Initial Demonstration of the Real-Time Safety Monitoring Framework for the National Airspace System Using Flight Data

Abstract

As new operational paradigms and additional aircraft are being introduced into the National Airspace System (NAS), maintaining safety in such a rapidly growing environment becomes more challenging. It is therefore desirable to have an automated framework to provide an overview of the current safety of the airspace at different levels of granularity, as well an understanding of how the state of the safety will evolve into the future given the anticipated flight plans, weather forecast, predicted health of assets in the airspace, and so on. Towards this end, as part of our earlier work, we formulated the Real-Time Safety Monitoring (RTSM) framework for monitoring and predicting the state of safety and to predict unsafe events. In our previous work, the RTSM framework was demonstrated in simulation on three different constructed scenarios. In this paper, we further develop the framework and demonstrate it on real flight data from multiple data sources. Specifically, the flight data is obtained through the Shadow Mode Assessment using Realistic Technologies for the National Airspace System (SMART-NAS) Testbed that serves as a central point of collection, integration, and access of information from these different data sources. By testing and evaluating using real-world scenarios, we may accelerate the acceptance of the RTSM framework towards deployment. In this paper we demonstrate the framework's capability to not only estimate the state of safety in the NAS, but predict the time and location of unsafe events such as a loss of separation between two aircraft, or an aircraft encountering convective weather. The experimental results highlight the capability of the approach, and the kind of information that can be provided to operators to improve their situational awareness in the context of safety.

Identification of Safety Metrics for Airport Surface Operations

“…The monitoring facet involves computing the probability distribution of the safety metrics, p(φ(k)), at a current time k from NAS state estimates obtained using nonlinear estimation techniques such as unscented Kalman filters or particle filters. 15 The prediction problem focuses on using uncertainty propagation techniques such as Monte Carlo sampling to compute the probability distribution of future NAS states, safety metrics p(Φ k H k ), and safety-related events occurring within a prediction horizon k H > k. 15,17 For detailed information on the development, modeling, monitoring, and prediction aspects of the RTSM framework, References [11,[15][16][17] should be consulted.…”

Real-Time Prediction of Safety Margins in the National Airspace

“…In previous papers, we documented the selection of safety metrics, 10 the RTSM framework applied to predicting safety metrics, 11 and an initial implementation connected to real-time surveillance and weather data. 12 In this paper, we describe how the framework can be applied to determining safety margins, that is, the difference between the current situation and the minimum acceptable situation. The "situation" can be an individual safety metric or an aggregate of multiple safety metrics.…”