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Benchmarking the processing of aircraft tracks with triples mode and self-scheduling

Published in:
2021 IEEE High Performance Extreme Computing Conf., HPEC, 20-24 September 2021.

Summary

As unmanned aircraft systems (UASs) continue to integrate into the U.S. National Airspace System (NAS), there is a need to quantify the risk of airborne collisions between unmanned and manned aircraft to support regulation and standards development. Developing and certifying collision avoidance systems often rely on the extensive use of Monte Carlo collision risk analysis simulations using probabilistic models of aircraft flight. To train these models, high performance computing resources are required. We've prototyped a high performance computing workflow designed and deployed on the Lincoln Laboratory Supercomputing Center to process billions of observations of aircraft. However, the prototype has various computational and storage bottlenecks that limited rapid or more comprehensive analyses and models. In response, we've developed a novel workflow to take advantage of various job launch and task distribution technologies to improve performance. The workflow was benchmarked using two datasets of observations of aircraft, including a new dataset focused on the environment around aerodromes. Optimizing how the workflow was parallelized drastically reduced the execution time from weeks to days.
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Summary

As unmanned aircraft systems (UASs) continue to integrate into the U.S. National Airspace System (NAS), there is a need to quantify the risk of airborne collisions between unmanned and manned aircraft to support regulation and standards development. Developing and certifying collision avoidance systems often rely on the extensive use of...

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Applicability and surrogacy of uncorrelated airspace encounter models at low altitudes

Published in:
J. Air Transport., Vol. 29, No. 3, July-September 2021, pp. 137-41.

Summary

National Airspace System (NAS) is a complex and evolving system that enables safe and efficient aviation. Advanced air mobility concepts and new airspace entrants, such as unmanned aircraft, must integrate into the NAS without degrading overall safety or efficiency. For instance, regulations, standards, and systems are required to mitigate the risk of a midair collision between aircraft. Monte Carlo simulations have been a foundational capability for decades to develop, assess, and certify aircraft conflict avoidance systems. These are often validated through human-in-the-loop experiments and flight testing. For example, an update to the Traffic Collision Avoidance System (TCAS) mandated for manned aircraft was validated in part using this approach [1]. For many aviation safety studies, manned aircraft behavior is represented using the MIT Lincoln Laboratory statistical encounter models [2–5]. The original models [2–4] were developed from 2008 to 2013 to support safety simulations for altitudes above 500 feet above ground level (AGL). However, these models were not sufficient to assess the safety of smaller unmanned aerial systems (UAS) operations below 500 feet AGL and fully support the ASTM F38 and RTCA SC-147 standards efforts. In response, newer models [5–7] with altitude floors below 500 feet AGL have been in development since 2018. Many of the models assume that aircraft behavior is uncorrelated and not dependent on air traffic services or nearby aircraft. The models were trained using observations of cooperative aircraft equipped with transponders, but data sources and assumptions vary. The newer models are organized by aircraft types of fixed-wing multi-engine, fixed-wing single engine, and rotorcraft, whereas the original models do not consider aircraft type. Our research objective was to compare the various uncorrelated models of conventional aircraft and identify how the models differ. Particularly if models of rotorcraft were sufficiently different from models of fixed-wing aircraft to require type-specific models. The scope of this work was limited to altitudes below 5000 feet AGL, the expected altitude ceiling for many new airspace entrants. The scope was also informed by the Federal Aviation Administration (FAA) UAS Integration Office and Alliance for System Safety of UAS through Research Excellence (ASSURE). The primary contribution is guidance on which uncorrelated models to leverage when evaluating the performance of a collision avoidance system designed for low-altitude operations, such as prescribed by the ASTM F3442 detect and avoid standard for smaller UAS [8]. We also address which models can be surrogates for non-cooperative aircraft without transponders. All models and software used are publicly available under open source licenses [9].
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Summary

National Airspace System (NAS) is a complex and evolving system that enables safe and efficient aviation. Advanced air mobility concepts and new airspace entrants, such as unmanned aircraft, must integrate into the NAS without degrading overall safety or efficiency. For instance, regulations, standards, and systems are required to mitigate the...

