Terminal Radar Approach Control Research Articles

An Air Traffic Control Wind Compression Forecasting Tool for the TRACON Environment

Strong wind in the lower levels of the atmosphere near major airports can impact normal air traffic control (ATC) operations with respect to the horizontal spacing of aircraft. This is known in the ATC community as wind compression. Wind compression occurs when a strong wind from a specific direction and speed (at critical altitudes) impact normal ATC operations. The impact of wind compression becomes most noticeable when the spacing between aircraft decreases near or below minimal acceptable limits. Previous attempts to forecast wind compression have been unsuccessful. Wind shear calculations, time, height wind forecasts, and maximum winds below a certain altitude, such as 3050 m (10 000 ft), do not adequately convey the impact of wind compression. These methods are insufficient because they do not account for the flight profile of the numerous arrival routes that aircraft must travel to land at major airports. The Terminal Radar Approach Control (TRACON) Wind Compression Tool assists aviation forecasters in determining which arrival routes are impacted and the specific layers of the approach that are susceptible to wind compression. This program (1) diagnoses five different layers on an arrival route that may have potential wind compression impacts and (2) forecasts the onset and end of a compression event, the altitudes impacted, and the relative strength of the wind compression. Key information using the wind forecast from three National Weather Service models is condensed for each model hour and is placed into a timeline forecast.

Predicting separation errors of air traffic controllers through integrated sequence analysis of multimodal behaviour indicators

Predicting separation errors in the daily tasks of air traffic controllers (ATCOs) is essential for the timely implementation of mitigation strategies before performance declines and the prevention of loss of separation and aircraft collisions. However, three challenges impede accurate separation errors forecasting: 1) compounding relationships between many human factors and control processes require sufficient operation process data to capture how separation errors occur and propagate within controller-in-the-loop processes; 2) previous human factor measurement approaches are disruptive to controllers’ daily operations because they use invasive sensors, such as electroencephalography (EEG) and electrocardiography (ECG), 3) errors accumulated in using the tasks and human behaviors for estimating system dynamics challenge accurate separation error predictions with sufficient leading time for proactive control actions. This study proposed a separation error prediction framework with a long leading time (>50 s) to address the above challenges, including 1) a multi-factorial model that characterizes the inter-relationships between task complexity, behavioral activity, cognitive load, and operational performance; 2) a multimodal data analytics approach to non-intrusively extract the task features (i.e., traffic density) from high-fidelity simulation systems and visual behavioral features (i.e., head pose, eyelid movements, and facial expressions) from ATCOs’ facial videos; 3) an encoder-decoder Long Short-Term Memory (LSTM) network to predict long-time-ahead separation errors by integrating multimodal features for reducing accumulated errors. A user study with six experienced ATCOs tested the proposed framework using the Phoenix Terminal Radar Approach Control (TRACON) simulator. The authors evaluated the model performance through two types of metrics: 1) point-level metrics, including precision, recall, and F1-score, and 2) sequence-level metrics, including alignment accuracy and sequence similarity. The results showed that 1) the model using the task and visual behavioral features significantly improved the prediction performance compared to the model using one single feature (eyelid movements), with an improvement of up to 26.95% in alignment accuracy for 10s-ahead prediction; 2) the model that combined task and visual behavioral features had a higher or comparable performance to models with different hybrid features, achieving an alignment accuracy of 82.38% for 50s-ahead error prediction; and (3) the proposed method outperformed three baseline models – Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and classic LSTM – by 8.21%, 3.47%, and 3.14% in alignment accuracy, respectively, for predicting 50s-ahead separation errors. These results suggest that the proposed model can effectively predict separation errors in air traffic control.

Tactical Separation and Safety-Alerting System for Terminal Airspace

The provision of tactical alerts to aid air traffic controllers in providing separation assurance in terminal airspace is hindered by the complexity of the airspace, its operations, and flight procedures. A prototype automation system is studied that provides controllers with both separation and safety alerts based on or derived from the separation standard for terminal airspace. The system models flight trajectories heuristically with the use of merged intent information from readily available sources: area navigation departure procedures, flight-plan routes, and arrival nominal interior routes used in terminal automation systems. Flight vertical intent is modeled according to standard procedural restrictions, except when superseded by controller-issued altitude clearances. Importantly, flight trajectories are modeled for all aircraft, including those conducting visual approaches. New safety-alert thresholds for aircraft conducting visual approaches are studied. Performance of the system is evaluated through fast-time playback of recorded air traffic data from high-fidelity human-in-the-loop simulations and real-world operations in two terminal radar approach control facilities. The prototype system is found to produce a false-alert rate of 8% for separation alerts. The number and validity of safety alerts are studied by comparing with the current Conflict Alert system, showing that the false alerts of Conflict Alert are at 85% and they are avoided in the prototype system.

