## 6 Conclusions  
In this study, we developed a system that could collect and analyze log data related to truck transport times in underground mines using Bluetooth beacons and tablet computers. We presented the results of analyzing truck transport routes by applying the system to domestic limestone underground mines. Log data related to the transport times of trucks were continuously collected through tablet computers and uploaded to a cloud server. The log data processing application developed was able to compile log data files uploaded to the cloud server into a single data file to calculate the statistics for each section of a transport route, making it easy to determine the characteristics of the truck transport time.  
Through spatial analysis using the average truck transport time and standard deviation, it was possible to select the sections in which a truck's transport times were expected to be stable and sections in which they were expected to be unstable. For the selected sections, time-series analysis was performed by dividing the log data into weekly units. The transport routes were then appropriately classified. Moreover, through analysis of the production logs at a site and interviews with employees, it was possible to determine the reasons for these sections appearing stable or unstable.  
The log data collection system related to truck transport times using Bluetooth beacons and tablet computers developed in this study can be used to analyze and diagnose truck transport routes in mines and improve haulage operations. The transport times of trucks may increase abnormally because of factors such as congestion in mines due to the opening of new workplaces, the centralization of workplaces based on daily production plans, inefficient equipment dispatch plans, or detours because of vehicle failures. However, by comparing and analyzing log data files continuously accumulated in the cloud and newly collected log data files, it is possible to prevent an increase in the truck transport time in advance. This will help improve the productivity and profitability of a mine, as it improves the efficiency and effectiveness of its haulage system.  
The log data collection and analysis system developed in this study can only analyze log data uploaded to the cloud after production work has been completed. Consequently, it is not possible to compare and review log data collected during production using existing log data in real time. However, if a real-time comparison and review system can be established to compare existing log data with log data collected in real time during production, significant improvements in the efficiency and productivity of haulage operations can be realized. Additionally, it is believed that if the system is improved to accurately measure the time required for loading and dumping of ones, analysis and diagnosis of the entire process of truck haulage operations will be possible. Therefore, in a future study, we intend to develop a system that can compare and review the truck transport time and accurately measure the time required for loading and dumping of ones as collected through a tablet computer installed on a truck with existing data in real time.  
Conceptualization, Y.C.; Data curation, Y.C.; Funding acquisition, Y.C.; Investigation, S.P. and Y.C.; Methodology, S.P. and Y.C.; Project administration, Y.C.; Resources, Y.C.; Software, Y.C.; Supervision, Y.C.; Validation, S.P.; Visualization, S.P.; Writing--original draft, S.P.; Writing--review and editing, Y.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (2021 RIA2C1011216).  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.