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Processing of crowdsourced observations of aircraft in a high performance computing environment

Published in:
2020 IEEE High Performance Extreme Computing Conf., HPEC, 22-24 September 2020.
Topic:

Summary

As unmanned aircraft systems (UASs) continue to integrate into the U.S. National Airspace System (NAS), there is a need to quantify the risk of airborne collisions between unmanned and manned aircraft to support regulation and standards development. Both regulators and standards developing organizations have made extensive use of Monte Carlo collision risk analysis simulations using probabilistic models of aircraft flight. We've previously determined that the observations of manned aircraft by the OpenSky Network, a community network of ground-based sensors, are appropriate to develop models of the low altitude environment. This works overviews the high performance computing workflow designed and deployed on the Lincoln Laboratory Supercomputing Center to process 3.9 billion observations of aircraft. We then trained the aircraft models using more than 250,000 flight hours at 5,000 feet above ground level or below. A key feature of the workflow is that all the aircraft observations and supporting datasets are available as open source technologies or been released to the public domain.
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Summary

As unmanned aircraft systems (UASs) continue to integrate into the U.S. National Airspace System (NAS), there is a need to quantify the risk of airborne collisions between unmanned and manned aircraft to support regulation and standards development. Both regulators and standards developing organizations have made extensive use of Monte Carlo...

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Initial assessment of wind forecasts for Airport Acceptance Rate (AAR) and Ground Delay Program (GDP) planning

Published in:
MIT Lincoln Laboratory Report ATC-414

Summary

The planning and execution of the Airport Acceptance Rate (AAR) for major metroplex airports is a complex and critical function of traffic managers in the National Airspace System (NAS). Despite the importance of AAR planning, traffic managers currently have no widely available decision support to provide guidance for runway selection and the determination of a sustainable AAR. The AAR Decision Support Capability (AARDSC), currently under development as part of the Collaborative Air Traffic Management Technology Work Package 4 (CATMT WP4), will provide such guidance. This report provides an initial analysis of the impacts of surface winds and winds aloft on the key factors associated with the AAR (the selection of runway configuration and aircraft ground speed and spacing on final approach) and the capabilities of currently available weather forecasts to accurately predict those impacts. The report was limited in scope by the schedule and available resources, and is intended as a foundation for a comprehensive forecast assessment in follow-on work. Surface wind forecasts from the Terminal Aerodome Forecast (TAF) and numerical prediction models (the High Resolution Rapid Refresh [HRRR], Rapid Refresh [RAP] and Rapid Update Cycle [RUC], collectively described as "MODEL") were compared to observed winds gathered from METAR reports as Newark International Airport (EWR). TAF and METAR were compared for 639 days of operations from 2011-2013. MODEL forecasts and METAR were compared for 21 days of operation, 16 of which had Traffic Management Initiatives (TMI) in place to mitigate adverse weather impacts. Winds aloft were translated into several wind impact metrics. The impacts of winds aloft forecast errors were evaluated by comparing impact metrics calculated from MODEL forecasts with those calculated from analysis fields for the 21 case days. Forecasts were evaluated at horizons of 2, 4, 6, and 8 hours.
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Summary

The planning and execution of the Airport Acceptance Rate (AAR) for major metroplex airports is a complex and critical function of traffic managers in the National Airspace System (NAS). Despite the importance of AAR planning, traffic managers currently have no widely available decision support to provide guidance for runway selection...

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Sector workload model for benefits analysis and convective weather capacity prediction

Published in:
10th USA/Europe Air Traffic Management Research and Development Sem., ATM 2013, 10-13 June 2013.

Summary

En route sector capacity is determined mainly by controller workload. The operational capacity model used by the Federal Aviation Administration (FAA) provides traffic alert thresholds based entirely on hand-off workload. Its estimates are accurate for most sectors. However, it tends to over-estimate capacity in both small and large sectors because it does not account for conflicts and recurring tasks. Because of those omissions it cannot be used for accurate benefits analysis of workload-reduction initiatives, nor can it be extended to estimate capacity when hazardous weather increases the intensity of all workload types. We have previously reported on an improved model that accounts for all workload types and can be extended to handle hazardous weather. In this paper we present the results of a recent regression of that model using an extensive database of peak traffic counts for all United States en route sectors. The resulting fit quality confirms the workload basis of en route capacity. Because the model has excess degrees of freedom, the regression process returns multiple parameter combinations with nearly identical sector capacities. We analyze the impact of this ambiguity when using the model to quantify the benefits of workload reduction proposals. We also describe recent modifications to the weather-impacted version of the model to provide a more stable normalized capacity measure. We conclude with an illustration of its potential application to operational sector capacity forecasts in hazardous weather.
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Summary

En route sector capacity is determined mainly by controller workload. The operational capacity model used by the Federal Aviation Administration (FAA) provides traffic alert thresholds based entirely on hand-off workload. Its estimates are accurate for most sectors. However, it tends to over-estimate capacity in both small and large sectors because...

