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3
Aviation Safety and Pilot Commuting
The concern about the potential effects of pilot commuting on fatigue
is rooted in concerns that increased pilot fatigue might increase the risk of
an airline accident. As discussed in Chapter 4, there is extensive scientific
evidence on the negative effects of fatigue on the performance of many cog-
nitive tasks, including those essential for safely operating a commercial air-
craft. This chapter provides the context in which to consider that evidence.
This chapter begins with a review of the airline safety record in the
United States and then turns to the sources of improvement in aviation
safety. Of particular importance for the focus of this report is a discus-
sion of those features of the aviation system that can mitigate the risk of
individual pilot fatigue for flight safety. In the third section the chapter ex-
amines investigations of the National Transportation Safety Board (NTSB)
for accidents that occurred from 1982 to 2010 in order to determine how
often fatigue is found to be a probable cause or contributing factor for an
accident and the extent to which there is evidence that commuting might
have contributed to that fatigue. Finally, the chapter examines what is
known about the current patterns of pilot commuting.
AVIATION SAFETY
Figure 3-1 confirms that airline travel is the safest form of passenger
travel in the United States. Measured on the basis of fatalities per 100 mil-
lion passenger miles, the fatality rate for both buses and trains was about
4 times higher than for airlines while the fatality rate for automobiles was
about 75 times higher.
45
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46 THE EFFECTS OF COMMUTING ON PILOT FATIGUE
0.8
Million Passenger Miles
0.7
Fatalities per 100
0.6
0.5
0.4
0.3
0.2
0.1
0
Autos Buses Trains Airlines
Mode
FIGURE 3-1 Safety of travel inFigure 3-1.eps
the United States: 1989-2007.
SOURCE: Derived from data, used with permission, from Air Transport Associa-
tion of America, Inc. (n.d.). See http://www.airlines.org/Economics/DataAnalysis/
Pages/SafetyRecordofUSAirCarriers.aspx [August 2011]. 1927-1937: AA Statistical
Handbook (December 1945). 1938-1971: CAB Handbook of Airline Statistics
(1973), Part VIII, Items 19c, d, pp. 595-596; NTSB Safety Studies Division. 1972-
1982: FAA Statistical Handbook (1972-1982), Table 9.3, p. 161, citing NTSB for
totals; 1983-present: NTSB Aviation Accident Statistics, Table 6. Fatal Accident
Rate excludes incidents resulting from illegal acts, consistent with NTSB practice.
Although measuring safety in terms of fatalities per passenger mile is a
useful way of comparing safety across different modes of road travel, it is
not the most useful way to measure airline safety.1 For automobile travel,
for example, the risk of an accident varies across the types of roads used.
Travel on interstate highways is much safer than travel on arterial high-
ways, which in turn are much safer than travel on local roads (National
Research Council, 2010, Figure 3-10). Travel on rural roads is more dan-
gerous than travel on urban roads for all highway types. But in all of these
categories of highway travel, the risk is roughly proportional to the distance
traveled, so that the risk of a fatal accident on a 200-mile trip is about twice
the risk on a 100-mile trip. Thus, for highway travel, measuring safety on
a passenger-mile basis is a reasonable portrayal of the risk a traveler faces.
1 Transportation safety is usually measured as the ratio of some adverse outcome, such as
an accident or fatality, to a measure of exposure such as the number of trips taken or the
distance traveled.
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47
AVIATION SAFETY AND PILOT COMMUTING
The safety of airline travel is different. With airline flights, the risk of
accident is largely confined to the landing and takeoff phases of flight, in-
cluding the climb, descent, and approach phases.2 Thus, for airline travel,
the risk of an accident on a 1,000-mile flight is virtually the same as on
a 500-mile flight since the only difference is the amount of time spent in
cruise. When looking at airline travel, either across segments of the industry
or over time, it is better to measure safety on a departure basis rather than
on a mileage basis.
A common way to do this is to measure fatal accidents per million
aircraft departures. One shortcoming of this measure, however, is that a
fatal accident is defined as one in which at least one passenger was killed.
In this measure, an accident in which one passenger of 200 passengers on
board was killed is treated the same as one in which all 200 passengers
were killed. So fatal accidents per 1 million departures, although better
than a distance-based measure, is still not a good measure of the risk a
passenger faces when taking an airline flight. However, this measure is
often used when looking at worldwide safety trends because there is often
limited information available about enplanements in some countries, some
ambiguity about the number of passengers killed in an accident, or the
definition of what constitutes a fatality may differ slightly. In the United
States and throughout much of the rest of the world a fatality is considered
to be from the accident if the passenger dies within 30 days of the accident
from injuries suffered in the accident. To reflect the risk to a passenger from
taking an airline flight, a commonly used measure is passenger fatalities per
million enplanements.
