Human Factors Lessons Learned from a Surface Management System Simulation

Abstract

The Surface Management System (SMS), being developed at NASA Ames Research Center in conjunction with the FAA, is a decision support tool that helps air traffic controllers and air carriers manage aircraft movements on the surfaces of busy airports. By presenting information and advisories to the Air Traffic Control Tower, Terminal Radar Approach Control (TRACON), En-route Center, and air carriers, SMS creates shared departure situational awareness, thereby increasing the efficiency, capacity, and safety on the airport surface. This paper discusses the human factors lessons that were learned during the real-time simulation of SMS that was conducted in January, 2002 at the Future Flight Central facility at NASA Ames Research Center. Five active Dallas-Fort Worth (DFW) Tower controllers participated in the simulation. This paper discusses one of the main objectives of the threeday simulation; to conduct human factors studies to better understand the challenges involved with introducing automation tools into the Tower environment. To this end, studies observing controller coordination were conducted, usability, suitability, and acceptability questionnaires were administered, and informal debriefs were held after each of the nine runs. BACKGROUND Tower controllers are responsible for the safe, orderly, and expeditious flow of air traffic on the airport surfaces. Specifically, Tower controllers currently are responsible for taxiing aircraft, sequencing aircraft for departure, and clearing flights for takeoff and landing. In order to get information about the current state of the aircraft and airport resources, Tower controllers use several different information sources: flight strips, a map display known as the Airport Surface Detection Equipment (ASDE), and a repeater of the Terminal Radar Approach Control (TRACON) radar, known as a Digital Brite Radar Indicator (D-BRITE). Flight strips provide detailed flight information for each departure aircraft including the aircraft type, first departure fix, flight plan, and flight ID of the aircraft. *Aerospace Engineer. Human Factors Engineer. Aerospace Engineer, Member AIAA. The D-BRITE provides controllers with the flight identification (ID) of aircraft in the terminal airspace. The ASDE, which presently operates at many large airports, provides a map display of the airport surface that shows the locations of aircraft and other vehicles. The map display provides aircraft location information in an intuitive display that is similar to the controllers’ out-the-window view. ASDE does not identify the aircraft flight number or provide any other flight-specific information because it is a primary (i.e., skin paint) surface surveillance radar. New surface surveillance systems, such as ASDE-X and a prototype that is being developed under the FAA’s SafeFlight 21 program, will provide real-time information about the location and identity of aircraft. The ASDE map display, flight strips, and D-BRITE provide a good picture of the current state of the airport. However, data regarding future departure demand on airport resources is not currently available. NASA Ames Research Center, in cooperation with the Federal Aviation Administration (FAA) is developing a decision support tool known as the Surface Management System (SMS). The project is supported by NASA’s Advanced Air Transportation Technologies (AATT) Project. SMS uses information provided by the new surface surveillance systems and departure plans provided by the air carriers in order to provide the Tower, TRACON, Center and air carriers with better information about current and future demand, thereby creating shared awareness of the departure situation and improving the capacity, efficiency, and flexibility of the airport. SMS aids controllers with a variety of tasks including runway balancing and departure scenario optimization. Runway balancing is the task of ensuring that all active departure runways are equally busy in terms of imposed delay and usage. A departure scenario is defined as the mapping of departure fixes or gates to a departure runway. The purpose of these runway assignment rules is to ensure that the airborne trajectories of aircraft that § An aircraft’s departure fix is the first fix listed in its flight plan. At DFW the 16 departure fixes are grouped into four “gates,” one per side: North, South, East, and West. AIAA's Aircraft Technology, Integration, and Operations (ATIO) 2002 Technical 1-3 October 2002, Los Angeles, California AIAA 2002-5810 Copyright © 2002 by the American Institute of Aeronautics and Astronautics, Inc. No copyright is asserted in the United States under Title 17, U.S. Code. The U.S. Government has a royalty-free license to exercise all rights under the copyright claimed herein for Governmental purposes. All other rights are reserved by the copyright owner. 