Air traffic management in the New York (NY) metropolitan area presents significant challenges such as excess demand, chronic delays, and inefficient routes. At NASA, a new research effort has been initiated to explore Next Generation Air Transportation System (NextGen) Trajectory Based Operations (TBO) solutions to address lingering problems in the NY metroplex. One of the larger problems in NY is departure delays at LaGuardia Airport (LGA). Constant traffic demand and physical limitations in the number of taxiways and runways cause LGA to often end up with excessive departure queues that can persist throughout the day. At the Airspace Operations Laboratory (AOL) located at NASA Ames Research Center, a TBO solution for "Departure-Sensitive Arrival Spacing" (DSAS) was developed. DSAS allows for maximum departure throughput without adversely impacting the arrival traffic during the peak demand period. The concept uses Terminal Sequencing and Spacing (TSS) operations to manage the actual runway threshold times for arrivals. An interface enhancement to the traffic manager's timeline was also added, providing the ability to manually adjust inter-arrival spacing to build precise gaps for two or even three departures between arrivals. With this set of capabilities, inter-arrival spacing could be controlled for optimal departure throughput. The concept was prototyped in a human-in-the-loop (HITL) simulation environment to determine operational requirements such as coordination procedures, timing and magnitude of TSS schedule adjustments, and display features for the tower, Terminal Radar Approach Control (TRACON), and Traffic Management Unit (TMU). An HITL simulation was conducted in August, 2014, to evaluate the concept in terms of feasibility, impact on controller workload, and potential benefits. Three conditions were compared: (1) a baseline condition using new RNAV/RNP procedures (no TSS); (2) the new procedures + TSS; and (3) new procedures + TSS + DSAS schedule adjustments. Results showed that with a maximum arrival demand (40-41 arrivals per hour), departure throughput could be increased from 38 aircraft/hour (baseline condition), to 44 aircraft/hour (TSS condition), to 47 aircraft/hour (TSS + DSAS). The results suggest that DSAS operations have the potential to increase departure throughput at LGA by up to 9 aircraft/hour with little or no impact on arrivals during peak traffic demand period.
New performance standards for a detect and avoid (DAA) system are being developed to support the broader integration of unmanned aircraft systems (UAS) into the National Airspace System (NAS). One subset of these performance standards will address the minimum DAA display requirements to support pilot performance on maintaining well clear of other aircraft. These performance standards must take into account the current air traffic control (ATC) operational environment. In particular, the DAA system, and pilots’ interactions with that system, must account for the requirement for pilots to request a clearance for deviations from their approved instrument flight rules route. A series of human-in-the-loop (HITL) experiments were conducted to help identify the minimum information requirements for DAA displays. As part of these experiments, several pilot-ATC interaction metrics were collected, such as the amount of time it took pilots to request an ATC clearance prior to executing a maneuver to maintain well clear after the appearance of a DAA alert, and the proportion of time that pilots received an ATC clearance prior to maneuvering. The results indicate that while there was no observed effect of different display configurations on pilots’ interactions with ATC, these interactions were affected by the combination of alerting and operational procedures. When pilots received an unambiguous alert to potential well clear violations in conjunction with operational procedures that specified the expected actions to various alert levels, the time that it took for pilots to notify ATC of he need to maneuver dropped substantially, and the rate of obtaining a clearance increased. The implications of these results for developing performance standards for DAA are discussed.
The concept of free-flight, introduced in the 1990s, opened a debate on the efficiency of letting aircraft deal with conflicts without any centralized control. Many models have been proposed for autonomous aircraft solvers, but their efficiency is not well-known. In this paper we experiment with a powerful algorithm derived from robotics, which can deal with thousands of robots in very small spaces and show how its performance plummets when speeds are constrained. We also compare this autonomous algorithm with a centralized approach using evolutionary computation on a complex example to point out their relative performance in a constrained speed environment. This comparison provides scientific arguments for the need for centralized air traffic control.
