Machine Learning Approach for Labeling Undetected Planned Trips in Public Transport Operators

Accurate labeling of undetected trips in public transportation is critical, as it directly affects operational efficiency, cost savings, and service quality. Undetected trips refer to scheduled trips that were either not completed or inaccurately recorded by Automatic Vehicle Location (AVL) systems. These discrepancies can disrupt resource allocation, hinder operational planning, and compromise financial accountability. If undetected trips are not properly classified, they can cause significant financial losses, misallocation of resources, and lower customer satisfaction due to unaddressed service issues. This paper presents a machine learning approach to automate the classification of undetected trips in public transit. The model categorizes trips into three types: Operated (successfully completed trips), Lost-Deductible (missed trips within operational limits), and Lost - Non-deductible (missed trips outside operational standards and noncompensable). Automating this process enhances operational efficiency, reduces financial losses, and streamlines claim management. By replacing manual classification with AI-driven automation, transit operators can ensure faster, more accurate trip labeling, ultimately leading to optimized resource use, better decision-making, and higher service standards.