ABSTRACT as SESAR in Europe and the myriad

ABSTRACT

This
study will be on the use of NextGen Air Traffic Control automation by using a
sample of information on pilot deviation form an unnamed airline.  As ADS-B implementation has been fully
implemented with this airline, I wanted to study how the automation is affecting
(increasing or lowering) deviations. 
Using FOQA data received from aircraft data recorders I was able to get
a sample from 50 aircraft flying out of JFK for ground-based operations before
and after ADS-B implementation. According to the FAA, ADS-B is an environmentally friendly technology
that enhances safety and efficiency, and directly benefits pilots, controllers,
airports, airlines, and the public. It forms the foundation for NextGen by
moving from ground radar and navigational aids to precise tracking using
satellite signals.  Results unfortunately
were not returned by the time of the writing of this paper and I have zero
conclusions to offer.  That being stated
as someone who has flown both with and without ADS-B, I can state that controllers
are much more aware of where aircraft are and are able to stop them much
quicker if something is wrong.

1.0 INTRODUCTION

During my first course of my master’s degree I
wrote papers on the implementation of NextGen air traffic control into the Air
Traffic Management System.  This included
U.S. NextGen software, as well as SESAR in Europe and the myriad of other new
systems that are being adopted and meant to work together across the
globe.  These systems are hoped to allow
for greater capacity of air travel in an ever-growing market.  I wanted to use my time and research to see
if the new systems are actually increasing safety as they increase
automation.  I have been working with the
company that I work for in getting data from flight recorders to help with
this.  It has been a process, as most of
the information is confidential, so it must be handled with care.  That being said, no information that is being
given out in this paper is confidential but will not contain names of people or
the company. 
With the completion of ADS-B implementation on all of this airlines aircraft, a
study was started to see if the numbers of runway incursion and pilot deviation
were being lowered.  While it is possible
to see the population numbers and get an exact look on the year, we wanted to
get a sample size for a small portion of the time it had been working out of
our main airport.  With that it was
decided to analyze flights out of JFK airfield to see what, if any, changes had
occurred due to the new technology.

2.0 Literature
Review

Evaluation of
NextGen Air Traffic Control Operation with Automated Separation Assurance
by Thomas Prevot, Joey Mercer, Lynn Martin, Jeffrey Homola, Christopher Cabrall
and Connie Brasil.  They discuss problems
with safely increasing the capacity of airspace while describing approaches to
allocating separation with ground-based automation.  They used past task studies and an experiment
simulation of the operation in an air traffic environment.  The researchers used a one-way ANVOA test on
the mean workload of four serperate sectors 
to examine the differences as well as Tukeys Honestly Significant
Differences test.  They developed
conclusions that “Overall traffic density has a primary impact on workload,
safety, and acceptability of the concept. 
The Concept of ground-based automated separation assurance, safety,
workload and acceptability are no longer directly linked to the total aircraft
within a sector.”  This means that based
off of their research, it is possible to accommodate the higher demand of
future air travel. (Mercer, et al., 2010)

Algorithm and
operational concept for solving short range conflicts by H. Erzberger and
K. Heere.  A candidate for the
next-generation sys- tem, referred to as the automated airspace concept (AAC),
incorporates two levels of protection against conflicts and one against
collisions.
The first level, referred to as the autoresolver, is designed for resolving
conflicts that are ?2–30min to first loss of separation. The second level
of separation assurance handles conflicts that are not detected until loss of
separation is <2 min away or, even if successfully detected, could not be resolved successfully by the first level. A system referred to as the tactical separation assured flight environment (TSAFE) is hypothesized for detecting and resolving these close-in conflicts. TSAFE would automatically take control of resolving close-in conflicts when the conflict detection element of TSAFE predicts that time to loss of separation has breached a critical time threshold. The design and performance of the detection element of TSAFE have been described in several papers 4, 5. This article focuses on the design of the resolution function of TSAFE, which is referred to as TSAFE Resolution by describing an algorithm designed specifically for resolving close-in conflicts in the horizontal plane. The concept has the potential to increase airspace capacity by allowing controllers to handle more traffic while committing fewer operational errors. (Erzberger & Heere, 2009) Training student air traffic controllers to trust automation by Adriana Miramontes, Andriana Tesoro, Yuri Trujillo, Edward Barraza, Jillian Keeler, Alexander Bourdreau, Thomas Strybel, and Kim-Puong Vu.  This paper investigates if trust in automation could be trained into controllers in a NextGen environment.  The looked at whether trust could be changed over time as a function of training and if it positively affected their performance in terms of safety and efficiency.  The researchers used a 2x2 mixed factorial ANOVA was run.  They also ran a multiple regression.  Their research found that trust could be trained into controllers and it did not affect performance measures of safety.  They were also able to find that high emotional stability was related to higher trust in the automation. (Miramontes, et al., 2015) Air traffic controller trust in Automation in NextGen by Tannaz Mirchi, Kim Phuong Vu, James Miles, Lindsay Sturre, Sam Curtis, Thomas Strybel.  The goal of this study was in determining proper measures of trust in automation, effective training methods for ensuring proper trust in automation, and the effects of trust on ATC performance and Situational Awareness.  A 3x3 within-subject's analysis of variance was conducted to evaluate whether there were differences in sensitivity between trust ratings measured over the course of the testing they did.  They discovered that over a 16-week internship the trust ratings increased but the number of aircraft near-misses was not significantly different.  One other disturbing find was that many of the participants whose trust in automation went up had more near misses, which could be blamed on complacency negatively affecting their situational awareness. (Mirchi, et al., 2015) These articles are all focused on the same concepts as I am, NextGen software, but were focused on the controller side.  I wanted to study the pilot and aircrew side of the equation.  What was shown in the studies is that while overall the capacity of air travel is able to be increased by the software, it did not drastically change safety.  Much of this can be blamed on complacency or the increased traffic that would result from it. METHOD Experimental design was to be used to test random crew flight data.  This flight data was used to get information on Runway, taxi, and low altitude air-to-air conflict scenarios.  100 flights were randomly selected for use in the test over separate 3 month periods out of JFK. 50 before and 50 after implementation of ADS-B implementation. This allows for a sample testing and idea of the how often pilot deviations were occurring before implementation and a sample testing after to see if there had been any differences. The plan was to use the data to develop an ANOVA test based on the mean number of instances that happened on the runway, taxiway and in low altitude air-to air conflicts.  However, the data was not secured in time for the paper.  The through process was that the study would give me a better understanding of if we were seeing results from the changes.  However, I bit off more than I could chew and have little to show for it. RESULTS No results were found due to the lack of receiving the data on time.  This is my fault.  It is my belief that we would not have had significant findings based on the small sample size and the already rare occurrence of runway incursion/pilot deviations.  The intent is still to do the follow up testing and use the results as a possible work in capstone course. However, I can state from doing the work that we have seen a drastic change in how controllers are able to see what pilots are doing and intervene before any incidents can happen.  If this is changing the actual statistical numbers, I cannot say at this time. CONCLUSION At this time, no solid conclusions can be made on the data.  What I have seen with my own eyes is that the greater the automation the more controllers and pilots can do.  It is allowing for a larger capacity for aircraft in the system.  This stated, automation has a tendency to cause complacency as people just assume "George" the computer will solve everything.  This can lead to danger and breakdowns in safety.