This paper presents a framework to assess Air Traffic Management (ATM) performance in relation to flight efficiency. The main philosophy behind the approach presented in this paper is to quantify the quality of the service delivered by an Air Navigation Service Provider (ANSP) to an airline in terms of meeting commonly agreed-upon objectives. The definition is, therefore, aligned with the future paradigm of Trajectory Based Operations, where achieving the trajectory agreed between the ANSP and the airspace user becomes the focus. A staged approach to flight efficiency assessment is proposed to quantify the quality of the ANSP’s service in terms of both “facilitating what has been agreed” and “improving what can be agreed.” The framework promotes the development of more consistent efficiency performance metrics between ANSPs, by defining clear definitions for assessment references. Application of the framework is illustrated with several examples using the Airservices Dalí trajectory modeler.
NASA has developed an advanced arrival management capability for terminal controllers, known as Terminal Sequencing and Spacing (TSAS). TSAS increases use of performance-based navigation (PBN) arrival procedures during periods of high traffic demand. It enhances two Federal Aviation Administration (FAA) operational systems with terminal metering and controller spacing tools. Sixteen high-fidelity human-in-the-loop simulations, involving more than five hundred hours of evaluation time, were conducted to mature TSAS from proof-of-concept design to fully functional prototype. These simulations modeled arrival procedures at several US airports, incorporated a broad range of traffic demand profiles and wind conditions, and used controllers with extensive operational experience. Two metrics are evaluated for these simulations: PBN Success Rate and Inter-Arrival Spacing Error. The PBN Success Rate shows a definitive trend when TSAS is used. TSAS increases from 42% for today’s operations to 68% for terminal metering only and 92% for terminal metering with controller-managed spacing tools. Meanwhile, the Inter-Arrival Spacing Error improves 25% to 35% when TSAS is used compared to when it is not used. The TSAS technology was transferred to the FAA, and it is targeted for deployment to several busy airports in the US starting in 2018.
The Federal Aviation Administration’s (FAA’s) Traffic Alert and Collision Avoidance System (TCAS) Program Office is developing an advanced Airborne Collision Avoidance System (ACAS X) to meet the needs of both manned aircraft and Unmanned Aircraft Systems (UAS). ACAS Xu, the UAS variant, provides vertical Resolution Advisory guidance in most situations and horizontal RA guidance for cases where either surveillance quality or vehicle performance does not support vertical maneuvers. ACAS Xu is fully interoperable with TCAS II and also features new passive (ADS-B based) collision avoidance maneuver coordination techniques. To evaluate the effectiveness of ACAS Xu for collision avoidance as well as to inform interoperability requirements for integration with self-separation systems, the FAA, in conjunction with NASA, General Atomics Aeronautics Systems, Inc, and Honeywell Aerospace, conducted a proof-of-concept flight test in November 2014 with both manned and unmanned intruder aircraft. This paper describes the ACAS Xu system as well as the flight test and provides a summary of observed system performance.
As unmanned aircraft systems (UAS) become more important to the US military and other users, the pressure to allow them to fly in the national airspace increases. The greatest impediment to this is the lack of an alternative means of compliance with federal “see and avoid” regulations to provide the capability to avoid airborne conflicts between the UAS and manned aircraft. To provide this alternative means of compliance, the US Army is leading the development of a Ground-Based Sense and Avoid System (GBSAA). The system uses ground-based radars, threat detection and alerting logic, and decision support display aids to provide an air picture of the UAS’s operating environment and follows the DO-254 and DO-178C standards for safety critical avionics hardware and software, respectively. This system will allow greater airspace access and lower cost operations by replacing ground observers in the field with a centralized system, thus consolidating the observer function. The first GBSAA deployment site is expected to go live in 2016 at Fort Hood Army Air Field, Fort Hood, TX, operating under the FAA’s Certificate of Authorization process. This paper provides an overview of the system and of a human-in-the-loop simulation-based test exercise that is a key component of the certification of the system. During this test exercise, 19 self-separation violations, in which intruders came within 1 nmi horizontally and 100 ft vertically of the ownship, and no near-mid-air collisions (NMACs) occurred during 195 hours of simulation, including many stressing multi-intruder scenarios, during which reported workload was low and situation awareness was high throughout. All participants ultimately stated that the GBSAA system was appropriate for UAS operations within the National Airspace System (NAS).
