Air Traffic Volume and Air Traffic Control Human Errors

doi:10.4236/jtts.2011.13007

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Journal of Transportation Technologies, 2011, 1, 47-53

doi:10.4236/jtts.2011.13007 Published Online July 2011 (http://www.scirp.org/journal/jtts)

Air Traffic Volume and Air Traffic Control Human Errors

Woo-Choon Moon, Kwang-Eui Yoo, Youn-Chul Choi

Ministry of Land, Transport and Maritime Affairs, Gwacheon-si, Korea

School of Air Transport, Transportation, Logistics and Air Law, Korea Aerospace University, Goyang City, Korea

School of Aeronautical Engineering, Han S eo U ni versi t y, Seosam-si, Korea

E-mail: [email protected], [email protected], p[email protected]

Received April 5, 2011; revised May 11, 2011; accepted May 22, 2011

Abstract

Navigable airspaces are becoming more crowded with increasing air traffic, and the number of accidents

caused by human errors is increasing. The main objective of this paper is to evaluate the relationship be-

tween air traffic volume and human error in air traffic control (ATC). First, the paper identifies categories

and elements of ATC human error through a review of existing literature, and a study through interviews and

surveys of ATC safety experts. And then the paper presents the results of an experiment conducted on 52 air

traffic controllers sampled from the Korean ATC organization to find out if there is any relationship between

traffic volume and air traffic controller human errors. An analysis of the experiment clearly showed that sev-

eral types of ATC human error are influenced by traffic volume. We hope that the paper will make its con-

tribution to aviation safety by providing a realistic basis for securing proper manpower and facility in accor-

dance with the level of air traffic volume.

Keywords: ATC, Human Error, Air traffic volume, Workload

1. Introduction

The objectives of the air traffic services shall be to pre-

vent collisions between aircraft, prevent collisions be-

tween aircraft on the manoeuvring area and obstructions

on that area, expedite and maintain an orderly flow of air

traffic, provide advice and information useful for safe

and efficient conduct of flights, notify appropriate or-

ganizations regarding aircraft in need of search and res-

cue aid, and assist such organizations as required [1]. In

order to achieve this purpose, air traffic controllers

should be especially apt to deal with the interaction be-

tween humans and mechanical devices while they pro-

vide directions or advices to pilots to maintain vertical

and horizontal separations between aircrafts and avoid

aircraft collision. Accordingly, the air traffic controllers

need to conduct multiple functions at the same time, such

as thinking, listening and speaking. Considering the

complicated nature of this task, one would wonder

whether there is a higher probability for an air traffic

controller to make mistakes when traffic is heavier. The

objective of this paper is to investigate if air traffic con-

trol (ATC) human error is influenced by the size of traf-

fic volume.

First, categories and elements of ATC human error

was studied through existing literature, and interviews

and surveys of ATC safety experts. And then the paper

presents the results of an experiment conducted on 52 air

traffic controllers sampled from the Korean ATC or-

ganization, to find out if there is a relationship between

traffic volume and air traffic controller human error. It

should also be noted that we used an ATC simulator in-

vented by the Korean Government for this experiment.

2. ATC Human Error and Air Traffic

Volume

It is known that a lot of aircraft accidents are caused both

directly and indirectly by human factors. Considering

that human factor involves all aeronautical personnel

who are related to aircraft operations, it is critical to do

an in-depth research on the human factors of pilots and

air traffic controllers who take the most crucial roles in

aircraft operations. As for pilots, there are various studies

and solutions that deal with issues such as the Cockpit

Resource Management (CRM) and incorporation of a

human resource management system into the Line Ori-

ented Flight Training (LOFT). However, researches on

the ATC human factor have been relatively inactive.

According to the Boeing Company, it turned out that

48 W. C. MOON ET AL.

of the commercial aircraft accidents for the past 10 years,

55% were caused by pilot error, 17% by aircraft defect,

13% by weather condition, 5% by airport and ATC, 3%

by maintenance and 7% by miscellaneous matters [2].

