## 3 Materials and Methods
### Data Collection and Extraction
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Time of Day** & \multicolumn{3}{c}{**West Direction**} & \multicolumn{3}{c}{**East Direction**} \\
**Plate No** & **Bus Capacity** & **Occupancy/(\%)** & **Plate No** & **Bus Capacity** & **Occupancy/(\%)** \\ \hline \multirow{9}{*}{Morning} & 0034 & 27 & 14/(52) & 4025 & 17 & 14/(52) \\  & 0002 & 27 & 17/(63) & 4054 & 17 & 7/(41) \\  & 0020 & 27 & 28/(104) & 4  
Within the shuttle service, the number of employees to be picked up or dropped off at stops for home-work-home trips may vary for reasons such as shift changes, additional labor requirements due to work intensity, employees working overtime and employees being directed to other units when needed. For this reason, the analysis of the data shows that the number of services and trips required in the transport network varies from day to day. As this change cannot be foreseen in the current active shuttle system, the provision of all shuttles owned by the company leads to inefficient capacity management. According to the information obtained from the available data, the change in the number of passengers at the stops for a sample period of one week is shown in Figure 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vehicle Type** & \begin{tabular}{c} **Fuel Consumption** \\ **(per km)** \\ \end{tabular} & \
\begin{tabular}{c} **Monthly Maintenance and** \\ **Repair Expenses (TL)** \\ \end{tabular} &
\begin{tabular}{c} **All Other Annual** \\ **Expenses (TL)** \\ \end{tabular} \\ \hline
17 + 1 & \begin{tabular}{c} USD 0.13 \\ USD 0.16 \\ \end{tabular} & \
\begin{tabular}{c} USD 71.5 \\ USD 92.7 \\ \end{tabular} &
\begin{tabular}{c} USD 668.3 \\ USD 899.4 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Costs per vehicle used in the shuttle system. Figure 4: Daily change in passenger numbers at the stops over a week. Figure 3: Content and size of the dataset. Analysis of the data shows that the capacity of the shuttles used to transport employees to and from work is not being used effectively [63; 64]. The random distribution of employees from the shipyard stop to the shuttles results in some shuttles being full and passengers having to stand, while other shuttles run with a number of passengers below capacity. For this reason, in the designed transport network, the services that are put into service without numerical service planning are reflected as additional costs. When analyzing the data obtained from the existing system, the occupancy rates of the shuttles, calculated by taking into account the total capacity of the shuttles used for morning and evening transport during the week, are as shown in Figure 5.  
An analysis of Figure 5 shows that almost half of the service capacity is not used on Sundays, when the number of employees using the shuttle service is lower than on other days, and there is a gap of around 30-40% on other days. It is assumed that by optimizing the number of shuttle services, the capacity can be used optimally, and the company can provide the shuttle service at minimum cost. The study proposes a model that minimizes the number of trips and suggests a cost-effective service.