A majority of aircraft are now using Global Navigation Satellite System (GNSS) for navigation. This has led to an effect of reducing the magnitude of lateral deviations from the route center line and, consequently, increasing the probability of a collision, should a loss of vertical separation between aircraft on the same route occur. The International Civil Aviation Organization (ICAO) has introduced Strategic Lateral Offset Procedures (SLOP) that allow suitably equipped aircrafts to fly with 1nmi or 2nmi lateral offset to the right of airway centerline in oceanic airspace. Very few aircraft, however, are using the SLOP procedure because of the lack of understanding of its safety benefits and implementation issues in identifying correct lateral offset that can reduce the collision risk. This paper proposes an Evolutionary Computation framework using Differential Evolution process to identify optimal lateral offsets for each airway in a given airspace such that it reduces the overall collision risk. Airway specific lateral offsets are then correlated with airway-traffic features using Multiple Regression models to identify which features can explain the optimal lateral offset. The proposed approach establishes a generic mapping that can suggest optimal lateral offsets for a given airspace based on airway-traffic features to mitigate collision risk. The proposed methodology is applied to Collision Risk assessment of one-day traffic data (710 flights) in Bahrain Upper Airspace (FL290-FL410) to estimate optimal lateral offset that resulted in significant reduction of collision risk. Further, the number of flights and crossings on an airway were identified as key features affecting optimal lateral offset.
In this paper the existing continuous descent operations (CDO) procedures at three relevant German airports are analyzed with respect to both the achievable (maximum specific range) and the effectively achieved fuel savings, when compared to conventionally flown arrivals. To do so, we applied our highly precise flight performance model Enhanced Jet Performance Model (EJPM) to several thousand flown trajectories before and after CDO implementation, the data of which were provided to us as radar track data. A technique was developed to estimate the individual aircraft gross mass for calculating the optimum rate of descent starting from the computed flight-specific top of descent (ToD). Furthermore, we considered 3D weather and wind data to determine the CDO trajectory. When locating the trajectories within typical ICAO CDO procedure corridors, we found that the current generic design criteria do not allow using the full fuel-saving potential of CDO. Often because of poor CDO execution from the ground and flight deck, only selected aircraft types managed to maintain the defined boundaries. To gain insight on how much detailed procedure guidance is required, a comprehensive weather and aircraft mass sensitivity analysis is also presented. We found analytic models to improve CDO procedures based on local traffic and meteorological conditions, which should supplement current guidance material.
The Dynamic Weather Routes (DWR) tool continuously and automatically analyzes active flights in en route airspace and finds simple route corrections to achieve more time- and fuel-efficient routes around convective weather. A strong partnership between the National Aeronautics and Space Administration (NASA), American Airlines (AA), and the Federal Aviation Administration (FAA) has enabled testing of DWR in real-world air traffic operations. NASA and AA have been conducting a trial of DWR at AA’s Integrated Operations Control Center in Fort Worth, Texas since July 2012. This paper describes test results based on AA’s use of DWR for their flights in and around Fort Worth Center (ZFW). Results indicate an actual savings of 3,949 flying minutes for 624 AA revenue flights from January 2013 through December 2014. Of these, 101 flights each indicate a savings of 15 min or more. Potential savings for all flights in ZFW airspace, corrected for savings flights achieve today through normal pilot requests and controller clearances without DWR, is about 100,000 flying minutes for 15,000 flights in 2013. Results indicate that AA flights with DWR in use realize about 15% more savings than non-AA flights. A weather forecast analysis examines the extent to which DWR routes rated acceptable by AA users remain clear of downstream weather. A sector congestion analysis indicates congestion could be reduced by about 20% if all flights fly DWR routes rather than nominal weather-avoidance routes.
