Predicting Real-Time Safety of the National Airspace System

Abstract

Situation awareness is necessary for operators to make informed decisions regarding avoidance of airspace hazards. To this end, each operator must consolidate operationsrelevant information from disparate sources and apply extensive domain knowledge to correctly interpret the current state of the NAS as well as forecast its (combined) evolution over the duration of the NAS operation. This time-and workload-intensive process is periodically repeated throughout the operation so that changes can be managed in a timely manner. The imprecision, inaccuracy, inconsistency, and incompleteness of the incoming data further challenges the process. To facilitate informed decision making, this paper presents a model-based framework for the automated real-time monitoring and prediction of possible effects of airspace hazards on the safety of the National Airspace System (NAS). First, hazards to flight are identified and transformed into safety metrics, that is, quantities of interest that could be evaluated based on available data and are predictive of an unsafe event. The safety metrics and associated thresholds that specify when an event transitions from safe to unsafe are combined with models of airspace operations and aircraft dynamics. The framework can include any hazard to flight that can be modeled quantitatively. Models can be detailed and complex, or they can be considerably simplifed, as appropriate to the application. Real-time NAS safety monitoring and prediction begins with an estimate of the state of the NAS using the dynamic models. Given the state estimate and a probability distribution of future inputs to the NAS, we can then predict the evolution of the NAS-the future state-and the occurrence of hazards and unsafe events. The entire probability distribution of airspace safety metrics is computed, not just point estimates, without significant assumptions regarding the distribution type and/or parameters. We demonstrate our overall approach through a simulated scenario in which we predict the occurrence of some unsafe events and show how these predictions evolve in time as flight operations progress. Predictions accounting for common sources of uncertainty are included and it is shown how the predictions improve in time, become more confident, and change dynamically as new information is made available to the prediction algorithm.

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

“…a https://www.fly.faa.gov/ASDI/asdi.html b https://www.faa.gov/air traffic/technology/asde-x/ c https://www.faa.gov/air traffic/technology/eram/ d http://www.unidata.ucar.edu/software/thredds/current/tds/ For the purpose of this paper, all input data were retrieved or calculated using real archived flight data and logged in CSV-formatted files by the testbed. Input for the RTSM calculations at every second include the following: (1) aircraft type, (2) call sign, (3) departure or arrival flag, (4) latitude, (5) longitude, (6) altitude, (7) heading, (8) speed, (9) wind direction, (10) wind magnitude, (11) and wind gust speed. Also included are the latest flight plan information reported for a given flight, resolved to latitude and longitude coordinates and updated asynchronously.…”

“…In the remainder of the section we summarize the models, and refer the reader to our previous publications for additional details. 11,12 A. Modeling the NAS The first step is to develop a model describing the dynamics of the NAS, i.e., how the state x evolves in time:…”

“…In the past, 11 we used Monte Carlo sampling for prediction. A standard Monte Carlo approach generates many realizations or samples of the prediction inputs, and calls Algorithm 1 for each realization.…”

“…The Latin Hypercube sampling method improves coverage of uncertain variables and provides better accuracy than the Monte Carlo sampling method we had adopted in our earlier implementation of the RTSM framework. 11 In previous work, 11,12 we demonstrated our framework in simulation on three different constructed scenarios. In this paper, we demonstrate this framework on real flight data.…”

“…Towards this end, in previous work, we developed the Real-Time Safety Monitoring (RTSM) framework. 11,12 This proposed framework monitors and predicts the state of safety and determines whether any unsafe event will occur within a predetermined time frame of interest. The RTSM framework is model-based, and may include any hazard that can be modeled quantitatively.…”