Although ATC accounted for only 5% of commercial

aircraft accidents, which is comparatively lower than

other factors, it should not be overlooked that the 55%

portion for which pilot error accounts, either directly or

indirectly involves ATC because the cooperation be-

tween a pilot and an air traffic controller composes a

significant part of aircraft operation.

Inspired by the current CRM program originally de-

signed for the airline cockpit crew, EUROCONTROL

has developed Team Resources Management (TRM) in

order to research human factors in air traffic controllers

[3]. FAA has also created a new area called “ATC-

CRM” for the study of controllers’ errors [4].

The definition of air traffic volume used for ATC

purposes is the maximum number of aircraft entering a

sector in a given length of time. It is generally accepted

that heavy traffic volume may present an excessively

heavy workload to ATC personnel [5] and may thus re-

sult in a higher probability of error [6]. So, it is also nec-

essary to examine workload increase according to traffic

volume increase. US Federal Aviation Administration

(FAA) has reported that supplementary manpower in

ATC is not provided in a timely manner, which cones-

quently causes a heavy workload and finally leads to

more accidents [7]. The ATC workload standard of

EUROCONTROL is as described in the following table

[8].

As ATC control sectors become more complicating

because of the increase in air traffic volume worldwide,

there have been efforts to rearrange sector structures and

introduce more enhanced and automated ATC systems

all around the world. However, due to the increase of

information as well as air traffic volume, air traffic con-

trollers are exposed to problems and situations that they

have never experienced before, consequently increasing

workload which is the cause of human errors [9]. The

following two accidents are example cases of air colli-

Table 1. Sector hourly capac ity.

Threshold (%) Interpretation Recorded Working time during 1

hour

70 or above Overload 42 minutes and more

54 - 69 Heavy Load 32 - 41 min

30 - 53 Medium Load 18 - 31 min

18 - 29 Light Load 11 - 17 min

0 - 17 Very Light Load 0 - 10 min

Source: EUROCONTROL, Pessimistic Sector Capacity Estimation, 2003.

Table 2. Major airplane accidents related to ATC human

factor.

DateAircraft and accident outline Major cause

1956.6

In the airspace over Grand Canyon,

the U.S, DC-7 aircraft of UAL and

L-1049 aircraft of TWA (both fly-

ing under IFR) had a mid-air colli-

sion at 20,000 feet, causing death of

all 128 passengers.

Air traffic congestion

Shortage of controlling

facility

Shortage of ATC man-

power

Insufficient delivery of

Traffic information

2002.7

While controlled by the ACC of

Zurich, Switzerland, TU-154 air-

craft of Russian Bashkirian Airlines

and B757 cargo aircraft of the U.S.

DHL were flying on a collision

course at the same altitude (FL360).

Both airplanes descended to avoid

each other, then the Bashkirian air-

craft collided at a right angle with

the Boeing cargo aircraft at FL354,

killing all 71 passengers.

ATC instruction error

RADAR malfunction

(Short Term Conflict A-

lert)

Route congestion

Shortage of ATC man-

power

sion in which human errors occurred directly or indi-

rectly because of heavy workload due to high air traffic

volume, and lack of ATC facility and manpower.

3. Structure of ATC Error Elements

Based on a literature review [10], and interviews and

surveys of ATC safety experts, the study categorized the

ATC error into three categories; communication error,

procedure error, and instruction error. The definition of

each error category is as follows:

· Comm unicatio n error refers to errors during radio

communication. Communication error in ATC is divided

into the two categories of errors that occur between a

pilot and an air traffic controller, and the errors that oc-

cur between air traffic controllers. For instance, there are

errors such as not challenging incorrect readback, using

wrong call-signs, using non-standard phraseology, and

missing and clipping the call sign.