Figure 3-2 shows the aviation safety record from 1959 through 2009
for U.S. and Canadian operators (combined) and for operators in the rest
of the world. Canadian operators have generally had comparable safety
to U.S. operators, and the two countries are often grouped together.3 Two
things are apparent in the figure. First, the safety record both in the United
States and Canada and in the rest of the world has improved considerably
since the 1960s and 1970s. Second, the safety record in the United States
and Canada has been markedly better than the combined record for the
rest of the world. It is important to note, however, that the safety record in
the rest of the world varies considerably both by region and by individual
airline: consequently, although the combined safety record is worse than
for the United States and Canada, there are individual airlines in the rest
of the world that have amassed excellent safety records.
2 For commercial jet service between 1999 and 2008, only 10 percent of fatal accidents
occurred during the cruise phase of flight according to the Boeing Commercial Airplanes Statis
tical Summary of Commercial Jet Airplane Accidents (Boeing Commercial Airplanes, 2011).
3 For more discussion of U.S. and Canadian aviation safety, see Oster et al. (1992, Ch. 4).
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50
48
Rest of the world
U.S. & Canadian operators 1991 Through 2010
2.0
Rest of the world
40
U.S. & Canadian operators
1.5
1.0
Annual
fatal
accident 30
0.5
rate
(accidents
0.0
per million
91 92 94 96 98 00 02 04 06 08 10
departures)
Year
20
10
0
59 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10
Year
FIGURE 3-2 U.S. and Canadian operators accident rates by year.
SOURCE: Boeing Commercial Airplanes (2011, p. 18). Reprinted with permission.
Figure 3-2.eps
landscape
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49
AVIATION SAFETY AND PILOT COMMUTING
0.160
Passenger Fatalities per
0.140
Million Enplanements
0.120
0.100
0.080
0.060
0.040
0.020
0.000
1990-2010 1990-2000 2001-2010
Time Period
FIGURE 3-3 U.S. air carrier safety record: 1990-2010.
SOURCE: Data on passenger fatalities and enplanements calculated from infor-
mation from the National Transportation Safety Board (n.d.) and the Bureau of
Transportation Statistics (n.d.-a).
Figure 3-3 shows the U.S. Air Carrier Safety record over the 1990 to
2010 period. As can be seen in the figure, the safety record for the second
half of this period is notably better than for the first half.4 However, look-
ing at aviation safety records over time must be done with care. Airline
accidents are rare events, but when an accident happens, large numbers of
people can be killed, so the passenger fatality rates from year to year show
considerable variation. Therefore, one needs to be cautious in drawing in-
ferences about airline safety getting better or worse when looking at only
a few years of data.
IMPROVEMENTS IN AVIATION SAFETY
Commercial aviation involves complex interactions and coordination
among equipment, information, and people. As a result it is not surprising
that the reasons aviation safety has improved over time involve a variety
of factors. One source of improvement has been the improved performance
and reliability of critical equipment such as aircraft, engines, and avionics.
Equipment failures have decreased dramatically and system redundancy
has typically enabled safe landings when these failures do occur. Similarly,
more accurate air traffic control procedures have improved safety margins
4 Fatalities
from accidents involving illegal acts (sabotage, suicide, and terrorism) have been
excluded from this analysis.
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50 THE EFFECTS OF COMMUTING ON PILOT FATIGUE
both in the air and on the ground. Airline pilot training has benefited from
the widespread use of improved training programs and advanced flight
simulators in which pilots can learn to manage both normal and non-
normal events safely (Helmreich et al., 1999). Many of these and other
improvements have resulted from the combined efforts of many people and
organizations—including the National Transportation Safety Board, the
Federal Aviation Administration, airframe and aircraft component manu-
facturers, airlines, pilots, and many others—to understand the causes of
accidents and to take steps to reduce the risk of future accidents.
A particularly important component of aviation safety improvement
for the purpose of the committee’s work has been the joint application of
procedural, social, and technological systems to identify crew errors on
the flight deck and to facilitate their correction or mitigation. Such errors
can stem from a variety of human factors including fatigue. One approach
known to reduce risks from errors is crew resource management (CRM)
(see Helmreich and Foushee, 2010). CRM training is mandated by the Fed-
eral Aviation Administration (FAA) for the pilots of all Part 121 operators
to facilitate effective crew communication, coordination, and the use of
appropriate resources to prevent error. This systematic training is designed
to enhance the ability of crews to perform as a team in order to reduce the
potential for human error and improve safety on the flight deck. Such train-
ing emphasizes the importance of communication and consultation with
each other regarding potential safety threats (including crew members’ own
fatigue state), managing such threats, confirming actions being taken, and
cross-checking information from both instruments and external sources.
The intention is to improve situational awareness, problem solving, and
decision making.
If an individual crew member is fatigued and thus more likely to make
errors, CRM can help mitigate the effects of fatigue so that the errors are
made less frequently or are caught quickly before they lead to an increased
safety risk. Specifically, the practice of CRM requires a crew member to
monitor other crew members, aircraft automation, and the overall flight
situation and to identify any suspected errors with a verbal challenge that
must be acknowledged. Such crew coordination practices have been shown
in observational studies to be effective in identifying, trapping, and correct-
ing pilot errors due to fatigue (Foushee et al., 1986; Thomas et al., 2006;
Petrilli et al., 2007; Helmreich and Foushee, 2010; Thomas and Ferguson,
2010).
