Selection of the Most Relevant Indicators to Improve Data Monitoring in a State-Owned Passenger Transportation Using PCA

The identification of the most relevant operational performance indicators in the transport sector can be complex and challenging for managers due to the complexity of data and numerous possible indicators. This study aims to address this research gap by utilizing principal component analysis (PCA) to tackle this challenge and identify the essential drivers for performance in passenger transport. By applying PCA, the study’s original dataset was refined, narrowing in on the most impactful operational performance indicators, related to cost, safety, and maintenance. These results offer insights for managers to make data-driven decisions while monitoring these critical indicators over time, improving performance, and optimizing service delivery. This study concludes that the PCA process serves as an effective tool for identifying relevant performance indicators in passenger transport. Moreover, it offers valuable decision-making instruments for the industry.