· Procedure error involves incompliance with ATC

procedures; for instance, failure to respond to an unan-

swered call, not responding to alarm, not identifying air-

craft, failure to terminate radar services, not issuing ap-

proach clearance, not giving reasons for vectoring in-

formation, failure to deliver information to aircraft, etc.

· Instruction error occurs while conducting control

procedures and communications. Specifically, there are

errors such as delivery of incorrect information, issuing

descent instruction late, issuing flight phase change in-

struction late, direction instruction error, clearance in-

struction error, etc.

This research also tried to define major error elements,

which are components of each ATC human error cate

gory, by the analysis of the data on ATC error items.

These data are obtained from the interviews and surveys

W. C. MOON ET AL.

Table 3. Structure of ATC human error elements.

Category Explanation Elements Operational definition

C1 Incorrect Readback Not

challenged

C2 Wrong callsign Used

C3 Non-standard

Phraseology

C4 Missed call

C5 Callsign

Omission/Truncation

Communica-

tion error

Difficulties in

communicative

interaction or

aeronautical

operations

C6 Clipped call

P1 Failure to respond to

unanswered call

P2 No/late response to

alarm

P3 No level verification

P4 No Identification of

aircraft

P5 Radar service not ter-

minated

P6 Late/No Issuance of

landing clearance

Procedure

error

Errors such as

difficulties in

following

checklists

P7 Reasons for Vectoring

not Given

I1 Incorrect information

passed to aircraft

I2 Late descent

I3 Late change

I4 Altitude Instruction

Error

I5 Heading Instruction

Error

Instruction

error

Errors such as

giving incorrect

instructions

I6 Clearance Instruction

Error

of profoundly experienced ATC practitioners who par-

ticipated to provide their opinions on ATC human error

elements. Finally, we constructed a structure of error

elements as shown in the following table.

4. Empirical Analysis on Level of Influence

on ATC Human Errors according to Air

Traffic Volume

4.1. Preparation for Experiment

This study conducted experiments on ATC duty per-

formance and human error during duty with sampled air

traffic controllers utilizing simulated approach control

lab. The ATC task is generally divided into 4 major parts:

area control, approach control, aerodrome control and

ramp control. According to the Korean Government’s

data (2008), there are about 300 air traffic controllers

who perform this duty. The sample group for this re-

Table 4. Demographic distr i bution of sample gr oup.

Category Category Number of sample Percentage (%)

male 46 88.5

Gender female 6 11.5

In 20s 15 28.8

In 30s 34 65.4

Age

In 40s 3 5.8

ATC 33 36.5

Duty Non-ATC 19 63.5

5 years or less 12 23.1

6 - 10 years 22 42.3

11 - 20 years 16 30.8

Work

experience

21 years and more2 3.8

search was air traffic controllers who are currently or

were previously in charge of ATC aerodrome control and

approach control at international airports in Korea. The

characteristics of this 52 sample group are as described

in the following table.

The ATC simulation equipment utilized for this re-

search is a kind of training device for air traffic control-

lers, developed by the Ministry of Land Transportation

and Maritime (MLTM) of Korea in 2007. This device

can simulate various flight situations that may occur

during air traffic control duty. It has basic functions that

give various control instructions related to flight maneu-

ver such as climb, cruise, descent, and speed control of

an airplane. It enables trainees to experience situations

close to ones that occur during the actual duty of ATC by

simulating situations and conditions such as the approach

course diagram, initial take-off direction, instrument

landing approach path, various weather conditions, etc.

4.2. Conducting the Experiment

In order to carry out the experiment, the above men-

tioned ATC simulator was installed and the training pro-

gram was adjusted so that it could produce the data ap-

propriate for the purpose of this research. There were 3

computers, 4 monitors, 5 radio communication devices

and 2 speakers provided for the participant group con-

sisting of 1 air traffic controller and 3 pilots. The seats

for the pilots and controller is separated by more than 5

meters with a partition, so that verbal communication

between the air traffic controller and pilot can be con-

ducted in a situation similar to that of the actual radio

communication between pilots and the controller.