Checklists are another highly reliable error-trapping mechanism
(Boorman, 2001; Pronovost et al., 2006) that can help pilots avoid miss-
ing key actions for successfully completing important safety-related tasks.
Similarly, the use of callouts can help maintain attention both for the person
making the callout and the person receiving it. The use of standard operat-
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51
AVIATION SAFETY AND PILOT COMMUTING
ing procedures and the annual training that reinforces their use provides
highly structured, routinized processes that can facilitate reliable and re-
peatable cognitive performance. In addition, social interaction among the
crew members can help maintain alertness on the flight deck and, through
exchanging relevant information, can help reorient a pilot to focus on task
performance. Taken together, these forms of crew interaction can help miti-
gate fatigue risk in individual pilots as well as fatigued crews. A potential
downside is that they may mask a pilot’s awareness of his or her actual
level of fatigue.
For very long flights of more than 8 hours, crew augmentation, adding
one or two additional crew members, can help mitigate fatigue risk par-
ticularly when inflight rest facilities such as bunks are provided for crew
members to sleep when they are not on duty. Even on shorter flights, re-
search has shown that short, controlled naps are a well-established fatigue-
mitigation strategy (Rosekind et al., 1994; Werfelman et al., 2009) that can
enhance all cognitive and physiological processes.5 However, in considering
naps, one has to take account of sleep inertia so that recovery time is pro-
vided before the crew member has to perform.
Flight deck technologies can also help mitigate the effects of fatigue.
Onboard map displays have greatly enhanced crews’ cognitive situation
awareness regarding airplane navigation (Wiener and Nagel, 1988). A
range of systems such as stall and wind shear warnings, Traffic Collision
Avoidance Systems, and Ground Proximity Warning Systems (now part of
the Terrain Awareness and Warning System) have been shown to be highly
effective in helping crews manage safety risks even when tired at the end of
a long flight or series of flights (see, e.g., Kuchar and Drumm, 2007). More
generally, when designed properly, automation can support and supplement
the cognitive capacity crews need to operate safely, while enabling a pilot
to transition back to taking over the aircraft manually when necessary. Air
traffic control flight monitoring can also trap and help correct errors both
by monitoring by human controllers and with automated systems such as
Minimum Safe Altitude Warning Systems.
Each of these systems and processes can be effective in mitigating risks
to safety from an individual’s fatigue but none is completely reliable and
some introduce other cognitive loads. Taken together, however, they help
mitigate potential safety risks of fatigue.
FATIGUE-RELATED AVIATION ACCIDENTS
A complication in understanding past accidents and in preventing future
ones is that airline accidents rarely have a single cause. Rather, accidents are
5 Napping is discussed further in Chapter 4.
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52 THE EFFECTS OF COMMUTING ON PILOT FATIGUE
usually the culmination of a sequence of events that involve multiple causes
and contributing factors. It is often difficult to determine what happened
that led to an accident and what the contributing factors were, particularly
when the flight deck crew is killed in the accident and cannot provide input
to the investigation. Although there is usually information about what they
were saying from the cockpit voice recorder and information about what
was happening to the aircraft from the flight data recorder, there can often
be some doubt about whether all of the things that may have contributed
to the accident were identified and understood.
Assessing the role that pilot fatigue may have played in an accident is
a challenge because of other potential contributing factors. In some cases,
the cockpit voice recorder may reveal that pilots talked about being fatigued
during the flight or there may have been other signs of fatigue from the
cockpit voice recorder. In other cases, the record may be clear that a pilot
received very little sleep prior to the flight.
Beyond assessing the role of fatigue in an accident, assessing the role
that pilot commuting may have played in pilot fatigue may be an even
greater challenge. A pilot who lives close to the domicile and has a short
commute may not necessarily arrive for duty well rested depending on
the pilot’s activities prior to the commute. If the pilot did not sleep well the
night before reporting for duty or if the pilot engaged in physically tiring
activity prior to reporting for duty, then the pilot may be fatigued even if the
commute was very short. Conversely, if the pilot commutes to the domicile
by air from a distant point, that pilot will not necessarily report for duty
fatigued. The pilot may fly to the domicile city the day before the duty cycle
begins and get a good night’s sleep in a hotel before reporting for duty. It
is important to realize that the length of the commute, measured either by
distance or time spent commuting, does not necessarily determine whether
or not the pilot reports for duty fit and well rested.
As discussed in Chapter 4, fatigue can be exacerbated by cumulative
sleep debt, the situation when sleep obtained over multiple days is too short
in duration to maintain alertness. If a commute prior to the start of duty
contributes to cumulative sleep debt from inadequate sleep throughout a
multiday trip, then it is conceivable that commuting may have contributed
to fatigue that built during the multiday trip and subsequently contrib-
uted to an accident. In the analysis of NTSB accident reports discussed be-
low, the committee was unable to assess whether this might have happened
in any of the fatigue-related accidents.