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Data-driven evaluation of a flight re-route air traffic management decision-support tool

Published in:
Applied Human Factors and Ergonomics Conf., 21 July 2012.

Summary

Air traffic delays in the U.S. are problematic and often attributable to convective (thunderstorms) weather. Air traffic management is complex, dynamic, and influenced by many factors such as projected high volume of departures and uncertain forecast convective weather at airports and in the airspace. To support the complexities of making a re-route decision, which is one solution to mitigate airspace congestion, a display integrating convective weather information with departure demand predictions was prototyped jointly by MIT Lincoln Laboratory and the MITRE Corporation. The tool was deployed to twelve air traffic facilities involved in handling New York area flights for operational evaluation during the summer of 2011. Field observations, data mining and analyses were conducted under both fair and convective weather conditions. The system performance metrics chosen to evaluate the tool's effectiveness in supporting re-route decisions include predicted wheels-off error, predicted wheels-off forecast spread, and hourly departure fix demand forecast spread. The wheels-off prediction errors were near zero for half the flights across all days, but the highest 10% errors exceeded 30 minutes on convective weather days. The wheels-off forecast spread exceeded 30 minutes for 25% of forecasts on convective weather days. The hourly departure demand forecast spread was 9 flights or less for 50% of departures across all days except one. Six out of the seven days having the highest hourly departure demand forecast spreads occurred in the presence of long-lived weather impacts.
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Summary

Air traffic delays in the U.S. are problematic and often attributable to convective (thunderstorms) weather. Air traffic management is complex, dynamic, and influenced by many factors such as projected high volume of departures and uncertain forecast convective weather at airports and in the airspace. To support the complexities of making...

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Evaluation of the Integrated Departure Route Planning (IDRP) Tool 2011 prototype

Published in:
MIT Lincoln Laboratory Report ATC-388

Summary

The Integrated Departure Route Planning (IDRP) tool combines convective weather impact forecasts from the Route Availability Planning Tool (RAPT) with departure demand forecasts from the MITRE tfmCore system to aid traffic managers in formulating plans to mitigate volume congestion in fair weather and during convective weather impacts. An initial prototype was deployed in the summer of 2010 for a very limited field evaluation. A second, more comprehensive field evaluation of the "Phase 2" IDRP prototype was performed in the summer of 2011. The key focus of IDRP is the planning and implementation of departure reroutes to avoid weather impacts and volume congestion on departure fixes and routes. This evaluation assesses three facets of the IDRP prototype critical to the successful realization of its concept of operations: 1. performance of weather impact forecasts from RAPT and departure demand forecasts from tfmCore, 2. effectiveness of reroute decisions, and 3. potential impacts on procedures and decision making based on observations of IDRP use in the field. The evaluation concludes with suggestions for future enhancements to improve the performance and realization of potential benefits in operational use of IDRP.
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Summary

The Integrated Departure Route Planning (IDRP) tool combines convective weather impact forecasts from the Route Availability Planning Tool (RAPT) with departure demand forecasts from the MITRE tfmCore system to aid traffic managers in formulating plans to mitigate volume congestion in fair weather and during convective weather impacts. An initial prototype...

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Estimation of New York departure fix capacities in fair and convective weather

Published in:
3rd Aviation, Range, and Aerospace Meteorology, 23 January 2012.