The spatial background of the experimental scenario is

the terminal management areas of two large international

airports in Korea, Jeju International Airport and Gimhae

International Airport. We conducted the experiment twice

spending four months in total. The first period of ex-

periment was from January 15, 2009 to March 15, 2009,

50 W. C. MOON ET AL.

Table 5. Definition of traffic volume level.

Level of Traffic Volume Number of Aircraft Con-

trolled during 15 minutes

V1 10

V2 20

Experiment 1

V3 30

L1 2.5

L2 5

L3 7.5

L4 10

L5 12.5

L6 15

L7 17.5

L8 20

L9 22.5

L10 25

L11 27.5

Experiment 2

L12 30

and the second one was from July 1, 2009 to August 31,

2009. Each sampled air traffic controller was asked to

perform air traffic control duty for 15 minutes with var-

ied levels of air traffic volume. They were required to try

out transfer of control, issuance of traffic information,

and aircraft separation. In the first period there were

three levels of traffic volume, designated as V1, V2 and

V3. The second period experimented with twelve levels

of traffic volume, designated as L1, L2, L3, L4, L5, L6,

L7, L8, L9, L10, L11 and L12 (refer to Table 5). This

volume spectrum is defined to accommodate the entire

range of possible situations, from a very low level to an

extremely high level of traffic volume. As one can see at

Table 5, the level of traffic volume is defined by the

number of aircraft controlled by each sampled controller

during the 15 minute experimental session. The number

of errors made by each sampled controller was counted

utilizing the records of ATC duty during his/her experi-

mental session.

4.3. Analyses

This study established four hypotheses, in order to dis-

cuss the relationship between air traffic volume and fre-

quency of error occurrence:

Hypothesis 1: Overall ATC Error will increase as air

traffic volume increases.

Hypothesis 2: Communication error will increase as

air traffic volume increases.

Hypothesis 3: Procedure error will increase as air traf-

fic volume increases.

Hypothesis 4: Instruction error will increase as air traf-

fic volume increases

4.3.1. Test of Hypothesis 1

The error data obtained in the second period of the ex-

periment was used to test hypothesis 1. The level of in-

Table 6. Frequency of error by the level of air traffic vol-

ume.

Level of Air

traffic volume

Average Fre-

quency of error

Level of Air

traffic volume

Frequency

of error

L1 1.370 L7 3.370

L2 1.550 L8 3.553

L3 1.962 L9 3.970

L4 2.350 L10 4.904

L5 2.765 L11 5.701

L6 3.178 L12 6.304

Figure 1. Regression r esult; frequency of error and level air

traffic volume.

fluence that traffic volume has on frequency of error oc-

currence is presented in Table 6. First, we performed a

correlation analysis to see the level of correlation be-

tween the two variables, traffic volume and frequency of

ATC human errors. The result showed that 0.984 (p <

0.01) was the correlation coefficient. So, it can be said

that there is very strong relationship between the level of

air traffic volume and the error frequency of the air traf-

fic controller. And we also performed a simple regres-

sion analysis utilizing traffic volume as the independent

variable “x”, and frequency of error as the dependent

variable, “y”. The result of the regression analysis was “y

= 0.0516 x + 0.1886” with an R2 value of 0.9675, which

also indicates high significance.

Although the overall equation shows a linear regres-

sion curve, there are some areas where one can detect un-

proportionally higher marginal increase in frequency of

error. As you can see in Figure 1, the marginal increase

in error frequency from the volume level 22.5 to 27.5 is

higher than in other areas.