Although there is strong evidence that fatigue can result in deteriorated
pilot performance (discussed below), even in such cases, the fact that a pilot
is likely to have been fatigued does not necessarily mean that the pilot’s fa-
tigue resulted in errors made during the accident sequence or contributed to
the cause of the accident. Well-rested pilots have been involved in airplane
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53
AVIATION SAFETY AND PILOT COMMUTING
crashes and fatigued pilots have completed flights without accidents. How-
ever, because the contribution of fatigue can be difficult to detect during an
accident investigation, it is quite possible that fatigue may have contributed
to accidents even when there is no clear evidence of pilot fatigue in the ac-
cident record.
Committee’s Method of Analysis
Recognizing these challenges, the committee examined NTSB reports of
recent accidents6 to try to assess the roles that pilot fatigue and commuting
may have played as risks to aviation safety. Between 1982 and 2010, there
were 863 accidents in the Part 121 portion of the industry where the NTSB
had determined the probable cause and contributing factors7 to the accident.
One approach would have been to look at the accident reports for all
863 accidents to determine how often pilot fatigue or commuting might
have played a role in the accident. Unfortunately, the committee did not
have the time or the resources to conduct such an analysis. Instead, the
committee did an electronic search of the NTSB Aviation Accident and
Incident Data System, which contains information collected during NTSB
investigations of accidents and incidents involving civil aircraft within the
United States, its territories and possessions, and in international waters.
This system contains both the NTSB “probable cause reports,” which pro-
vide the NTSB findings as to the probable cause and contributing factors of
the accident, and the NTSB “factual reports,” which provide descriptions
of the sequence of events that culminated in the accident.8
One limitation of this analysis is that it provides no information about
how often pilots were fatigued during their flights but were not involved in
an accident. A second limitation of this approach is that accidents in which
6 An aircraft accident is defined in Title 49 Section 830.2 as “an occurrence associated with
the operation of an aircraft which takes place between the time any person boards the aircraft
with the intention of flight and all such persons have disembarked, and in which any person
suffers death or serious injury, or in which the aircraft receives substantial damage.”
7 “The NTSB determines the probable cause or causes of accidents. The objective of this
determination is to discern the cause-and-effect relationships in the accident sequence. This
could be described as why the accident happened. In determining probable cause, the NTSB
considers all facts, conditions, and circumstances associated with the accident. Within each
accident occurrence, any information that helps explain why that event happened is designated
as either a ‘cause’ or ‘factor.’ The term ‘factor’ is used to describe situations or circumstances
that contribute to the accident cause” (National Transportation Safety Board, 2010a, p. 52).
8 The database was accessed through the FAA’s Aviation Safety Information Analysis and
Sharing System (ASIAS) (see http://www.asias.faa.gov/portal/page/portal/asias_pages/asias_
home/datainfo:databases:k-o) [June 2011] by using the NTSB Query Tool. The database can
be accessed directly through the NTSB website, but the ASIAS website provides easier and
quicker access to the same data.
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54 THE EFFECTS OF COMMUTING ON PILOT FATIGUE
fatigue may have played some role in the accident but in which the NTSB
determined that the role was not sufficient for fatigue to be considered a
probable cause or contributing factor will not be included. For example,
considerable attention was paid to the first officer’s commute and possible
fatigue following the 2009 Colgan Air crash in Buffalo, New York. How-
ever, this accident was the culmination of a series of events and errors by
the flight crew and the NTSB did not find that fatigue was either a probable
cause or a contributing factor in that accident, so that accident was not
included in our analysis as a fatigue-related accident.
Both fatigue and commuting were discussed in the NTSB report on the
Colgan accident. In the wake of that accident, the NTSB made 25 safety
recommendations. One of those recommendations was related to fatigue
and recommended that the FAA:
Require all 14 Code of Federal Regulations Part 121, 135, and 91K opera-
tors to address fatigue risks associated with commuting, including iden-
tifying pilots who commute, establishing policy and guidance to mitigate
fatigue risks for commuting pilots, using scheduling practices to minimize
opportunities for fatigue in commuting pilots, and developing or identify-
ing rest facilities for commuting pilots. (National Transportation Safety
Board, 2010b, pp. 112-113)
To carry out its analysis, the committee did an electronic search of the
NTSB’s online accident database for Part 121 accidents between 1982 and
2010 where the probable cause or contributing factor contained any of the
words “fatigue” or “tired” or “sleep” or “commute” or “commuting.”
Each record found in the search was reviewed to see if the reference was
to pilot fatigue. (Many of the references were to component failure due to
metal fatigue.)
Table 3-1 shows the number of accidents in each injury category and
how many of those accidents had references to pilot fatigue, including the
statements on probable cause and contributing factors.9 Of the 863 Part
121 accidents that occurred during this period, nine of the accidents made
some reference to pilot fatigue as a contributing factor.
Table 3-2 lists each of the nine accidents with fatigue as a probable
cause or contributing factor. Each accident report was examined individu-
9 The NTSB injury categories are defined as follows: Fatal—Any injury that results in death
within 30 days of the accident; Serious—Any injury that (1) requires the individual to be
hospitalized for more than 48 hours, commencing within 7 days from the date the injury
was received; (2) results in a fracture of any bone (except simple fractures of fingers, toes,
or nose); (3) causes severe hemorrhages, nerve, muscle, or tendon damage; (4) involves any
internal organ; or (5) involves second- or third-degree burns, or any burns affecting more than
5 percent of the body surface; Minor—Any injury that is neither fatal nor serious; None—No
injury (taken from CFR, Title 49, Transportation, Part 830).
