# Analyzing the Factors Influencing Time Delays in Korean Railroad Accidents
## 5 Analysis Results
### Technical-Related Time Delays
\begin{table}
\begin{tabular}{l r r r r r r} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{3}{c}{**Two-Stage Model**} & \multicolumn{3}{c}{**ZINB Model**} \\ \cline{2-7}  & **Estimate** & **S.E.** & \(p\)**-Value** & **Estimate** & **S.E.** & \(p\)**-Value** \\ \hline \multicolumn{7}{l}{_Negative Binomial Part_} \\ Intercept & 1.548 & 0.170 & 0.000 & 1.485 & 0.148 & 0.000 \\ Type of event & & & & & \\ High potential event & 0.090 & 0.160 & 0.592 & 0.189 & 0.147 & 0.198 \\ Train fire & 1.037 & 0.400 & 0.009 & 1.076 & 0.321 & 0.001 \\ Derailment & 0.067 & 0.210 & 0.748 & 0.427 & 0.185 & 0.021 \\ Facility type related to & & & & & & \\ Signal (or telecom.) & \(-\)0.290 & 0.036 & 0.005 & \(-\)0.236 & 0.033 & 0.000 \\ Electric power & 0.151 & 0.057 & 0.008 & 0.185 & 0.049 & 0.000 \\ Rail (or structures) & \(-\)0.166 & 0.065 & 0.010 & \(-\)0.082 & 0.058 & 0.158 \\ Cause directly related to & & & & & & \\ Others & \(-\)0.114 & 0.030 & 0.000 & \(-\)0.089 & 0.026 & 0.001 \\ Type of train & & & & & & \\ Conventional & 0.422 & 0.023 & 0.000 & 0.441 & 0.021 & 0.000 \\ Location occurred & & & & & & \\ Between stations & 0.091 & 0.023 & 0.000 & 0.092 & 0.020 & 0.000 \\ \(a\) (_overdispersion_) & 5.521 & 0.039 & 0.000 & 2.491 & 0.081 & 0.000 \\ \multicolumn{7}{l}{_Logit Part_} \\ Intercept & 1.005 & 0.342 & 0.000 & 0.907 & 0.348 & 0.000 \\ Type of event & & & & & \\ High potential event & 3.848 & 0.342 & 0.000 & 3.969 & 0.350 & 0.000 \\ Train fire & 14.794 & 378.292 & 0.968 & 14.794 & 656.015 & 0.982 \\ Derailment & 1.310 & 0.505 & 0.009 & 1.349 & 0.509 & 0.008 \\ Facility type related to & & & & & & \\ Signal (or telecom.) & \(-\)0.731 & 0.196 & 0.000 & \(-\)0.751 & 0.223 & 0.001 \\ Electric power & \(-\)0.449 & 0.357 & 0.208 & \(-\) 0.513 & 0.385 & 0.183 \\ Rail (or structures) & \(-\)1.089 & 0.289 & 0.000 & \(-\)1.183 & 0.305 & 0.000 \\ Cause directly related to & & & & & & \\ Others & \(-\)0.321 & 0.179 & 0.072 & \(-\)0.307 & 0.198 & 0.121 \\ Type of train & & & & & & \\ Conventional & \(-\)0.021 & 0.156 & 0.892 & \(-\)0.180 & 0.175 & 0.301 \\ Location occurred & & & & & & \\ Between stations & 0.204 & 0.161 & 0.205 & 0.1179 & 0.176 & 0.311 \\ LL at constant only & & & & & & \\ LL at convergence & & & & & & \\ \(\rho^{2}\) & & & & & & 0.058 \\ AIC & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Modeling outputs (delayed time) for the technical-related factors. Table 7 provides a succinct summary of the comparative predictive performance among the three models concerning technical-related factors. The model-fit \(R^{2}\) values are recorded as 0.197 for the Two-stage model, 0.209 for the ZINB model, and 0.300 for the ANN model. Noteworthy is the ANN model's outperformance, displaying a 6.6% improvement in model fit compared to the Two-stage model and a 5.9% enhancement compared to the ZINB model. These trends persist in the testing data, with a 10.6% and 6.0% improvement associated with the ANN model. Additionally, upon scrutiny of the MSE and RMSE results, the ANN model exhibits comparatively lower prediction error rates in contrast to the other two regression models.