A critical challenge for integrating Unmanned Aircraft Systems (UAS) is developing a means to sense and avoid (SAA) other aircraft. One of the main functions of SAA is to ensure the UAS remains well clear from other air traffic. Human pilots determine well clear subjectively, but SAA systems need a quantified alternative means of compliance. The UAS Science and Research Panel (SARP) brought together key researchers to define guiding principles for UAS Well Clear (UWC). The SARP aligned research efforts from NASA, MIT Lincoln Laboratory, and the US Air Force Research Laboratory to evaluate three UWC candidates in four simulation environments. These UWC candidates were evaluated against eight agreed operational suitability metrics, resulting in a recommended quantitative definition for UWC. This paper will describe the technical rationale for this recommendation, subsequent operational considerations, and potential to extend the work to small UAS.
With the increasing demand to integrate unmanned aircraft systems (UAS) into the National Airspace System (NAS), new procedures and technologies are necessary to ensure safe airspace operations and minimize the impact of UAS on current airspace users. Currently, small UAS face limitations on their use in civil airspace because they lack the ability to detect and avoid other aircraft. This article presents a framework that consists of an Automatic Dependent Surveillance-Broadcast (ADS-B)-based sensor, track estimator, conflict/collision detection, and resolution that mitigates collision risk. ADS-B offers long-range, omni-directional intruder detection with comparatively few size, weight, power, and cost demands. The proposed conflict/collision detection and planning algorithms for conflict/collision resolution are designed in the local level frame, which is the unrolled, unpitched body frame where the ownship is stationary at the center of the map. The path planning method is designed to be multi-resolutional at increasing distance from the ownship to account for both self-separation and collision avoidance thresholds. We demonstrate and validate this approach using simulated ADS-B measurements.
A fundamental requirement for the integration of unmanned aircraft into civil airspace is the capability of aircraft to remain well clear of each other and avoid collisions. This requirement has led to a broad recognition of the need for an unambiguous, formal definition of well clear. Any such definition must be interoperable with existing airborne collision avoidance systems (ACAS). A particular class of well-clear definitions uses logic checks of independent distance thresholds as well as independent time thresholds in the vertical and horizontal dimensions to determine if a well-clear violation is predicted to occur within a given time interval. Existing ACAS systems also use independent distance thresholds; however, a common time threshold is used for the vertical and horizontal logic checks. The main contribution of this paper is the characterization of the effects of the decoupled vertical time threshold on a well-clear definition in terms of (1) time to well-clear violation, and (2) interoperability with existing ACAS. This paper provides governing equations for both metrics and includes simulation results to illustrate the relationships. In this paper, interoperability implies that the time of well-clear violation is strictly less than the time a resolution advisory is issued by ACAS. The encounter geometries under consideration in this paper are initially well clear and consist of constant-velocity trajectories resulting in near-mid-air collisions.
In the en route environment, air traffic control is responsible for maintaining separation between aircraft with the assistance of many decision support tools (DSTs), such as Conflict Probe and Trajectory Predictor. Conflict Probe predicts potential airspace conflicts and notifies the controller of the impending separation violation, while Trajectory Predictor predicts more accurate aircraft position. In the Next Generation Air Transportation System (NextGen) environment, it is envisioned that Conflict Probe performance will be improved by adding more accurate aircraft and pilot intent data, better air traffic surveillance, and 4D trajectory-based operations. This paper investigates the effect and sensitivity of a proposed NextGen improvement on the Conflict Probe. A quantitative study is designed to examine integration of the text in the 4th line of the flight data block, which currently serves as an optional note field for air traffic controllers, into the en route DST to create new trajectories. The new, more accurate trajectories are compared against the original baseline trajectories to determine aircraft position accuracy improvements from the Trajectory Predictor, and false alert reductions from the Conflict Probe. The study findings quantitatively demonstrate that heading amendments increase trajectory accuracy by reducing horizontal error as much as 6.21nm, cross track error by 3.65nm, and along track error by 3.64nm on the first day. Speed amendments, noted in 4th line of the flight data block, decrease by 10.3% the overall number of false alerts generated by the Conflict Probe.
