# Analysis of the Railway Accident-Related Damages in South Korea
## 4 Real Application
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Independent** \\ **Variables** \\ \end{tabular} } & \multicolumn{6}{c}{**Zero-Inflated Gamma Regression**} & \multicolumn{6}{c}{**Zero-Inflated Log-Normal Regression**} \\ \cline{2-13}  & \multicolumn{3}{c}{**Logit Part**} & \multicolumn{3}{c}{**Gamma Part**} & \multicolumn{3}{c}{**Logit Part**} & \multicolumn{3}{c}{**Log-normal Part**} \\ \cline{2-13}  & **Est.** & **S.E.** & \(p\)**-Value** & **Est.** & **S.E.** & \(p\)**-Value** & **Est.** & **S.E.** & \(p\)**-Value** & **Est.** & **S.E.** & \(p\)**-Value** \\ \hline Year (2008–2016) & \(-\)0.228 & 0.019 & \(<\)0.001 & \(-\)0.018 & 0.022 & 0.414 & \(-\)0.228 & 0.019 & \(<\)0.001 & 0.004 & 0.026 & 0.886 \\ \hline \multicolumn{13}{c}{Organization (vs. Metro)} \\ \hline KORAIL & \(-\)2.842 & 0.200 & \(<\)0.001 & 0.255 & 0.172 & 0.137 & \(-\)2.841 & 0.200 & \(<\)0.001 & 0.131 & 0.301 & 0.664 \\ \hline \multicolumn{13}{c}{Accident Factor (vs. Non-human-related)} \\ \hline Human-related & & & 0.485 & 0.122 & \(<\)0.001 & & & 0.459 & 0.153 & 0.003 \\ \hline \multicolumn{13}{c}{Train Type (vs. General)} \\ \hline KTX & \(-\)0.222 & 0.225 & 0.326 & 0.790 & 0.133 & \(<\)0.001 & \(-\)0.218 & 0.225 & 0.333 & 1.593 & 0.150 & \(<\)0.001 \\ Urban & \(-\)1.551 & 0.205 & \(<\)0.001 & 0.534 & 0.169 & 0.001 & \(-\)1.549 & 0.205 & \(<\)0.101 & \(-\)0.303 & 0.296 & 0.307 \\ \hline \multicolumn{13}{c}{Accident Type (vs. rolling-stock)} \\ \hline Traffic & \(-\)0.871 & 0.164 & \(<\)0.001 & & & \(-\)0.871 & 0.164 & \(<\)0.001 & & & \\ Safety & \(-\)1.103 & 0.677 & 0.103 & & & \(-\)1.098 & 0.676 & 0.104 & & \\ Miscellaneous & \(-\)0.330 & 0.156 & 0.034 & & & \(-\)0.331 & 0.156 & 0.034 & & \\ No. of Delayed Trains & 0.022 & 0.011 & 0.044 & 0.094 & 0.009 & \(<\)0.001 & 0.022 & 0.011 & 0.043 & 0.053 & 0.006 & \(<\)0.001 \\ \hline \multicolumn{13}{c}{Interaction between No. of Trains Delayed and Train Types} \\ \hline KTX & \(-\)0.003 & 0.031 & 0.918 & & & \(-\)0.004 & 0.031 & 0.902 & & & \\ Urban & 0.042 & 0.014 & 0.003 & & & 0.042 & 0.014 & 0.003 & & \\ Delay Time & 0.011 & 0.002 & \(<\)0.001 & 0.007 & 0.001 & \(<\)0.001 & 0.011 & 0.002 & \(<\)0.001 & 0.001 & 0.145 \\ \hline \multicolumn{13}{c}{Interaction between Delay Time and Train Types} \\ \hline KTX & 0.048 & 0.009 & \(<\)0.001 & & & 0.048 & 0.009 & \(<\)0.001 & & & \\ Urban & \(-\)0.012 & 0.002 & \(<\)0.001 & & & \(-\)0.012 & 0.002 & \(<\)0.001 & & \\ No. of Casualities & 0.069 & 0.118 & 0.557 & 0.149 & 0.066 & 0.024 & 0.069 & 0.118 & 0.557 & 0.197 & 0.075 & 0.009 \\ Log-likelihood & & & & \(-\)4156.3 & & & & & \(-\)3999.3 & & \\ AICC & & & & & & & & & & 8048.9 & \\ \hline \hline \multicolumn{13}{c}{Ext., Estimate: S.E., standard error; AICC, Akaike Information Criterion Corrected ([PERSON] and [PERSON] [33]).} \\ \end{tabular}
\end{table} 
Table 6: Two-Part Regression Analyses of Amount of Costs due to Railway Accidents. Table 6 illustrates the statistical results obtained from the TPM analysis on the amount of costs incurred due to the railway accidents during the 9 years' period of study. The severity of a railway accident is generally assessed by the amount of costs, which was our primary end-point variable. Therefore, we regarded some of the previously used dependent variables such as number of delayed trains and amount of delayed time as potential independent variables, so as to assess their effects on the amount of costs incurred during railway accidents. We also considered some interaction effects between the two factors in the model, for example, the number of trains delayed and train types and the amount of delayed time and train types. As mentioned earlier, we employed two different TPMs, the ZIG model and ZILN model to take care of the dependent variables having excessive zeros and extremely right-skewed distribution. Table 6 shows that the regression results of the two models are quite similar and the two parts of each model are statistically meaningful.