Our proposed innovation is a Go-Around Prediction (GAP) service that encapsulates predictive analytics so that stakeholders of NASA’s In-time System wide Safety Assurance (ISSA) strategic thrust can readily use it to assess the go-around probability in real time during aircraft arrival operations in the National Airspace System (NAS). Our proposed innovation is directly relevant to subtopic A3.03 Future Aviation System Safety and fills two critical gaps in the state-of-the-art. First, it allows for the continuous monitoring of the arrival domain of the NAS and fuses diverse data sets including airborne trajectory, surface tracking, air traffic automation, and weather data to identify the precursors to a key indicator of risk in the system (i.e., a go-around). Second, it applies innovative machine learning (ML) techniques to build and train a model using historic go-around occurrences in order to predict go-around safety margins in real time. A key outcome in the first decade of ISSA-related research is improved safety through initial real-time detection and alerting of hazards at the domain level and decision support for limited operations. Our proposed innovation directly addresses this outcome by focusing on the near-airport (within 10 miles) domain to identify risks to stakeholders (e.g., air traffic controllers and pilots) in enough time (before a go-around is necessary) for them to employ effective risk mitigations. Through the combination of a real-time data input stream and a ML based predictive model, the software service allows for the continuous computation of the probability of a go-around. Results can be updated and displayed to operators (i.e., air traffic controllers and pilots) as each arrival flight approaches the airport. This additional information will allow operators increased situational awareness during the approach phase of flight leading to earlier mitigation of developing risks and, if needed, more time to safely manage go-arounds.
(1) GAP advances NASA SWS research by accelerating risk detection to real-time.
(2) GAP integrates with the In-Time Aviation Safety Management System (IASMS) to assess operational safety and identify emerging risks potentially introduced by new DSTs during initial deployment.
(3) Integration with NASA’s Digital Information Platform (DIP) provides valuable information on go-arounds to aviation stakeholders.
(4) The predictive analytics service serves as a model for other NASA analytics development.
(1) ANSP personnel use the GAP capability to identify risks in airport operations much sooner than currently possible, thereby increasing the safety margin.
(2) Airlines and airports use GAP to provide insight into go-around causes with the intent of reducing their risky and disruptive nature at major airports.
(3) Automated Safety Management System (SMS) reporting for ANSPs, airlines, and airports.