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55
AVIATION SAFETY AND PILOT COMMUTING
TABLE 3-1 Total Accidents and Fatigue Accidents by
Injury Category, 1982-2010
Injury Category Total Accidents Fatigue Accidents
Part 121 Fatal 95 2
Part 121 Serious 423 4
Part 121 Minor 78 0
Part 121 None 337 3
Total 863 9
SOURCE: National Transportation Safety Board Accident and Incident
Data System, accessed through the Federal Aviation Administration’s
Aviation Safety Information Analysis and Sharing System (ASIAS).
TABLE 3-2 Fatigue-Related Accidents, 1993-2009
Category of Fatal/
Event Date Operator Name Operation Flight Phase Nonfatal
Aug 18-93 Connie Kalitta Services Nonscheduled Approach Serious
May 8-99 American Eagle Scheduled Landing-Roll Serious
June 1-99 American Airlines Scheduled Landing Fatal
July 26-02 Federal Express Corp Nonscheduled Approach Serious
Oct 19-04 Corporate Airlines Scheduled Approach Fatal
Feb 18-07 Shuttle America Scheduled Landing-Roll None
Corporation
Apr 12-07 Pinnacle Airlines Scheduled Landing None
Jan 27-09 Empire Airlines Nonscheduled Landing Serious
May 6-09 World Airways Nonscheduled Landing-Flare Serious
SOURCE: National Transportation Safety Board Accident and Incident Data System, accessed
through the Federal Aviation Administration’s Aviation Safety Information Analysis and Shar-
ing System (ASIAS).
ally to determine if commuting by the pilots appears to have been a major
contributor to that fatigue.
Connie Kalitta Services
The NTSB Aircraft Accident Report provides the following flight his-
tory factual information for an uncontrolled collision with terrain on Au-
gust 18, 1993: “A Douglas DC-8-61 freighter . . . registered to American
International Airways (AIA) Inc., [doing business as] Connie Kalitta Ser-
vices, Inc., and operat[ed] as AIA flight 808, collided with level terrain ap-
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66 THE EFFECTS OF COMMUTING ON PILOT FATIGUE
TABLE 3-3 Distribution of Home-to-Domicile Distances by Industry
Segment (in percentage)
Greater
Than
Less Than 31-90 91-150 750-1,500 1,501-2,250 2,250
Operation 30 Miles Miles Miles Miles Miles Miles
Mainline 31 14 4 16 4 2
Regional 37 9 4 16 5 1
Cargo 37 4 1 17 7 2
Charter 29 9 4 27 2 1
SOURCE: Data from stakeholders’ input to committee.
have made the commute the day prior to reporting for duty and may have
had a full night’s sleep in a hotel following the commute, prior to report-
ing for duty. The first column of Table 3-3 shows the percentage of pilots
in each of the four industry segments whose home-to-domicile distance is
less than 30 miles. This distance is admittedly arbitrary but is intended to
represent a relative short commute similar to that experienced by much of
the nonpilot workforce. The second column shows the percentage of pilots
in each industry segment whose home-to-domicile distance is between 31
and 90 miles while the third column shows the percentage whose home-to-
domicile distance is between 91 and 150 miles. These columns represent
longer home-to-domicile distances but still ones where a commute is likely
to be made by surface transport. By adding the numbers in the first three
columns, one can see the percentage of pilots whose home-to-domicile dis-
tance is less than or equal to 150 miles. For mainline pilots, this sum is 49
percent; for regional pilots, this sum is 50 percent; for cargo pilots, this sum
is 42 percent; and for charter pilots, this sum is also 42 percent.
The fourth, fifth, and sixth columns in Table 3-3 show the percentages
of pilots whose home-to-domicile distances are, respectively, between 750
and 1,500 miles, 1,501 and 2,250 miles, and greater than 2,250 miles.
These columns represent home-to-domicile distances where one might ex-
pect pilots to commute by air transport. To provide some perspective of
these distances, the straight-line distance between Dallas and Indianapolis
is about 768 miles, the straight-line distance between Salt Lake City and
Detroit is 1,487 miles, and the straight-line distance between San Diego
and Miami is 2,265 miles. Again, by adding these three columns, one
can see that 22 percent of both mainline pilots and regional pilots have
home-to-domicile distances of greater than 750 miles while 26 percent of
cargo pilots and 30 percent of charter pilots have these longer home-to-
domicile distances.
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67
AVIATION SAFETY AND PILOT COMMUTING
Looking more broadly at the data in Table 3-3, several things stand
out. First, the distributions appear to be very similar for mainline and
regional pilots even though these two segments of the industry differ in
many respects. Second, the distributions for the cargo and charter segments
of the industry are different from both each other and from the scheduled
passenger segments. Given their differences in operating and basing poli-
cies (see Chapter 2), this is not surprising. Finally, looking at the right-most
column, it appears that the proportion of pilots who have extremely long
home-to-domicile commutes—coast to coast or international—is in about
1-2 percent across these four industry segments.