2 American Institute of Aeronautics and Astronautics takeoff from different runways do not cross. Departure scenario optimization is the task of ensuring that the current departure scenario provides the most efficient runway usage and leads to the least possible number of delays on the airport surface. Occasionally, it is possible to further enhance the efficiency of the airport surface by identifying aircraft that should be exceptions to the departure scenario. In this case, SMS provides runway advisories via map displays and timelines, advising the controller to taxi the aircraft to an alternate departure runway. SMS also provides additional advisories to help manage surface movements and departure operations. For example, SMS aids controllers with the task of sequencing departure aircraft by taking into account inter-departure gaps required by wake-vortex considerations and downstream departure flow constraints. SMS currently employs three types of user interfaces: map displays, timelines, and load graphs. Map displays of the airport surface provide a two-dimensional representation of the airport and include flight-specific information on data tags. Timelines provide flightspecific information and predictive time information, and load graphs provide aggregate data. Two simulations were conducted in order to solicit user feedback about the SMS concept, the preliminary user interfaces, and the algorithm performance. These realtime controller-in-the-loop simulations of SMS were conducted in the Future Flight Central (FFC) air traffic control Tower simulation facility at NASA Ames Research Center in September, 2001 and January, 2002. FFC is a 360-degree, high fidelity control Tower simulator designed to provide the look and feel of a Level V airport Tower cab. Developed as a joint effort between NASA and the FAA, FFC uses twelve large rear projection screens and computer-generated imagery to provide a 360-degree out-the-window view. Controllers use standard headsets to talk to the pseudopilots who control the individual aircraft movements. The initial simulation, held in September, 2001, consisted of three 45-minute runs. During each run, a different set of displays was presented to each Tower controller. Data were recorded during each run and the controllers completed questionnaires and participated in group debrief interviews after each run. Four active Dallas-Fort Worth (DFW) Tower controllers staffed the Local and Ground positions, while controllers from other airports observed and provided feedback. Observers were also present from Delta Air Lines and United Airlines. A large amount of expert user feedback was acquired through multiple discussions and informal debriefs with the controllers as well as through questionnaires and recordings of the simulation proceedings. The results of Simulation 1 indicated that map displays were well-liked by the Local and Ground controllers and that timelines had potential uses for them as well, but that both timelines and load graphs might be better suited for a Traffic Management Coordinator (TMC) or Supervisor position. The experimental design and results of Simulation 1 are described in detail in Reference 2. The feedback and human factors observations recorded during this simulation were incorporated into a refined version of SMS that was evaluated in a second simulation in January, 2002. The second simulation consisted of nine hour-long runs based on actual DFW traffic. Data were recorded during each run and the controllers completed questionnaires after specific runs. Additionally, group debrief interviews were held after each run. More than 30 participants were involved in the simulation, including five active DFW Tower controllers, a controller from the Memphis, TN airport (MEM) Tower, a supervisor from the Norfolk, VA Tower, and airline observers from FedEx, Northwest Airlines, UPS, American Airlines, and United Airlines. The five active DFW controllers staffed the Tower positions, while the other controllers observed and provided feedback. A large amount of expert user feedback was acquired through the informal debrief sessions as well as through questionnaires and data collected during the simulation. This paper summarizes the methodologies employed, the SMS displays tested, and the human factors lessons learned from the simulation. SMS will be further refined based on the feedback from the simulation and will be evaluated next in the FedEx ramp tower at MEM in the summer of 2002. An evaluation in the ATC Tower will follow in 2003. SIMULATION DESCRIPTION Simulation Environment In nominal operating conditions, DFW operates two air traffic control Towers, one controlling the west half of the airport, the other controlling the east half. Since the FFC facility can simulate only one Tower, only one side of the airport could be modeled. Therefore, since the majority of the gates and runways are located on the east half of the airport, a modified version of the East Tower of DFW was modeled. Operations were only conducted in South Flow under Visual Flight Rules (VFR) conditions. Figure 1 is a diagram of DFW airport. The box encloses the area that was modeled for

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Cite this paper

@inproceedings{Walton2002HumanFL, title={Human Factors Lessons Learned from a Surface Management System Simulation}, author={Deborah H. Walton and Cheryl Quinn and Stephen Atkins}, year={2002} }