Aviation safety analysis increasingly benefits from large-scale operational trajectory data, yet raw motion traces alone are insufficient for understanding safety-critical events on the airport surface. The significance of an aircraft’s motion depends on the structured operational environment in which it occurs, including runways, taxiways, hold-short lines, and interactions among multiple agents over time.

This thesis presents a broader framework for AI-enabled aviation safety and develops several components to support it. Amelia-42 dataset provides a large-scale operational data foundation for studying airport surface movement. Trajectory alignment with airport surface graphs recovers snapped routes, route-transition predictions, candidate conflict points, and time-to-node estimates. To support interpretable safety reasoning, World2Rules introduces a neural-symbolic pipeline that learns human-interpretable runway-incursion rules from incident reports and nominal operational observations. Critical Scenario Identification mines real runway-incursion reports and evaluates whether models can identify the critical agents and timestamps in safety-relevant interactions. Together, these components demonstrate how trajectory data can be transformed into structured representations, interpretable safety rules, and evaluation methodologies that support the identification, explanation, and analysis of safety-critical aviation scenarios.

Committee:
Dr. Sebastian Scherer (Chair)
Dr. Jean Oh
Junwon Seo