## 3 Methods
### Truck Transport Time Data Analysis and Transport Route Diagnostic Method  
In this work, we propose a method to analyze and diagnose transport routes by examining the truck transport time for each section through a log data processing program. The analysis proceeds in three main stages: First, a log data processing program is used to calculate the statistics of the truck transport time for each section. Next, spatial analysis is conducted using the average truck transport time and standard deviation by section, and a section is selected for analysis. As for the average transport time for each section, the shorter the distance between the Bluetooth beacon installation points, the shorter the average transport time. Conversely, the longer the distance, the longer the average transport time. Therefore, a section in which the standard deviation is large means that there is a large variation in the truck transport time. Conversely, in a section where the standard deviation is small, the variation in the truck transport time is small, appearing close to the average. For this reason, when comparing the truck transport time and standard deviation for each section, there is a limitation in that the application cannot consider the differing distances for each section. Therefore, in this study, the percentage of the standard deviation for the average transport time of the truck for each section was calculated as shown in Equation (1), and the section to be analyzed was selected using this value.  
\[\frac{\textit{Standard deviation}\ (SD)\ \textit{of truck transport time}}{\textit{Average of truck transport time}}\times 100 \tag{1}\]  
A section in which the calculated value is large means that the standard deviation is larger than the average transport time--that is, the variation in the truck transport time is large--and the section in which the value appears small means that the variation in the truck transport time is relatively small. In this study, two sections in which the transport  
Figure 5: Conceptual diagram of the operation of the log data analysis program developed in this study.  
time of the truck appeared to be stable (the section with a small value) and two sections in which it appeared to be unstable (the section with a large value) were selected.  
For the four selected sections, a time-series analysis was performed by dividing the log file by week; thus, the types of transport routes were classified. The type of transport route could be determined using a time-series analysis graph, in which the types of transport routes were classified into four types based on the degree of data distribution and fluctuation (Figure 6). Types 1 and 2 were sections in which the distribution of truck transport time data was large, and they were generally seen in sections where the distance between beacon installation points was long or there were loading areas or dumping areas around the transport route. In Type 1 sections, the truck transport time was expected to appear stable without a specific event during the transport operation, whereas in Type 2, the truck transport time was expected to appear unstable owing to a large distribution of time data and various events (vehicle breakdown, tunnel closure, congestion, etc.) that may have occurred during the haulage operation. Types 3 and 4 included sections in which the distribution of truck transport time data was small, generally seen where there were no loading and dumping areas on the transport route, and only ore transport operations were performed. In Type 3, the section was expected to appear stable with little fluctuation in the truck transport time. As for Type 4, the truck transport time was generally stable, but events that caused the truck transport time to rapidly increase occurred intermittently. Finally, we compared and reviewed the production logs at the site and interviewed field staff to determine why truck travel times on transport routes appeared to be stable or unstable.