Figure 3-4 shows the distributions of home-to-domicile distances for
mainline and regional pilots. The similarity of these distributions seen in
Table 3-3 is even more apparent when the entire distributions are examined.
So in spite of differences in average age, pay, average flight length, and in-
dustry structure, it appears that the home-to-domicile commuting patterns
of mainline and regional pilots are very similar.
Table 3-4 shows the distribution of home-to-domicile distances for
mainline pilots by airline. (The total sample line is the same as the line for
the mainline airlines in Table 3-3.) The four mainline airlines that provided
data included both large, well-established airlines and smaller, more re-
cently established airlines. As can be seen in the table, the top two airlines,
both large established carriers, have similar distributions, while the bottom
Percentage
Miles
FIGURE 3-4 Distribution of home-to-domicile distances for mainline and regional
Figure 3-4.eps
pilots.
SOURCE: Data from stakeholders’ input to committee.
bitmap
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68 THE EFFECTS OF COMMUTING ON PILOT FATIGUE
TABLE 3-4 Distribution of Home-to-Domicile Distances for Mainline
Pilots by Airline (in percentage)
Greater
Mainline Less Than 31-90 91-150 750-1,500 1,501-2,250 Than 2,250
Airlines 30 Miles Miles Miles Miles Miles Miles
A 33 12 5 15 3 1
J 34 18 3 18 4 3
N 18 17 4 20 6 3
W 8 6 3 13 23 19
Total Sample 31 14 4 16 4 2
17,519 Pilots
NOTE: For all home-to domicile distance tables the de-identified airlines have coded alphabeti-
cally based on the order in which the input was received.
SOURCE: Data from stakeholders’ input to committee.
TABLE 3-5 Distribution of Home-to-Domicile Distances for Regional
Pilots by Airline (in percentage)
Greater
Regional Less Than 31-90 91-150 750-1,500 1,501-2,250 Than 2,250
Airlines 30 Miles Miles Miles Miles Miles Miles
C 24 6 4 25 7 2
D 27 4 1 27 3 0
E 47 12 3 6 3 1
F 34 6 13 15 2 2
H 42 12 4 6 3 1
K 22 12 3 18 10 0
O 34 9 4 22 6 1
R 40 6 5 12 4 1
T 100 0 0 0 0 0
U 80 11 0 3 0 2
X 11 16 10 25 5 7
Total Sample 37 9 4 16 5 1
7,533 Pilots
SOURCE: Data from stakeholders’ input to committee.
two, both smaller, more recently established airlines, are different both from
the two larger airlines and from each other.
Table 3-5 shows the distribution of home-to-domicile distances for re-
gional pilots by airline. The 11 regional airlines that provided data included
airlines of varying size and operating in different regions of the country. The
data show that there is variation in home-to-domicile patterns across the air-
lines. One might infer that differences in various characteristics of the airlines
are associated with different home-to-domicile patterns.
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69
AVIATION SAFETY AND PILOT COMMUTING
TABLE 3-6 Distribution of Home-to-Domicile Distances for Cargo Pilots
by Airline (in percentage)
Greater
Cargo Less Than 31-90 91-150 750-1,500 1,501-2,250 Than 2,250
Airlines 30 Miles Miles Miles Miles Miles Miles
B 36 3 1 17 8 3
M 87 13 0 0 0 0
P 81 7 2 3 0 0
S 90 0 3 0 0 3
Total Sample 37 4 1 17 7 2
4,488 Pilots
SOURCE: Data from stakeholders’ input to committee.
TABLE 3-7 Distribution of Home-to-Domicile Distances for Charter
Pilots by Airline (in percentage)
Greater
Charter Less Than 31-90 91-150 750-1,500 1,501-2,250 Than 2,250
Airlines 30 Miles Miles Miles Miles Miles Miles
G 59 24 6 6 0 0
I 4 0 4 46 3 2
L 20 8 10 32 0 0
Q 67 25 3 2 0 0
V 57 7 1 8 3 0
Total Sample 29 9 4 27 2 1
631 Pilots
SOURCE: Data from stakeholders’ input to committee.
Table 3-6 shows the distribution of home-to-domicile distances for
cargo pilots by airline. The four cargo airlines that provided data included
airlines of varying size and operating patterns. The data show that there is
variation in home-to-domicile patterns across the airlines. One might infer
that differences in various characteristics of the airline are to be associated
with different home-to-domicile patterns.
Table 3-7 shows the distribution of home-to-domicile distances for
charter pilots by airline. The five charter airlines that provided data in-
cluded airlines of varying size and operating patterns. The data show that
there is variation in home-to-domicile patterns across the airlines. One
might infer from the table that differences in various characteristics of the
airline are to be associated with different home-to-domicile patterns.
Although the data the committee received are neither a complete ac-
counting nor a randomly drawn sample, the committee believes that they
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70 THE EFFECTS OF COMMUTING ON PILOT FATIGUE
provide useful information and some insight into the home-to-domicile
patterns of pilots in the Part 121 portion of the industry.
