## 6 Conclusions  
In this work, the temporal factors of truck tag recognition were extracted from the big data acquired through an underground mine safety management system, and a time study was performed to measure truck travel times. Additionally, a discrete event simulation that could simulate the truck haulage system of an underground limestone mine was designed, and a Windows-based simulation program for was developed running the simulation. Statistical analysis was performed using the big data acquired in October, November, and December 2018 to calculate the averages and standard deviations of the time spent on each truck unit operation during 579 truck haulage operations. The statistical values of truck travel time were used to perform a truck haulage simulation, and the results were compared with the results of actual haulage operations performed on January 2 and 9, 2019. The difference between \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Period**} & \multirow{2}{*}{**Statistics**} & \multicolumn{8}{c}{**Truck Travel Time (min)**} & \multirow{2}{*}{**TO 1**} & \multirow{2}{*}{**N 12**} & \multirow{2}{*}{**N 23**} \\ \cline{3-11} \cline{4-12}  & & & **DT** & **TE1** & **TE2** & **TE3** & & & **K1** & **K2** & **TL1** & **TL2** & **TL3** \\ \hline
2019-01-09 & Mean & 0.59 & 3.08 & 0.37 & 0.30 & - & 19.91 & 0.50 & 0.45 & 5.15 & 300 & 0 & 10 \\ log & STD 4 & 0.11 & 0.90 & 0.02 & 0.03 & - & 5.06 & 0.40 & 0.05 & 0.60 & & & \\ \hline \multirow{2}{*}{1 day} & Mean & 0.59 & 2.66 & 0.41 & 0.34 & - & 17.04 & 0.42 & 0.54 & 5.88 & 300 & 0 & 10 \\  & STD 4 & 0.08 & 0.00 & 0.04 & 0.07 & - & 0.25 & 0.04 & 0.01 & 0.00 & 300 & 0 & 10 \\ \hline \multirow{2}{*}{5 days} & Mean & 0.58 & 3.41 & 0.38 & 0.34 & - & 20.40 & 0.46 & 0.51 & 5.47 & 270 & 0 & 10 \\  & STD 4 & 0.08 & 0.57 & 0.03 & 0.04 & - & 2.87 & 0.07 & 0.04 & 0.34 & 270 & 0 & 10 \\ \hline \multirow{2}{*}{10 days} & Mean & 0.58 & 2.99 & 0.39 & 0.34 & - & 20.83 & 0.45 & 0.53 & 5.57 & 270 & 0 & 10 \\  & STD 4 & 0.08 & 0.40 & 0.04 & 0.04 & - & 3.72 & 0.05 & 0.03 & 0.37 & 270 & 0 & 10 \\ \hline \multirow{2}{*}{1 month} & Mean & 0.58 & 3.78 & 0.39 & 0.34 & - & 21.11 & 0.44 & 0.53 & 5.63 & 270 & 0 & 10 \\  & STD 4 & 0.08 & 0.92 & 0.06 & 0.04 & - & 4.00 & 0.05 & 0.03 & 0.47 & 270 & 0 & 10 \\ \hline \multirow{2}{*}{2 months} & Mean & 0.57 & 3.31 & 0.44 & 0.36 & 5.13 & 20.81 & 0.40 & 0.54 & 5.49 & 300 & 1 & 9 \\  & STD 4 & 0.04 & 0.56 & 0.04 & 0.04 & 0.00 & 3.33 & 0.06 & 0.03 & 0.52 & & \\ \hline \multirow{2}{*}{3 months} & Mean & 0.57 & 3.34 & 0.45 & 0.36 & 5.40 & 19.93 & 0.44 & 0.55 & 5.41 & 300 & 1 & 9 \\  & STD 4 & 0.05 & 0.56 & 0.04 & 0.04 & 0.00 & 4.51 & 0.06 & 0.03 & 0.49 & 300 & 1 & 9 \\ \hline \hline \end{tabular}  
* Total amount of loaded ore (tons), 2 No. of occurrence of K1 (times), 3 No. of occurrence of K2 (times), 4 Standard deviation.  
\end{table} 
Table 9: Comparison of the simulation results by data collection period and the haulage operation performed on 2019-01-09.  
the predicted and observed results for the total amounts of loaded ore was 30 tons and 0 tons on January 2 and 9, respectively. Moreover, the mean absolute errors between the predicted and observed mean truck travel times were 0.13 min and 0.14 min on January 2 and 9, respectively. This comparison showed that the developed truck haulage simulation could predict truck ore load, total ore production amounts, and truck wait times, which were in good agreement with actual values. Tag recognition packet data were extracted from the big data by setting the data aggregation period as 1 day, 5 days, 10 days, 1 month, 2 months, and 3 months, and then, truck travel times were measured. The results showed that accurate travel time measurements were possible when using the data that had been collected over a long period. Further, accurate simulation results were produced when the statistical values of truck travel time obtained from long-term data were entered in the simulation.