## 3 Methods
### Measuring Truck Travel Times from Big Data  
Only truck tag recognition packet data were extracted to consider only the temporal parameters required to measure truck travel times from big data. Then, the truck tag recognition packet data were classified according to the number of haulage operations. Figure 3 shows the features of the wireless APs that are passed while a truck is hauling ore in the underground mine. The last number in a wireless AP's IP address is the same as the order in which the wireless AP was installed. W01 at the underground mine's entrance was installed first, and therefore, the last number in its IP address is 1. The deeper the wireless AP is located within the mine, the larger the last number in its IP address. In other words, as a truck hauls ore, the last numbers in the IP addresses of wireless APs in the tag recognition data increase from 1 to the maximum and then decrease back down \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Protocol**} & \multicolumn{2}{c}{**Tag Recognition Packet**} & \multicolumn{2}{c}{**Environment Monitoring Packet**} \\ \cline{2-5}  & **Bytes** & **Frame** & **Bytes** & **Frame** \\ \hline Start of text & 1 & STX & 1 & STX \\ \hline \multirow{6}{*}{Command} & 1 & Data type & 1 & Data type \\  & 1 & Emergency type & 2 & Temperature \\  & 1 & Tag sequence & 2 & Humidity \\  & 6 & Tag ID & 2 & Earthquake \\  & 1 & Distance or power & 1 & Electric power \\  & 1 & Distance or power & 1 & Battery \\  & & & 1 & LED on/off \\  & 7 & Null & 7 & Null \\ \hline Checksum & 1 & CHKSUM & 1 & CHKSUM \\ \hline End of text & 1 & ETX & 1 & ETX \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Communication protocol frame of the packet data. Figure 3: Diagram showing the sequence of wireless APs in the underground mine. to 1. Therefore, the 1 st wireless AP tag recognition time was searched for, and then, the 1 st wireless AP tag recognition time was searched for again. The data that existed between the two time instances were classified as having occurred during the same haulage operation. The data that were classified according to the number of truck haulage operations were combined with the tag recognition packet data obtained from the crushing site (see Figure 4). When trucks loaded with ore travel away from W01 to the crushing site, the wireless AP installed at the crusher (WC) recognizes the trucks. When a truck drops the ore off in a hopper and then re-enters the portal of the underground mine to load ore, W01 recognizes the truck. The time at which WC recognizes the truck must be larger than the time at which W01 finally recognizes the truck in the previous haulage operation, and it must be less than the time at which W01 first recognizes the truck in the subsequent haulage operation. If this principle was satisfied, the data were combined. If not, it was determined that the time parameters for measuring truck travel times could not be extracted from the two data, and they were deleted. To measure truck travel times, this study extracted the tag recognition times of all wireless APs from the data that were classified as having the same number of haulage operations. When a truck is stopped within a tag recognition range for a long time, the tag is continually recognized by a single wireless AP, and a large amount of tag recognition temporal data accumulate. In such cases, distance packet information was used to extract tag recognition time as the time when the distance between the wireless AP and truck was the minimum. The difference in tag recognition times was calculated, and truck travel time was measured.