The practical possibilities of a Deep Turnaround based alarm system
The Deep Turnaround (DT) system introduces the use of multiple cameras at aircraft VOPs to capture visual data of turnaround operations. This data is processed by a machine learning algorithm to interpret and predict ground handling events using historical datasets. While DT is primarily intended to improve turnaround predictability and efficiency, the visual data it generates can also support additional operational applications. In this project, improved situational awareness during pushback operations could enhance both safety and coordination between Schiphol, LVNL, and the airlines.
This thesis investigates the practical possibilities of using Deep Turnaround data to develop an alarm system for Air Traffic Control/Departure Manager (DMAN) that detects and reports pushbacks initiated without clearance. The research focuses on whether such an application is technically and operationally feasible as a future operational concept, and what implications it would have for implementation in the airport environment. The study explores how DT data can be translated into reliable alerts, assesses potential benefits and limitations for ATC and other stakeholders, and outlines a conceptual system framework. The intended outcome is an evaluation of feasibility and a structured design of the DT-based alert system.