## 4 Results  
Big data were collected for three months, from October 1 to December 31, 2018. The overall number of packet data was 7,065,591. Of these, the number of truck tag recognition packet data was \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Data** & **Unit** \\ \hline \multirow{8}{*}{**Simulation parameters**} & Total simulation time & Days \\  & Daily working time & Minutes \\  & Number of trucks & Number \\  & Initial truck generating time & Minutes \\ \cline{2-3}  & Truck dispatch interval & Minutes \\  & Capacity of a truck & Tons \\ \hline \multirow{8}{*}{**Inputs**} & Travel time of the empty truck & TE1 & Minutes \\  & Travel time of the empty truck & TE2 & Minutes \\  & Travel time of the loaded truck & TE3 & Minutes \\ \cline{2-3}  & Travel time of the empty truck & TL1 & Minutes \\  & Travel time of the loaded truck & TL2 & Minutes \\  & Travel time of the crash & TL3 & Minutes \\ \cline{2-3}  & Working time & K1 & Minutes \\  & Travel time & K2 & Minutes \\ \cline{2-3}  & Dumping time & DT & Minutes \\ \hline \multirow{8}{*}{**Outputs**} & Total simulation time & Minutes \\  & Total amount of the loaded ore & Tons \\ \cline{1-1}  & Utilization rate of the crusher & Percentage \\ \cline{1-1}  & Average waiting time for dumping & Minutes \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Description of the input and output data of the truck haulage system simulation. 808,130. Only tag recognition packet data were extracted from the big data, and they were classified according to the number of truck haulage operations. Table 4 shows the tag IDs for the UWB and 900 MHz tags attached to the 4 trucks that performed haulage operations in the study area, and it shows the number of haulage operations performed by each truck. The number of haulage operations performed by the 4 trucks was 579, which was comprised of 268, 277, and 34 operations performed in October, November, and December, respectively. When the number of haulage operations was found to be 0, it was because the truck performed haulage operations at a different operation site during that month. To measure truck travel time, this study extracted the times at which the wireless APs recognized the tags for each number of haulage operations, and then, it calculated their differences. However, as measured truck travel times include the times that occur as exceptions, such as meals and excessive waits, it was necessary to remove the abnormal values that deviated considerably from the average. In this study, the average and standard deviation were calculated for each unit of travel time. The data that exceeded the mean value by more than three standard deviations were considered to be abnormal values and removed. The cumulative relative frequency of the data was calculated, and the values with a cumulative relative frequency of less than 0.05 or larger than 0.95 were deleted. Table 5 shows the results of using statistical analysis to calculate the quantity, average, and standard deviation of measured truck unit travel time data. Regarding the time during which a truck left W03 and performed the load operation and then returned to W03, two data sets (K1, K2) were classified according to the distance to the loading site. K1 is the case in which a truck travels from W03 to a relatively close loading site, and K2 is the case in which a truck travels from W03 to a long-distance loading site. There were relatively fewer data for the TE1 time compared with other times because the TE1 times could not be obtained as there were no times at which the crushing site wireless APs recognized the truck tags during the beginning of the day's first haulage operations. Figure 6 shows histograms for truck unit travel time data. All truck travel time data are shown in the form of a normal distribution. The GPSS/H simulation codes were modified to obtain the time spent on each unit operation according to a normal distribution in the simulation.