Classification of air traffic control scenarios using decision trees: insights from a field study in terminal approach radar environment

Air traffic controllers are responsible for the safe, expeditious and orderly flow of the air traffic. Their training relies heavily on the use of simulators that can represent various normal and emergency situations. Accurate classification of air traffic scenarios can provide assistance towards a better understanding of how controllers respond to the complexity of a traffic scenario. To this end, we conducted a field study using qualified air traffic controllers, who participated in simulator sessions of terminal radar approach control in a variety of scenarios. The aim of the study was twofold, firstly to explore how decision trees and classification rules can be used for realistic classification of air traffic scenarios and secondly to explore which factors reflect better operational complexity. We applied machine learning methods to the data and developed decision trees and classification rules for these scenarios. Results indicated that decision trees and classification rules are useful tools in accurately categorizing scenarios and that complexity requires a larger set of predictors beyond simple aircraft counts. The derived decision trees and classification rules performed well in prediction, stability and interpretability. Practical benefits can be derived in the areas of operations and system design in the context of air traffic flow and capacity management systems.

Human-in-the-Loop Simulation Experiment of Integrated Arrival/Departure Control Services For NextGen Operational Improvement

The National Airspace System suffers from a reduction in airport throughput or even closure when weather or traffic volume constrains arrival or departure gates to and from the Terminal Radar Approach Control (TRACON) facilities. To alleviate this, the Federal Aviation Administration introduced the Integrated Arrival and Departure Control Services (IADCS) concept that would extend terminal separations and procedures to the adjacent Air Route Traffic Control Center (ARTCC) sectors for more flexible traffic. It proposes the use of resectorization, bidirectional gates, bidirectional routes, and Air Traffic Control assigned routes. We evaluated them in the human-in-the-loop high-fidelity experiment and collected objective and subjective data. Our results clearly showed that all IADCS procedures except the vertically separated/bidirectional gate procedure were more effective than the Baseline condition that controllers currently use in the field. We conjecture the vertically separated/bidirectional gate procedure requires more complex perceptual and cognitive processes than the other procedures.

Reducing Departure Delays at LaGuardia Airport with Departure-Sensitive Arrival Spacing (DSAS) Operations

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.

Projecting the Effects of Air Carrier Fleet Plans on Future National Airspace System Operations

The Federal Aviation Administration’s Office of Performance and Operations Analysis developed a five-year operational outlook for the National Airspace System (NAS) to help better anticipate air traffic demand for planning and staffing purposes. Until now, most aviation forecasts have been based on long-term economic trends. In contrast, the approach presented in this paper, the Five Year Rolling Projection (FYRP), relies on public statements made by the major US carriers regarding their fleet and network plans. Airline fleet plans are relatively fixed for the near future because carriers are usually obligated to take delivery of aircraft orders. Therefore, short-term future operations growth is constrained by these fleet plans regardless of economic uptrends. Using 2013 publicly available information, the FYRP reports operations across the NAS will decrease by five percent and passenger capacity will increase by eleven percent by 2018. Traffic Control Center and Terminal Radar Approach Control facility traffic are each projected to decrease by four percent.

Adapting a Human – Automation Trust Scale to an Air Traffic Management Environment

Adapting a Human – Automation Trust Scale to an Air Traffic Management Environment

An Improved MPS Algorithm for Optimal Scheduling of Aircraft Arrivals at Congested Airports

This paper presents an improved aircraft arrival sequencing and scheduling model, and designs a new efficient algorithm to optimize the arrival traffic flow at the Terminal Radar Approach Control (TRACON) area. It has three steps: Firstly the aircrafts in same route are united into groups, then the positionmatrix is created according to a series of ATC (Air Traffic Control) constraints, finally the solution space tree is built to search the optimal sequence. The algorithm also take into account Max Position Shift (MPS) and wake vortex separations. Finally the algorithm is validated with practical data, the results shows the computing scale is small and the proportion of continuous landing aircrafts in final sequence is more in contrast to other algorithms, it is more convenient for the operation of controllers.

