Go-Around Prediction Service,Phase II

Our proposed innovation is a Go-Around Prediction (GAP) service that encapsulates predictive analytics so that stakeholders of NASArsquo;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 innovation is directly relevant to Subtopic A3.03 Future Aviation System Safety and fills two critical gaps in the state-of-the-art.nbsp; First, it allows for the continuous monitoring of the NAS arrival domain and fuses diverse data sets including airborne trajectory, surface tracking, 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 models using historic go-around occurrences 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.nbsp; Our 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 go-around probability. Through integration with NASA platforms such as the In-time Aviation Safety Management System (IASMS), results can be updated and displayed to operators (i.e., air traffic controllers and pilots) for each arrival flight. This information will provide operators increased situational awareness during the approach phase of flight leading to earlier mitigation of developing risks and more time to safely manage go-arounds.nbsp;