Prioritising bus station operational factors using machine learning–based principal component analysis

Bus transport is the main mode of public transit in Indian cities, necessitating efficient bus station layouts. As service complexity increases, improved traffic management and enhanced pedestrian facilities become essential. This study conducted questionnaire survey at major bus stations across India to identify the key operational factors. The research began with a site investigation to identify the general issues, followed by the development of a questionnaire based on expert recommendations to gather the stakeholder feedback. The collected responses were converted into a normalised matrix. Initially, a chi-squared test was performed to validate the field data, followed by principal component analysis for dimensionality reduction using machine-learning algorithms. The factors were then ranked based on their average normalised scores. The findings offer valuable insights for prioritizing key factors in the planning and design of new bus station layouts. These insights enable transit planners to effectively address critical considerations during the design process. Ultimately, the research contributes to the development of bus stations that facilitate smooth and unobstructed traffic flow.