Digital Airport Safety Twin

Predictive safety risk tools for airports utilize data analytics, Machine Learning (ML), and Artificial Intelligence (AI) to forecast potential safety hazards and risks in airport operations before they escalate into incidents or accidents. These tools analyze patterns, trends, and anomalies in historical data to train ML models, forming the basis for real-time safety and risk monitoring systems. Parallel to predictive tools, digital twin technology has evolved, initially focusing on facility maintenance but increasingly applying to airport and airspace operations using real-time data, enabling simulation, efficiency optimization, safety enhancement, and decision-making support. These models can identify operational bottlenecks, improve safety protocols, and provide predictive analytics for informed decision-making. The proposed "Digital Airport Safety Twin" platform offers a comprehensive approach to managing airport safety risk by merging predictive safety tools and digital twin technology into a homogenous safety and risk management system that enables continuous monitoring of safety and risk patterns across airfields through historical operational data, human factors, weather data, and safety-related information. It employs ML algorithms to assess and quantify safety risks associated with various airport operations, helping prioritize safety improvements. By analyzing a wide range of data sources, including airport surface movement, flight operations quality, and weather data, the platform can predict safety incident likelihoods and support safety enhancements. AI and Large Language Models (LLMs) help translate complex human factors and interactions into measurable safety and risk metrics. The innovation can be used in-house at NASA to support aviation safety platforms and research (i.e. the DIP, ATM-X) but can also be integrated into commercial ATM tools and platforms as a safety service to support risk planning, Trajectory-Based Operations (TBO), and others.