Table 7 shows the marginal increase in error fre-

quency at each level of traffic volume. The value in the

third column and sixth column of Table 7 is calculated

utilizing the following equations;

∆у = уi+1 – уi

уi+1 = frequency of error at xi+1 traffic volume

уi = frequency of error xi traffic volume

where, xi = traffic volume (number of aircraft controlled

per unit time),

yi = frequency of error occurrence

Referring to Table 7, it can be said that the marginal

W. C. MOON ET AL.

Table 7. Marginal increase in frequency of error by air traffic volume.

Traffic volume (xi)

Frequency of error

(yi)

marginal increase in

frequency (∆у) Traffic volume (xi) Frequency of error (yi) marginal increase in

frequency (∆у)

2.5 a/c 1.370 0 17.5 3.370 0.200

5 1.550 0.180 20 3.553 0.183

7.5 1.962 0.412 22.5 3.970 0.427

10 2.350 0.388 25 4.904 0.934

12.5 2.765 0.215 27.5 5.701 0.797

15 3.178 0.418 30 6.304 0.603

Table 8. Error frequency by the level of air traffic volume.

Air traffic

volume (V*)

Error

Type

Minimum

frequency

Maximum

frequency

Average

frequency

Standard

deviation

V1 0 4 1.75 1.26

V2 1 5 2.73 1.19

Commu-

nication

error 0 6 3.28 1.97

V1 0 5 1.51 1.05

V2 1 8 3.23 1.72

Proce-

dure

error 1 10 5.00 2.24

V1 0 2 0.42 0.72

V2 0 5 1.59 1.53

Instruc-

tion

error 0 6 2.34 1.78

Table 9. Frequency of each element of communication error

by level of air traffic volume.

Communication error (C)

Level of Air Traffic VolumeC1 C2 C3 C4 C5 C6

V1 0.08 0.08 0.33 0.35 0.27 0.65

V2 0.23 0.15 0.35 0.54 0.52 0.94

V3 0.35 0.15 0.44 0.42 0.58 1.35

Figure 3. Frequency of communication error by air traffic

volume.

Table 10. Frequency of each element of procedure error by

level of air traffic volume.

Procedure error (P)

Level of Air Traffic Volume

P1 P2 P3 P4 P5 P6 P7 P8

V1 0.00 0.13 0.00 0.22 0.13 0.09 0.17 0.78

V2 0.91 0.91 0.04 0.43 0.00 0.17 0.17 1.39

V3 1.22 1.22 0.09 0.65 0.39 0.35 0.39 1.78

Figure 2. Air traffic volume and frequency of error.

error frequency of each category was significantly af-

fected by the level of traffic volume. It was confirmed

that all three hypotheses were accepted with the signifi-

cance level of 0.05. This means that the error frequency

of each category of ATC human error is influenced by

air traffic volume in a statistically significant manner.

increase in error frequency is highest at the level of traf-

fic volume between 22.5 to 25.

4.3.2. Test of Hypotheses 2, 3 and 4

Hypotheses 2, 3, and 4 were tested with the data obtained

in the first period of the experiment. Those hypotheses

focus on error frequency variation of each error category,

such as communication error, procedure error and in-

struction error, depending on the level of traffic volume.

Table 8 and Figure 2 shows the distribution of average

error frequency for each category of ATC human error.

Each of the hypotheses was tested separately through

ANOVA (Analysis of Variance) to see if the average

error frequency of each category was significantly af-

fected by the level of traffic volume. It was confirmed

Table 9 is the distribution of error frequency for each

element in the communication error category. Figure 3

presents this information as a diagram. Table 10 is the

distribution of error frequency for each element in the

procedure error category. Figure 4 shows this in the

form of a diagram. Table 11 is the distribution of error

frequency for each of the 6 elements in the instruction

error category. Figure 5 shows this information in the

form of a diagram.

52 W. C. MOON ET AL.

Figure 4. Frequency of procedure error by air traffic vol-

ume.

Table 11. Frequency of each element of instruction error by

level of air traffic volume.

