SMART AVIATION: TOWARDS SECURE AND COLLABORATIVE FLIGHT DELAY PREDICTION USING MACHINE LEARNING

Abstract

Airlines serve as essential enablers of mobility across borders and regions, with passenger satisfaction remaining a cornerstone of their operational and strategic focus. Leading carriers such as Qatar Airways, Air Arabia, and Etihad Airways continue to set benchmarks in service excellence, cleanliness, and technological innovation. Flight punctuality is one of the important indicators of passenger satisfaction, among many others. Flight cancellations, as well as delays in departures or arrivals due to weather situations, congestion, technicalities, or ineffective logistics, do not only interfere with the schedules of passengers but also damage the reputation and cost-effectiveness of the airlines. When so much data in aviation keeps circulating, conventional manual analysis does not provide prompt analysis and decision making. This paper contributes to the concept of Smart Aviation since it provides a secure, collaborative machine learning-based platform to predict flight delay. The research aims to use supervised learning in a publicly accessible dataset on Kaggle to detect delay patterns, the classes of root causes, and conflicts before they even take place. Data-driven predictive models improve the situational awareness and make planning of proactive interventions to reduce the negative effects of delays. In addition, such an approach can promote the kind of collaboration that involves sharing data and making decisions by the major stakeholders in the aviation industry, airlines, airport authorities, and regulatory services. The proposed system uses the power of machine learning to be part of the larger mission of intelligent, responsive, and passenger-centric air travel that eventually is turning the management of flight delays into a more secure, efficient, and anticipatory program.