Framework for High-Density-Area Departure and Arrival Traffic Management

An operational concept is proposed to improve high-density-area departure and arrival traffic management that specifically accounts for complications arising in metroplex operations where multiple airports are located in close proximity. In the proposed concept, the roles and responsibilities are redistributed among the traffic management coordinators in different facilities, which include the air route traffic control center, terminal radar approach control, and the airport traffic control tower. The redistribution of roles and responsibilities facilitates improved decision-making capabilities, thereby increasing safe, efficient, and stable operation of departure and arrival traffic in the Next Generation Air Transportation System. This paper proposes a set of functions and capabilities needed to support the roles and responsibilities defined in the proposed concept. The decision support system framework defines three levels of decision making and incorporates an optimization methodology to assist decision makers at the different phases of the decision process. A detailed description of the decomposition and corresponding decision support system structure is presented, and a description of the optimization models is provided. An analysis is performed on a realistic traffic example to demonstrate the optimization model and illustrate the concept.

Developing Early Alerting Mechanism to Support Robust Terminal Radar Approach Control Operations

This paper describes the development of an automated mechanism to alert aviation traffic managers of the need to take action to adjust the rate of aircraft arriving into airports. When rates are too high, air traffic controllers are forced to do costly maneuvering and to hold aircraft to maintain required spacing. When arrival rates are too low, valuable airport landing capacity goes unused. In today's operations, mismatches between the planned and actual arrival rates often occur gradually and may not even be noticed until too late, after significant problems have materialized. This paper proposes an alerting mechanism that uses realtime signal metrics based on actual airspace operations to alert controllers of impending problems. Alerts would be triggered when signal metrics crossed their respective threshold values, which would be tailored for specific airspaces and generated with sufficient lead time to allow for mitigating actions. The alerting mechanism would reduce reliance on manual monitoring and thus reduce traffic manager workload. With historical flight data from airspace surrounding Atlanta International Airport an initial predictive model was developed and validated for one possible signal metric. Through discussions with subject matter experts, an analysis of various metric threshold values was also performed.

Tactical Conflict Detection in Terminal Airspace

Air traffic systems have long relied on automated short-term conflict prediction algorithms to warn controllers of impending conflicts (losses of separation). The complexity of terminal airspace has proven difficult for such systems as it often leads to excessive false alerts. Thus, the legacy system, called Conflict Alert, which provides short-term alerts in both en-route and terminal airspace currently, is often inhibited or degraded in areas where frequent false alerts occur, even though the alerts are provided only when an aircraft is in dangerous proximity of other aircraft. This research investigates how a minimal level of flight intent information may be used to improve short-term conflict detection in terminal airspace such that it can be used by the controller to maintain legal aircraft separation. The flight intent information includes a site-specific nominal arrival route and inferred altitude clearances in addition to the flight plan that includes the RNAV (Area Navigation) departure route. A new tactical conflict detection algorithm is proposed, which uses a single analytic trajectory, determined by the flight intent and the current state information of the aircraft, and includes a complex set of current, dynamic separation standards for terminal airspace to define losses of separation. The new algorithm is compared with an algorithm that imitates a known en-route algorithm and another that imitates Conflict Alert by analysis of false-alert rate and alert lead time with recent real-world data of arrival and departure operations and a large set of operational error cases from Dallas/Fort Worth TRACON (Terminal Radar Approach Control). The new algorithm yielded a false-alert rate of two per hour and an average alert lead time of 38 seconds.

Evaluations of Data Communications in Tower, TRACON, and En Route Air Traffic Control

The Federal Aviation Administration (FAA) has embarked on a large effort to create the Next Generation Air Transportation System (NextGen). NextGen will prepare the National Airspace System to accept increasing air traffic levels and complexity in a safe and efficient manner. Data Communications (Data Comm) is one of the core technologies of NextGen. The use of Data Comm will improve information exchange between pilots and air traffic controllers. In 2009, the FAA conducted three human-in-the-loop experiments to address the use of Data Comm in the Airport Traffic Control Tower, the Terminal Radar Approach Control, and the En Route airspace. The experiments focused on human factors aspects of air traffic control tasks under a variety of conditions including varying percentages of aircraft equipped for Data Comm, different interface designs, and changes in roles of responsibilities of air traffic controllers.

