severity models’ performance summary and Hyperparameters. The model performance metrics presented in Table 9 indicates that the average AUC value incased to 0.64, a 4-percentage point increase compared to the crash severity model that did not account for driver and vehicle age. An improvement was also achieved in terms of model sensitivity, which increased by 8-percentage points, from 0.46 in the crash severity model, to 0.54 in the driver severity model. Nevertheless, despite the improved performance, the accuracy and predictive power of the resulting models should be further improved to be used in operational crash severity prediction with greater reliability. Another challenge to applying this model in practice is that the information about individual drivers and vehicles is not known in real time. In fact, as noted earlier, the information about the drivers and vehicles participating in reported crashes is only known after the crashes occurs. Nevertheless, one way of overcoming this challenge is to use as inputs the relative shares of drivers by age and vehicles by age in the total driver population and vehicle fleet respectively. These relative shares could be estimated for a given roadway section or analysis area, and for given analysis time frame (e.g., AM peak, PM, peak, weekday, weekend, etc.).