Integrating Unmanned Aircraft Systems (UAS) into the National Airspace System (NAS) requires that UAS meet or exceed the safety requirements established for conventional aircraft, and for the UAS pilots to interact with air traffic controllers (ATCos) in an acceptable manner. UAS have several characteristics that differentiate them from conventional aircraft, including the possibility of greater latencies associated with remote pilot communication and command execution. The goal of this study was to determine how adding delays to UAS pilot communications and command executions affect ATCos' interactions with UAS and conventional aircraft. Six previously certified radar controllers and two currently certified radar controllers were recruited as participants to manage traffic in a simulated sector with conventional traffic and one UAS flying in it. The UAS pilot verbal communication and execution latencies were varied in separate scenarios to include an additional delay that was either short (1.5 s) or long (5 s), and constant or variable within each scenario.We measured both UAS and conventional pilots' verbal communication and execution initiation latencies, and obtained ATCos' acceptability ratings for the different delay conditions. We also examined the number of communication step-ons created by the additional communication delays implemented in the UAS control station, as well as other measures of the ATCo-pilot interactions. Although we were unable to specify a precise upper limit of acceptable latencies, we found ATCos rated UAS pilot verbal communication latencies to be acceptable when the latencies were short rather than long, and that acceptability ratings often reflect broad features of the sectors being managed. Implications of these findings for UAS integration in the NAS are discussed.
Air traffic management (ATM) is facing a tremendous increase in the amount of available flight data. Parallel to the decreasing time and cost necessary to produce ATM data, computational requirements for storage and analysis of the bulk of data are steeply increasing. Compression is a key technology to deal with this challenge often referred to as big data science. In this paper, we propose a new technique for compressing 4D-trajectories of the Demand Data Repository provided by EUROCONTROL. While standard compression algorithms compress such a trajectory file as a large chunk of consecutive text, we propose to look at different streams in the trajectory files. We design encoding techniques for each stream separately. In our evaluation the whole traffic over Europe is compressed from more than 50 GB down to 1.42 GB, yielding a compression ratio of approx. 35:1, a reduction of more than 80 percent compared to the best standard compression technique. In addition, our new compression technique is almost 10 times faster than the best competitor. Therefore, we are convinced that efficient, yet fast, compression of 4D-trajectories is possible on modern hardware and should be exploited. Keywords-4D-trajectories, storage, compression
In a series of real time trials, we simulated sophisticated air traffic management conflict resolution automation using unrecognizable replays of controllers’ own performance. Using a fairly novel experimental design and a prototype air traffic control interface, we explored with operational controllers the interactive effects of traffic complexity, level of automation, and “strategic conformance” (defined as the match between human and machine solution strategy) on a number of dependent measures. Conformal advisories (exact replays of a given controller’s previous solution) were accepted more often, rated higher, and responded to faster than were non-conformal advisories (replays of a colleague’s different solution). In the end, one result stood out in particular: roughly 24% of conformal advisories were rejected by controllers. How could it be that controllers, in effect, disagreed with their very own solutions roughly one quarter of the time? The project is currently exploring this and other related issues through extended human-in-the-loop simulations.
Analyses are presented that provide a first quantitative look at the impacts of lightning-related ramp closures on departing and arriving air traffic. Ramp closures are a necessity to ensure the safety of outdoor personnel servicing gate-side aircraft. Halting outdoor work delays air traffic and may cause ripple effects beyond the impacted airport. Today's ramp closure decision-making process is burdened with uncertainty related to the procedures and their implementation, and the lightning data used to trigger ramp closures. This uncertainty needs to be accounted for, as it has implications for ensuring personnel safety and minimizing avoidable operational inefficiencies. Our initial results highlight the fact that lightning ramp closures can exert substantial impacts on air traffic. Moreover, the choice of safety procedure and varied sources of lightning information yield large uncertainty that renders operators unsure as to whether or not their approach is safe and effective. Additional research will help refine the assessment of lightning ramp closure impacts and provide a perspective with other convective storm impacts. A better diagnosis of lightning threats and the prediction of such threats into the near future will enable more consistent and proactive decisions.