The home-to-domicile patterns of the mainline and regional airlines
appear, in aggregate, to be very similar even though these segments of the
industry have markedly different operations and industry structure. In
all four segments of the industry, however, a breakdown of the home-to-
domicile distances by airline suggests that there is considerable variation
across individual airlines. Policies directed at addressing concerns about
the potential impact of commuting on pilot fatigue should recognize this
heterogeneity in the industry.
Time Zone Considerations
The implications of crossing one or more time zones for potential fa-
tigue during duty are complex as such crossings involve the time of day of
flight, the direction of travel (whether traveling east to west where time is
“gained” or west to east where it is “lost”) as well as the standard consid-
erations related to characteristics of the commute. For example, the impli-
cations of crossing multiple time zones would be lessened if the pilot was
able to plan and implement a commute that enabled him or her to obtain
adequate sleep prior to duty (e.g., by arriving the night before). In addition,
crossing time zones in and of itself, particularly a single time zone, is not
an indicator of potential fatigue as the distance traveled can be quite short
or very far. Recognizing these caveats, the committee analyzed the available
zip code data to obtain additional descriptive information related to pilot
residences and domiciles specific to time zones.
The majority of pilots (73.5 percent) reported a residence in the same
time zone as their domicile. A significant additional percentage (18.8 per-
cent) reported a residence one time zone away from their domicile, with
much smaller percentages travelling two time zones (5 percent), three time
zones (2.3 percent), or four or more time zones (.4 percent) time zones. A
similar pattern emerges by type of carrier, particularly when comparing
mainline and regional airlines: see Figure 3-5. However, proportionally
fewer pilots who work for cargo and charter airlines report residences and
domiciles in the same time zone and more report distances that cross one
or two time zones.
When looking at time zones in combination with distance, the scenario
is more complex. The distance between home and domicile for pilots in the
same time zone ranged from less than a mile to 1,288 miles; for pilots who
cross a single time zone, the distance ranged from 14 to 2,439 miles. In
other words, there are long commutes that stay in a single time zone and
short commutes that cross into a different time zone. Similarly, a relatively
small percentage of pilots (11.1 percent) who travel across a time zone
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71
AVIATION SAFETY AND PILOT COMMUTING
100
80
0
Percentage
60 1
2
40
3
20
4+
0
Mainline Regional Cargo Charter
FIGURE 3-5 Share of pilots with home-to-domicile time zone differences.
SOURCE: Data from stakeholders’ input to committee.
Figure 3-5.eps
travel a greater distance than the pilots who have a residence and domicile
in the same time zone and some pilots who crossed three time zones re-
ported a shorter distance between domicile and residence than pilots who
crossed only one or two time zones. The greatest distances travelled obvi-
ously involve travel across multiple time zones. The shortest distance for
pilots travelling across two, three, or four or more time zones, respectively,
are 1,004, 1,656, and 2,890 miles. Table 3-8 shows detailed data for all
pilots as well as by carrier type.
There is little conclusive that can be said about the number of time
zones crossed given wide variation in distances travelled and lack of infor-
mation about how the commute is actually conducted. It is possible that
pilots who commute across multiple time zones are fatigued when they
arrive for work. It is also possible that these pilots fly to their domicile the
night before they are expected to report for duty and obtain adequate sleep
prior to duty. Without information about actual commuting practices, these
data serve merely a descriptive purpose and should not be used to make any
conclusions about the likelihood of fatigue as a result of the corresponding
commute.
Additional Considerations
The committee also reviewed data from NASA’s Aviation Safety Re-
porting System (ASRS). ASRS collects, processes, and analyzes voluntarily
submitted aviation safety incident reports of unsafe occurrences and haz-
ardous situations from pilots, air traffic controllers, dispatchers, flight
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TABLE 3-8 Distance Between Residence and Domicile by Time Zone and Carrier (by percentage within time zone)
72
No Time Zones One Time Zone
MILES ALL ML Reg’l Cargo Chart. ALL ML Reg’l Cargo Chart.
