Paper Contents
Abstract
Public transportation is an essential service for millions of people in urban areas, yet delays caused by traffic congestion, weather conditions, and operational inefficiencies often create significant challenges in daily life. This study focuses on developing a machine learning model to predict delays in public transport using logistic regression in Python. By analyzing real-world factors such as scheduled time, traffic density, weather conditions, and historical delay records, the system classifies whether a bus or train is likely to be on time or delayed. The proposed solution provides commuters with timely information, enabling better travel planning and reducing uncertainty. The research demonstrates how machine learning can be applied to everyday life problems, offering a scalable and cost-effective decision-support system for public transport authorities and commuters alike.
Copyright
Copyright © 2025 DHARSHINI G K. This is an open access article distributed under the Creative Commons Attribution License.