Summary

When convective weather impacts the New York Metro airspace, traffic managers may employ several tactics to mitigate weather impacts and maintain manageable and efficient flow of traffic to and from the airports. These tactics, which include maneuvering individual flights through weather, merging and redirecting traffic flows to avoid storms, and rerouting traffic from blocked routes onto unimpacted or less-impacted routes, all affect the capacity of the affected airspace resources (departure fixes, routes, or gates). Furthermore, the location of the weather impacts can have a great influence on the amount of leeway that traffic managers have in applying these tactics. In New York, departure fixes, the gateways to en route airspace where departure traffic from several metroplex airports are merged onto en route airways, are particularly critical. When congestion (volume of traffic in excess of capacity) occurs near departure fixes as a result of weather impacts, traffic managers must resort to airborne holding or unplanned departure stops to quickly reduce traffic over the fix to manageable levels. Nonetheless, when convective weather impacts densely packed and busy metroplex airspaces, it is inevitable that traffic will need to use impacted departure fixes and routes to keep delays in check. For this reason, predictions of the weather-impacted capacity of critical airspace resources like departure fixes that are based in the reality of commonly used impact mitigation tactics, are needed to help traffic managers anticipate and avoid disruptive congestion at weather-impacted departure fixes. The Route Availability Planning Tool (RAPT) is a departure management decision support tool that has been used in the New York operations since 2003. It predicts the weather impact on departure fixes and routes based on departure times. RAPT assigns a departure status (RED, YELLOW, or GREEN) to individual departure routes based on the departure time, the predicted severity of the convective weather that will impact the route, the likelihood that a pilot will deviate to avoid the weather along the route, and the operational sensitivity to deviations in the departure airspace that the route traverses. These blockages assist traffic managers in prompt route reopening of routes closed by convective weather impacts, as well as providing situational awareness for impeding impacts on routes. RAPT also identifies the location of weather impacts along the departure route. This paper presents an analysis of observed fair weather and convective weather impacted throughput on New York departure fixes. RAPT departure status and impact location are used to characterize the severity of departure fix weather impacts, and weather-impacted fix capacity ranges are estimated as a function of RAPT impacts. The use of traffic flow merging is identified, and weather impacted capacity ranges for commonly used merged flows are also estimated.
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Summary

When convective weather impacts the New York Metro airspace, traffic managers may employ several tactics to mitigate weather impacts and maintain manageable and efficient flow of traffic to and from the airports. These tactics, which include maneuvering individual flights through weather, merging and redirecting traffic flows to avoid storms, and...

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Estimating the likelihood of success in departure management strategies during convective weather

Published in:
Proc. 30th IEEE/AIAA Digital Avionics Systems Conference, DASC, 16-20 October 2011, pp. 6D4.

Summary

The presence of convective weather (thunderstorms) in terminal and nearby en route airspace of major metroplex areas can have significant impacts on departure operations. Traffic on departure routes impacted by convective weather may be constrained by miles-in-trail (MIT) restrictions, to allow controllers the time needed to maneuver individual flights around thunderstorms that pilots wish to avoid. When the workload required to manage traffic flows becomes too great, departure routes may be closed. Departures still on the ground that are filed on closed or restricted routes may face significant delays as they wait for clearance on their filed route, or for a viable reroute to be implemented. The solution proposed in concepts such as the Integrated Departure Route Planning tool (IDRP) [1] is the use of weather and departure demand forecasts to plan and implement reroutes to avoid weather and volume congestion proactively, well in advance of route restrictions or closures.
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Summary

The presence of convective weather (thunderstorms) in terminal and nearby en route airspace of major metroplex areas can have significant impacts on departure operations. Traffic on departure routes impacted by convective weather may be constrained by miles-in-trail (MIT) restrictions, to allow controllers the time needed to maneuver individual flights around...

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Analytical workload model for estimating en route sector capacity in convective weather

Published in:
9th USA/Europe Air Traffic Management Research and Development Sem., ATM 2011, 14-17 June 2011.

Summary

We have extended an analytical workload model for estimating en route sector capacity to include the impact of convective weather. We use historical weather avoidance data to characterize weather blockage, which affects the sector workload in three ways: (1) Increase in the conflict resolution task rate via reduction in available airspace, (2) increase in the recurring task load through the rerouting of aircraft around weather, and (3) increase in the inter-sector coordination rate via reduction in the mean sector transit time. Application of the extended model to observed and forecast data shows promise for future use in network flow models.
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Summary

We have extended an analytical workload model for estimating en route sector capacity to include the impact of convective weather. We use historical weather avoidance data to characterize weather blockage, which affects the sector workload in three ways: (1) Increase in the conflict resolution task rate via reduction in available...

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