## 3 Materials and Methods
### Data Collection and Extraction  
In the scheduling process of employee shuttle service for the Yalova Shipyard, monthly data for December 2023 were used because of the monthly travel planning procedure of shipyard management administration. To calculate the necessary vehicle numbers, total capacities, monthly costs, total passenger numbers, etc., monthly travel planning is conducted at the shipyard. For this reason, one of the months with highest data density was chosen and used in the study analysis. The dataset shows how many employees are picked up from each stop in the morning and how many are dropped off at each stop in the evening over one month. The data are collected by entering the information regarding how many employees get into the respective vehicle with the respective number plate into the system using the service boarding pass cards that employees use when they board the service. In a company with approximately two thousand employees, each service boarding card used by an employee during a round trip creates a separate data line, resulting in a very dense and complex digital dataset in the online server system of the operator. Some of the records in the dataset for a sample day are shown in Table 2.  
The raw data obtained digitally from the service boarding pass were cleaned and systematically prepared to ensure accuracy and robustness prior to the proposed model input. First, exploratory data analysis (EDA) was performed to identify data distributions, missing values and potential outliers. In examining the missing values of the data obtained, it was found that the missing values were limited. Therefore, the data were treated  
Figure 2: Current passenger service system in the Yalova Shipyard. by calculating the mean, median and imputation for numerical variables with limited missingness. Outliers were also carefully identified using z-scores and interquartile ranges (IQRs), supplemented by visual methods including scatter plots and box plots. Once identified, outliers were treated by limiting extreme values (Winsorization) to defined percentile limits. Observations clearly identified as erroneous were excluded from the analysis. Finally, data standardization and normalization ensured consistency across variables, and categorical data were appropriately coded to facilitate their use in the proposed model. These thorough  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data Registration** & **Observed Date** & **Observed Time** & **Licensed Plate No** & **Bus Stop** & **Bus** \\
**Number** & & **Time** & **Plate No** & **ID** & **Capacity** \\ \hline  
ast\_5 & 27 \\ �data cleaning procedures ensured reliable, high-quality inputs for the analysis, essential for effective DLP modeling. 1. Analysis Results  
Based on the data obtained, a dataset of 29,257 trips recorded over a month, for which the data were fully collected, was obtained (Figure 3). The dataset, which was transferred to the system via the card system and used in the analyses, included the stop name, date, time, vehicle number plate and vehicle capacity information. Using this dataset obtained after the data pre-processing phase, data were organized to analyze vehicle use, and data for a weekday sample are summarized in Table 3.  
There are currently eight buses with a capacity of \(17+1\) and twenty-six buses with a capacity of \(27+1\). The unit costs of vehicle types for the company are shown in Table 4. The shuttles depart from a designated stop at the shipyard and travel west and east. The shuttles stop at a total of eight stops, three in the west and five in the east, to pick up or drop off passengers (Figure 2).