William J. Hughes

Analysis of the Aircraft to Aircraft Conflict Properties in the National Airspace System

*† ‡ The primary function of administering the United States’ National Airspace System (NAS) is the air traffic controller task of actively monitoring assigned aircraft and resolving the conflicts (i.e. losses of minimum separations between aircraft) anticipated some time in the future. To mitigate the safety risks of increased traffic growth and effectively designing automation to aid in the separation management task, knowledge of the characteristics or properties of the conflicts is required. This paper reports on a comprehensive study that has examined these properties by collecting traffic data from all 20 NAS en route centers, developing software models to determine these events, implementing experimental design techniques to calibrate them, validating the models by comparing to advanced operational systems, and presenting detailed graphical and statistical analysis of the results. I. Introduction In the United States, the overall system of managing and controlling air traffic is known as the National Airspace System (NAS), which is administered by the Federal Aviation Administration (FAA). Detailed procedures involving restrictions on routing, speeds, and altitudes are an integral part of the NAS. These restrictions severely reduce the amount of aircraft traffic that NAS can accommodate, yet are needed to ensure the high level of safety required. At the heart of these operations is the human air traffic controller who must synthesize many pieces of timely information including radar surveillance information and flight data. Their fundamental responsibility is to ensure the safety of the aircraft flying within their regions of airspace in the most efficient means possible. To accomplish this, air traffic controllers actively monitor their aircraft and then resolve any conflicts (i.e., loss of minimum separation between aircraft or restricted airspace) predicted some time into the future. Furthermore, these resolutions are administered by air traffic controller voice instructions via radio transmissions to the aircraft. In the current system, there are automation systems that aid the air traffic controller mainly in the monitoring part of the task such as the ground based tactical and strategic conflict probes. In the en route centers, typically managing the aircraft above 18,000 feet, the Host Computer System’s (HCS) Conflict Alert function provides tactical alerts. The upgrade to the HCS, still under development, called the En Route Automation Modernization (ERAM), replaces Conflict Alert with several categories of alerts with the basic function requiring a minimum of 75 seconds warning. The User Request Evaluation Tool (URET), developed by MITRE Corporation’s Center for Advanced Aviation System Development, is an example of a strategic conflict probe in operation in the en route centers. It predicts conflicts up to 20 minutes in the future with typically a minimum warning of five minutes. Even with the aid of ground-based conflict probes, the task of separating aircraft will become increasingly difficult, since most air traffic service providers in the United States and Europe anticipate significant growth in air traffic. The growth is expected to out pace the capacity limits of the aviation systems, resulting in greater congestion and inefficiency. The interagency Joint Development Planning Office (JPDO) in the United States foresees a traffic demand increase by 2025 up to three times the number of flights of today’s traffic. 1 Given the need for enhanced safety and efficiency, broad categories of advances in ground and airborne automation are required. The JDPO, as established in their charter under the “Vision-100” legislation (Public Law 108-176) signed by President G. W. Bush in December 2003, has mandated a next generation operational concept of the NAS for 2025. 1 This next generation

Determination of Lateral Flight Adherence in Recorded Air Traffic Data

When using recorded air traffic data to measure the accuracy of air traffic management decision support tools that use intent-based trajectory modelers, it is necessary to determine whether or not an aircraft is adhering to its known flight plan clearance. This paper defines what is meant by adherence and presents metrics that can be used to define lateral flight adherence. The paper describes an algorithm that is currently being applied that uses a subset of these metrics. The paper then presents a number of examples obtained from recorded air traffic data, which show instances where aircraft deviate from their known lateral clearance. The paper then presents a number of alternative approaches that could be used to implement a better algorithm for determining whether or not an aircraft was in lateral adherence based on recorded air traffic data.

TIME SHIFTING AIR TRAFFIC DATA FOR QUANTITATIVE EVALUATION OF A CONFLICT PROBE

A conflict probe is an air traffic management decision support tool that predicts aircraft-to-aircraft and aircraft-to-airspace conflicts. In order to achieve the confidence of the air traffic controllers who are provided this tool, a conflict probe must accurately predict these conflicts. This paper discusses how a conflict probes quantitative accuracy requirements can be tested using hypothesis testing techniques. The paper also asserts that air traffic scenarios based on recorded field data are essential to the evaluation of a conflict probe and states that time shifting these scenarios can create data samples necessary to perform the hypothesis testing. This paper then compares three time shifting techniques: time compression, random time adjustment, and an implementation of a genetic algorithm.