Developing Target Symbicons for the Future Terminal Air Traffic Control Environment

Current air traffic control target symbols are holdover designs developed for obsolete display technology to depict radar surveillance uncertainty. Given new technological capabilities and new surveillance techniques, it is time to reexamine the target symbology for a new generation of air traffic control displays. Symbicons (Smallman, St. John, Oonk, and Cowen, 2001a, 2001b) are a combination of icons and symbols that code meaningful information using simplified visual coding. This assessment focuses on creating and evaluating novel designs for target symbicons for Terminal Radar Approach Control displays while incorporating human factors standards and best practices. In this study, we evaluated different candidate symbicons that conveyed information such as aircraft category and aircraft conformance. We examined the heuristics controllers use that make certain symbicons better candidates to represent different aircraft categories. We discuss the almost universal agreement among the controllers for the tested aircraft conformance symbicons.

The Design of a Distributed Scheduling System for Multi-Center Time-Based Metering of Air Traffic into Congested Resources

A distributed scheduling system has been developed and implemented within NASA’s Multi-center Traffic Management Advisor (McTMA) proto-type research system. This paper describes the design and operation of the distributed scheduling system, its testing, and potential future use. The distributed scheduler is a loosely-coupled network of schedulers, each operating over short distances to deconflict aircraft merging into local points of congestion, while informing all other networked schedulers of its constraints. This architecture coordinates aircraft arrivals into congested resources by allocating delay over long distances while being robust to uncertainties in long-range estimates of arrival times. The system reduced airborne delay by 20–42% (without a comparable increase in ground delay), reduced airborne holding by 90%, reduced vectoring, and resulted in a shorter final approach in field tests at four FAA Air Route Traffic Control Centers, the Philadelphia Terminal Radar Approach Control facility, and the FAA’s Air Traffic Control System Command Center. The FAA is currently incorporating technology from the distributed scheduler into its nationwide deployment of the Traffic Management Advisor system.

Federal aviation administration and ada

The US Federal Aviation Administration (FAA) depends upon large, complex and highly available software systems to manage the vast commercial and civil aviation network and to carry out the agency's mission of ensuring high capacity, efficient and extremely safe air travel for the flying public. The FAA's Tower domain provides air traffic control services in the airport environment. There are more than 500 FAA managed Towers. The Terminal Radar Approach Control (TRACON) domain provides air traffic control services in the terminal environment where aircraft are climbing or descending. There are 197 TRACONs. The En Route domain provides air traffic control services typically above 10,000 feet between departure and arrival domains where aircraft achieve their optimal cruise altitude. There are only 21 En Route centers, so each must provide coverage to very large areas. Ada has become a strategic technology in developing and sustaining systems that require high availability and high reliability. The FAA Air Traffic Organization's (ATO) major recent initiative, the En-Route Automation Modernization (ERAM) program goes a long way to reintegrating many disparate system components into a modern composite architecture. This program leverages and significantly expands upon previous Ada based systems, so the FAA has a vital interest in Ada language technology now and for the foreseeable future. Mr. O'Leary will present a federal customer's perspective on systems development and quality, with emphasis on higher reliability, security, and safety. Ada is exceptionally well positioned to support these characteristics, and that is why many current large FAA ATC systems are largely or significantly Ada based. He will touch on past Ada based systems that FAA fielded, the current major ERAM effort, and also where FAA is going in the future with the Next Generation Air Transportation System (NextGen). He will give some insights on what the language and development technologies can contribute towards facilitating the vision of our future. FAA accepts use of DO-278 as a means of assuring systems integrity for Communications, Navigation, Surveillance, and Air Traffic Management (CNS/ATM) software. Ada has also performed well in DO-278.

