## 3 Methods
### Dataset Preparation for DNN Model  
The big data collected by the mining safety management system installed in the study area were processed to create training data for the DNN model. The training data were created according to the following process. The big data (containing information such as the times at which the wireless APs recognized the truck tags during haulage work, the recognized truck tag IDs, and the distances between the wireless APs and tags) were collected on the web server. The big data were processed, and the information required for creating the training data (daily work time, number of dispatched trucks, production, and crusher utilization) was analyzed. The truck travel times for each interval were determined by calculating the time differences in the times at which the wireless APs recognized the truck tags. Finally, the processed data were saved in a suitable format for the training data. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Type** & **Node** & **Description** & **Unit** \\ \hline \multirow{6}{*}{Truck haulage operation} & X\({}_{1}\) & Total truck haulage operation time & Day \\  & X\({}_{2}\) & Daily working time & Minutes \\ \cline{1-1}  & X\({}_{3}\) & Number of dispatched trucks & Minutes \\ \cline{1-1}  & X\({}_{4}\) & Initial truck dispatch time & Minutes \\ \cline{1-1}  & X\({}_{5}\) & Truck dispatch interval & Number \\ \cline{1-1}  & X\({}_{6}\) & Truck loading capacity & Tons \\ \cline{1-1}  & X\({}_{7}\) & Probability of occurring loading 1 & Ratio \\ \cline{1-1}  & X\({}_{8}\) & Probability of occurring loading 2 & Ratio \\ \hline \multirow{6}{*}{Truck cycle time} & X\({}_{9}\) & Average travel time of empty & \\ \cline{1-1}  & X\({}_{10}\) & Standard deviation of travel time & Minutes \\ \cline{1-1}  & X\({}_{11}\) & Average travel time of empty & \\ \cline{1-1}  & X\({}_{12}\) & Standard deviation of travel time & \\ \cline{1-1}  & X\({}_{12}\) & of empty trucks at route 2 & Minutes \\ \cline{1-1}  & X\({}_{13}\) & Average travel time of empty & \\ \cline{1-1}  & X\({}_{13}\) & trucks at route 3 & Minutes \\ \cline{1-1}  & X\({}_{14}\) & Standard deviation of travel time & \\ \cline{1-1}  & X\({}_{14}\) & of empty trucks at route 3 & Minutes \\ \cline{1-1}  & X\({}_{15}\) & Average working time of trucks at & Minutes \\ \cline{1-1}  & X\({}_{15}\) & loading area 1 & Minutes \\ \cline{1-1}  & X\({}_{16}\) & Standard deviation of working & \\ \cline{1-1}  & X\({}_{16}\) & time of trucks at loading area 1 & Minutes \\ \cline{1-1}  & X\({}_{17}\) & Average working time of trucks at & Minutes \\ \cline{1-1}  & X\({}_{17}\) & loading area 2 & Minutes \\ \cline{1-1}  & X\({}_{18}\) & Standard deviation of working & \\ \cline{1-1}  & X\({}_{18}\) & time of trucks at loading area 2 & Minutes \\ \cline{1-1}  & X\({}_{19}\) & Average travel time of loaded & Minutes \\ \cline{1-1}  & X\({}_{20}\) & Standard deviation of travel time & \\ \cline{1-1}  & X\({}_{21}\) & of loaded trucks at route 3 & Minutes \\ \cline{1-1}  & X\({}_{22}\) & & Standard deviation of travel time & \\ \cline{1-1}  & X\({}_{23}\) & & Standard deviation of travel time & \\ \cline{1-1}  & X\({}_{24}\) & of loaded trucks at route 1 & Minutes \\ \cline{1-1}  & X\({}_{25}\) & Average working time of trucks at crushing area & Minutes \\ \cline{1-1}  & X\({}_{26}\) & Standard deviation of Working & \\ \cline{1-1}  & X\({}_{26}\) & times of trucks at crushing area & Minutes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of input nodes in DNN model. The data that were collected in the study area for 1 month in October 2018 were processed and 9072 training data points were created. Table 2 presents part of the training data created for the production prediction. For example, in the first row of Table 2, it can be seen that the 3 trucks were dispatched to the haulage operation for 420 min in a day, the initial truck dispatch time was 0 min, and the truck dispatch interval was set to 6 min.