Instruction error (I)

Level of Air Traffic Volume

I1 I2 I3 I4 I5I6

V1 0.04 0.09 0.04 0.09 0.04 0.00

V2 0.13 0.35 0.48 0.26 0.09 0.04

V3 0.09 0.48 0.96 0.26 0.22 0.13

Figure 5. Frequency of instruction error by air traffic vol-

ume.

5. Conclusions

Despite the utilization of high-tech equipment, ATC is

still mostly dependent on individual decision-making

[11], which is always subject to probability of occur-

rences of human error. The main purpose of this research

was to test a few hypotheses that claimed that the fre-

quency of ATC human error will be influenced by the

level of air traffic volume. The required data were gath-

ered through experiments that utilized a sample of air

traffic controllers who are currently working for the Ko-

rean ATC organization, and the ATC simulator.

We found there are significant relationships between

ATC human error and the level of traffic volume. As the

air traffic volume increases, the frequencies of error oc-

currence for most ATC human error elements defined by

this study, were verified to increase. Especially, the in-

crease of procedure errors was remarkably high com-

pared to other error categories. The marginal increase in

frequency of error from traffic volume 22.5 to 25 (num-

ber of aircraft) was revealed to be the highest. Therefore,

it may be efficient to limit traffic volume to less than 22

aircraft per 15 minutes. However, it is necessary to con-

sider differences that originate from factors such as air

traffic control system of each air traffic control facility,

sector conditions and flight procedures when determin-

ing the appropriate traffic volume.

It would be effective to assess the determined traffic

volume on a regular basis, and to take corrective meas-

ures such as supplementing manpower, increasing con-

trolling seats and enhancing air traffic control systems in

case determined traffic volume is exceeded. We hope

that the paper will make its contribution to aviation

safety by providing a realistic basis for securing the

proper amount of manpower and facility in accordance

with the level of air traffic volume.

6. References

[1] International Civil Aviation Organization, “Air Traffic

Services”, Annex 11 to International Convention on Civil

Aviation, 13th Edition, Montreal, 2001.

[2] Boeing Company, “Statistical Summary of Commercial

Jet Airplane Accidents Worldwide Operation”, Chicago,

2006.

[3] EUROCONTROL, “Guidelines for Developing and Im-

plementing Team Resource Management”, Brussels, 1996.

[4] Federal Aviation Administration, “The Effects of Perfor-

mance Feedback on Air Traffic Control Team Coordina-

tion: A Simulation Study”, Washington D.C. 2000.

[5] S. Malakis, T. Kontogiannis and B. Kirwan, “Managing

Emergencies and Abnormal Situations in Air Traffic

Control (part I): Task Work Strategies”, Applied Ergo-

nomics, Vol. 41, No. 4, 2010, pp. 620-627.

doi:10.1016/j.apergo.2009.12.019

[6] M. D. Rodgers, Richard H. Mogford and Leslye S. Mog-

ford, “The Relationship of Sector Characteristics to Op-

erational Errors”, Federal Aviation Administration Civil

Aeromedical Institute, Oklahoma City, 1998.

[7] Federal Aviation Administration, “FAA Strategies for

Reducing Operational Error Causal Factors”, Washington

D.C., 2004.

[8] EUROCONTROL, “Pessimistic Sector Capacity Estima-

tion”, Brussels, 2003.

[9] J. Ponds and A. Isaac, “Development of an FAA-EURO-

CONTROL Technical for the Analysis of Human Error in

ATM (DOT/FAA/AM-02/12)”, EUROCONTROL, Brus-

W. C. MOON ET AL.

sels, 2002.

[10] International Civil Aviation Organization, “Normal Op-

eration Safety Survey (NOSS)”, Doc 9910, ICAO, Mont-

real, 2008.

[11] Christopher D. Wickens, Anne S. Mavor, James McGee,

“Flight to the Future: Human Factors in Air Traffic Con-

trol”, National Academy Press, Washington D.C., 1997.