Potential Impacts of Very Light Jets in the National Airspace System

Very light jets constitute a class of three to eight passenger turbofan-powered aircraft that will enter service in 2006 and will need to be integrated into the National Airspace System. An aircraft performance analysis showed similarities between the predicted performance and capabilities of very light jets and the performance of existing light jets. Based on this, an analysis of operating patterns of existing light jets was used to predict how very light jets will be operated. Using 396 days of traffic data from the FAA enhanced traffic management system, the operating patterns of existing light jets were analyzed. It was found that 64% of all the flights flown by lightjets had their origin, destination, or both within the top 23 regional airport systems in the continental United States. This concentration of light jet traffic was found in areas of the air transportation system that are currently exhibiting dense traffic and capacity constraints. The structure of the network of routes flown by existing light jets was also studied and a model of network growth was developed. It is anticipated that this concentration will persist with emerging very light jet traffic. This concentration of traffic at key areas in the system will have implications for air traffic control management and airport activity. For regional airport systems, core airports are expected to saturate and, reliever airports will become critical for accommodating traffic demand. The entry of very light jets will increase the traffic load at the terminal airspace: terminal radar approach control. These impacts need to be taken into account to allow a successful integration of these aircraft in the National Airspace System.

Using Language Technology to Increase Efficiency and Safety in ATC Communication

Voice communication is a volatile part ofAir Traffic Control (ATC) and thus the InternationalCivilAviationOrganization(ICAO)putsgreatemphasisonimprovingcommunication in ATC. Research has shown that miscommunication happens on average once every hour per radio frequency where there is frequent communication, such as in Terminal Radar Approach Control (TRACON). This paper proposes that a Language Technology System (LTS) can make communication between controller and pilot more reliable and efficient, thus improving safety in aviation. An LTS can for example detect read back errors. It can also directly feed data from the voice recognizer into a Flight Data Processing System or interact with it otherwise as we show. By interviewing air traffic controllers and studying the literature, we identified these and several other examples of use of language technology in ATC. In this paper, we explore one of the identified examples, a system that takes over the communication between controller and pilot in oceanicATC. This system is not meant to control the airspace autonomously, but only relay information. Latest advances in language technology have enabled the development of such a system. The functionality of the proposed LTS is described using scenarios and sequence diagrams.A demonstration LTS using Hex Technology was implemented. A usability test was administered to seven controllers. Their attitude towards the agent was positive and indicates that there is reason for further research. The performance and error logs of the LTS were analyzed and give guidance on further development of a fully functioning LTS for ATC.

Evaluating Controller Use of Advanced Weather Products by Evaluating User Interaction Patterns

We evaluated how Terminal Radar Approach Control (TRACON) controllers interacted with advanced weather products when displayed on primary tactical radar displays or on auxiliary flat-panel displays. When we provide the controllers with access to appropriate weather tools, we potentially decrease workload and improve performance by increasing weather situational awareness (WSA). We assessed the benefit of providing weather information using Gantt charts to graphically depict user interaction patterns and by conducting an analysis of the frequency and duration of interactions with the weather tools. From these analyses, we identified two patterns of interacting with tools corresponding to heavy and light users. We correlated tool use with our measure of controller efficiency and found an increase in traffic throughput with increasing tool use. We also determined that the controllers were more likely to interact tactically with tools when placed on the Terminal Controller Workstation (TCW), turning them on when needed and turning them off when not needed. This method provides us with insight into how the controllers use the information on their displays to help provide them with tools to increase safety and efficiency.

Air Traffic Control Weather Radar Displays: Validation of a Masking Metric for Prediction of Text Block Identification

Background: The Federal Aviation Administration is evaluating the potential benefits of weather information overlaid on the Terminal Radar Approach Control (TRACON) radar display. The study's objective was to validate a background masking metric to assist display designers to identify good color combinations for an air traffic control weather radar display. Methods: A uniform gray pattern and two weather radar displays were used as the background for randomly selected aircraft data text blocks (varying luminance levels) positioned in eight fixed locations around a central location. Observers' task was to search for the data text block that matched the text block presented in the central location. Three text contrast levels were used for the uniform background and two levels were used for each of the weather radar backgrounds. Results: For each of the four weather radar conditions, we estimated the uniform background contrast that would give the same performance for percent correct and latency. Conclusions: A simple luminance contrast masking metric is a fairly good predictor of the masking of the text blocks by the colored weather map backgrounds.