Airlines Flight Baggage Handling using Predictive Analytics

Abstract

This paper presents a detailed study of predictive analytics for airline baggage handling systems. It explains how computer vision, machine learning, forecasting models, and routing algorithms improve baggage flow, reduce errors, and lower operational delays. The study also shows how predictive maintenance reduces downtime and improves system reliability. It includes AI versus non‑AI comparison tables, workflow breakdowns, and an integrated architecture. Findings show that predictive analytics improves accuracy, speed, and system capacity while reducing manual work. These results support future large‑scale adoption across airports. [1][2]