HEURISTIC METHODS TO POST PROCESS AIRCRAFT RADAR TRACK DATA

The Conflict Probe Assessment Team of the Federal Aviation Administration (FAA) has evaluated several decision support tools within the National Airspace System. The evaluation required knowledge of the actual flight paths flown by the aircraft in the test scenarios used. The aircraft flight paths are determined from radar surveillance data as processed by the FAA’s Host mainframe computers. The tracking data obtained from the FAA’s Host computers contains errors and noise and must be corrected before the evaluation software can use it. This paper presents a set of carefully crafted heuristic data processing methods that have been developed and used to clean up the radar tracking data as it is supplied by the Host computers. The methods have been applied to numerous air traffic scenarios containing thousands of aircraft flights. The methods are described and representative statistical results are presented. For a typical air traffic five-hour scenario it was necessary to discard 2% of the aircraft radar tracks, 2.4% of the track position reports, and to correct 1.3% of the reports. The post processing of the track reports enabled the downstream software tools to satisfactorily process the radar data and establish ground truth for the testing and evaluation of the air traffic control systems.

A Study on the Feasibility of Using a Genetic Algorithm to Generate Realistic Air Traffic Scenarios Based on Recorded Field Data

Air traffic scenarios based on recorded live data are used for the development and analysis of decision support tools used by air traffic controllers. Frequently these scenarios need to be modified in order to create aircraft-toaircraft encounters that are not present in the live data. This paper shows that a genetic algorithm can be used to time shift the flights in a scenario in order to create encounters with specific constrained characteristics. For this study these constraints were the distributions of the closest point of approach and the encounter angle of the encounters. The paper first describes how a genetic algorithm was implemented and then presents the results of two series of tests. The first series of tests were designed to determine if the implementation of a genetic algorithm could solve the problem. The second series of tests were designed to assess the time it took the implementation to solve the problem. The results of the study showed that a genetic algorithm could solve the problem in a reasonable time.

Methodology for the generation of air traffic scenarios based on recorded traffic data

This article presents a methodology to generate air traffic scenarios for the support of the testing and analysis of air traffic management decision support tools. This methodology was developed by and is currently being used by the Conflict Probe Assessment Team, a workgroup within the Federal Aviation Administrations Simulation and Modeling Group located at the Federal Aviation Administrations William J. Hughes Technical Center. The individual flights within the scenarios generated by this methodology follow realistic flight routes, yet the air traffic in these scenarios contains aircraft-to-aircraft conflicts and encounters that do not exist in the field. Scenarios with these characteristics are necessary to evaluate decision support tools that predict conflicts. The paper describes each of the three steps that comprise the methodology. The first step is data extraction during which traffic data, available from various sources and recorded in different formats, is extracted and placed into a set of relational database tables. The second step is data modification, where the data in the tables may be manipulated for test purposes. This manipulation may consist of simply culling undesirable flights, or it may involve using a genetic algorithm to time-shift the flights to induce conflicts or encounters. The third step is scenario generation, where scenarios are created based on the traffic data retrieved from the modified database tables. During this final step, the scenarios may be formatted for various target systems. The paper then describes how the Conflict Probe Assessment Team has used this methodology to generate scenarios that have been used for accuracy and risk reduction testing and for a study assessing the effect of weather forecast errors.

Performance Metrics for Tactical Aircraft to Aircraft Conflicts

An air traffic control system’s main function is to separate aircraft. The computer supporting the system assists the air traffic controllers by generating a conflict alert whenever it predicts that two aircraft are about to get too close to each other. The performance of the conflict alert function is a key element to the overall functioning of the air traffic control system. A set of metrics has been designed to measure the conflict alerting performance of an aircraft traffic control system. The key factors are the missed alert rate and the false alert rate. However there are several secondary factors that are essential to measuring the performance, especially in a simulation environment. This paper describes a set of metrics that have been developed to evaluate the performance of the FAA’s en route aircraft traffic control system. They have been applied to the existing system, the Host Computer System, and will be used to establish metrics for the new system now under development, the En Route Automation Modernization system. The metrics are calculated by post processing recorded data that has been produced by running a real time simulation of the air traffic system without controllers, using as input field recorded aircraft data that has been time shifted to induce aircraft-to-aircraft conflicts.