## 6 Human Perception of Severity and Objective Measures
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & \multirow{2}{*}{**B**} & \multirow{2}{*}{**Std. Error**} & \multicolumn{2}{c}{**Hypothesis Test**} & \multirow{2}{*}{**Elasticity**} \\ \cline{5-8}  & & & **Wald Chi-Square** & & **df** & **Sig.** \\ \hline Threshold & [Severity = 2.00] & \(-\)23.233 & 3.7402 & 38.585 & 1 & 0.000 & \\ \hline  & [Severity = 1.00] & \(-\)13.391 & 2.7741 & 23.300 & 1 & 0.000 & \\ \hline \(T_{2}\)\((T_{2}^{min})\) & \(-\)1.394 & 0.3234 & 18.569 & 1 & 0.000 & \(-\)0.149 \\ \hline \(D_{1}\)\((T_{1})\) & \(-\)8.183 & 1.6187 & 25.555 & 1 & 0.000 & \(-\)0.374 \\ \hline \(D_{R}\)\((D_{2}^{min})\) & \(-\)2.949 & 0.5994 & 24.214 & 1 & 0.000 & \(-\)0.114 \\ \hline PET \((T_{2})\) & \(-\)4.059 & 1.4477 & 7.860 & 1 & 0.000 & \(-\)0.208 \\ \hline \multicolumn{8}{c}{AIC = 116} & BIC = 160 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: MODEL C.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & \multirow{2}{*}{**B**} & \multirow{2}{*}{**Std. Error**} & \multicolumn{2}{c}{**Hypothesis Test**} & \multirow{2}{*}{**Elasticity**} \\ \cline{5-8}  & & & **Wald Chi-Square** & & **df** & **Sig. ** & \\ \hline Threshold & [Severity = 2.00] & \(-\)93.998 & 5.7783 & 264.627 & 1 & 0.000 & \\ \hline  & [Severity = 1.00] & \(-\)48.870 & 3.1290 & 243.936 & 1 & 0.000 & \\ \hline \(D_{1}\)\((T_{1})\) & \(-\)2.129 & 0.2511 & 71.917 & 1 & 0.000 & \(-\)0.138 \\ \hline PET \((T_{2})\) & \(-\)0.410 & 0.0508 & 65.161 & 1 & 0.000 & \(-\)0.210 \\ \hline \(T_{2}\)\((\)EA\()\) & \(-\)15.616 & 1 indicators were statistically associated with the human judgement of severity in both subsets. The models can be represented as below:  
MODEL B: **Severity rating by human observers (With Evasive action, N = 905)**-Indicators from moments: (\(T_{2}^{min}\)), (\(D_{2}^{min}\)), (\(T_{1}\)), (\(T_{2}\)). MODEL C: **Severity rating by human observers (With No Evasive action, N = 236)**-Indicators from moments: (\(T_{2}^{min}\)), (\(D_{2}^{min}\)), (\(T_{1}\)), (\(T_{2}\)). Models B and C were compared with the aim of determining whether the indicators associated with the evasive and non-evasive subsets were similar. The findings, presented in Tables 6 and 7, revealed that \(D_{1}\) (\(T_{1}\)) had the most considerable influence on both models, irrespective of the presence of other significant variables at different time intervals during the event, apart from (\(T_{1}\)). Thus, it can be inferred that this group of variables could be employed for all events, irrespective of whether there was any evasive action taken or not. Finally, the last model was developed using only those events where evasive action was observed, and all of the variables presented in Table 1 were considered. This model aimed to determine whether or not the indicators from the moment of evasive action were helpful in estimating the severity with respect to the human observer ratings. The model can be described as below:  
MODEL D: **Severity rating by human observers (With Evasive action, N = 905)**-Indicators from moments: EA, (\(T_{2}^{min}\)), (\(D_{2}^{min}\)), (\(T_{1}\)), (\(T_{2}\)). Models B and D have been compared to evaluate the impact of the variables estimated at the moment of the first evasive action. Tables 6 and 8 reveal that the significant variable sets for both models varied in terms of their emphasis on different stages of the event. The highest contribution of variables in Model B are the moment of \(D_{1}\), (\(T_{1}\)) and PET (\(T_{2}\)), whereas, in Model D, the variables associated with the moment of evasive action (EA) have the greatest importance. Akaike information criteria and Bayesian information criteria are used in this study to compare the models as the smaller the values, the better the model [45]. The AIC and BIC scores are defined as below:  
\[ \begin{array}{l}\text{BIC}=\text{K}\times\text{ln}\;\text{(N)}-2\times\text {ln}\text{(l)};\\ \text{AIC}=2\times\text{K}-2\times\text{ln}\text{(l)}.\end{array}