0-60 55.8 52.1 58.8 67.8 58.9 0.1 0.1 0.1
60-120 7.3 8.6 6.1 2.8 9.4 0.7 1.1 0.9
120-180 3.8 4.0 4.3 1.8 3.1 1.6 1.7 3.2 0.1
180-240 5.8 6.5 4.9 4.3 4.2 2.1 2.1 4.4 0.1 0.5
240-300 3.4 3.6 3.1 2.1 4.5 4.3 5.9 5.3 0.5 3.0
300-360 2.2 1.8 2.6 3.2 3.4 4.4 2.8 4.1 8.3
360-420 3.4 3.4 3.1 4.0 0.8 3.0 2.6 0.9 6.0 0.5
420-480 3.0 2.6 3.4 4.7 1.3 2.9 3.8 2.2 2.6
480-540 2.6 2.6 2.8 2.2 3.1 4.0 4.4 3.2 4.0 3.0
540-600 2.1 2.1 2.0 1.8 4.7 5.4 3.5 5.2 8.4 8.1
600-660 1.8 1.8 1.4 2.1 2.9 5.7 4.3 4.2 9.4 7.6
660-720 0.9 0.7 0.8 1.8 1.0 8.5 8.2 10.0 7.5 8.6
720-780 1.1 1.3 0.8 0.3 0.3 10.4 12.6 10.0 7.2 6.1
780-840 0.6 0.6 0.9 0.3 0.5 5.4 4.5 5.7 6.9 5.1
840-900 0.9 1.1 0.8 0.2 0.3 8.5 7.5 6.8 12.4 4.0
900-960 1.8 2.1 2.0 0.1 5.3 4.2 7.0 4.1 16.7
960-1,020 1.4 1.8 0.8 0.3 0.5 5.7 7.0 6.5 2.0 9.6
a
1,020-1,080 1.4 1.9 0.9 0.8 3.3 2.9 3.9 2.4 9.6
1,080-1,140 0.6 0.8 0.4 3.1 3.1 3.0 2.6 6.6
a
1,140-1,200 0.1 0.2 2.0 1.6 1.9 2.2 7.1
a
1,200-1,260 0.1 0.1 0.3 2.7 1.3 3.4 4.7 1.5
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a
1,260-1,320 2.0 1.8 2.2 2.2 1.0
1,320-1,380 1.3 2.0 0.9 0.3 1.5
1,380-1,440 4.5 7.4 3.4 1.2
1,440-1,500 1.0 1.1 0.8 1.4
1,500-1,560 0.8 1.0 0.2 1.1
1,560-1,620 0.7 1.0 0.2 0.5
1,620-1,680 0.1 0.2 0.1 0.1
1,680-1,740 0.1 0.1
a
1,740-1,800
1,800-1,860
1,860-1,920 0.1
a
1,920-1,980 0.1 0.3
1,980-2,040 0.2 0.7
2,040-2,100
2,100-2,160 0.1
2,160-2,220 0.1
2,220-2,280
2,280-2,340 0.1 0.2
2,340-8,400b 0.1 0.1 0.3
continued
73
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TABLE 3-8 Continued
74
Two Time Zones Three Time Zones
MILES ALL ML Reg’l Cargo Chart. ALL ML Reg’l Cargo Chart.
0-60
60-120
120-180
180-240
240-300
300-360
360-420
420-480
480-540
540-600
600-660
660-720
720-780
780-840
840-900
900-960
960-1,020 0.2 0.3 2.3
1,020-1,080 0.5 16.3
1,080-1,140 1.2 1.1 2.2 2.3
1,140-1,200 3.1 4.6 2.2 0.7
1,200-1,260 4.7 6.7 3.0 0.3 16.3
1,260-1,320 3.8 4.1 5.5 11.6
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1,320-1,380 4.6 4.6 7.2 0.3 9.3
1,380-1,440 7.2 7.5 8.5 4.3 11.6
1,440-1,500 4.4 5.5 5.0 0.3 9.3
1,500-1,560 6.4 5.1 6.5 8.3 14.0
1,560-1,620 12.4 13.1 11.7 12.3 7.0
1,620-1,680 13.6 15.1 7.7 19.6 0.4 0.4 0.7
1,680-1,740 6.2 1.2 16.7 6.0 1.0 1.2 0.7
1,740-1,800 5.6 4.9 5.2 8.6 2.7 3.9
1,800-1,860 6.9 5.8 6.7 11.0
1,860-1,920 6.6 2.1 4.5 21.6 6.7 5.9 12.2
1,920-1,980 2.1 3.5 0.7 0.7 8.6 9.8 7.5 16.7
1,980-2,040 1.5 2.1 1.7 5.3 5.3 6.8 16.7
2,040-2,100 0.7 0.4 2.0 6.0 6.3 7.5
2,100-2,160 4.3 6.7 3.0 0.7 3.4 3.1 6.1
2,160-2,220 0.5 0.9 5.1 5.1 7.5
2,220-2,280 0.3 0.5 0.3 4.9 4.3 8.8
2,280-2,340 0.3 0.7 3.9 3.5 6.8
2,340-8,400b 2.8 3.5 5.0 52.0 51.3 35.4 100.0 66.7
aLessthan .05 percent.
bThe distance between domicile and residence for all pilots who travelled across four time zones were all in this range.
75
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76 THE EFFECTS OF COMMUTING ON PILOT FATIGUE
attendants, maintenance technicians, and others.11 There was limited in-
formation available in the reports to determine the degree to which com-
muting was a factor in the reported incidents. Also, since these reports are
voluntarily submitted, in some cases to gain immunity from punishment,
it is not clear the extent to which these reports are representative of the
experiences of the entire Part 121 pilot population. The committee did not
find that these data were useful in the context of the committee’s charge,
and these data are not discussed in the report.
CONCLUSION
CONCLUSION: There is potential for pilots to become fatigued from
commuting. However, there is insufficient evidence to determine the
extent to which pilot commuting has been a safety risk in part because
little is known about specific pilot commuting practices and in part
because the safety checks, balances, and redundancies in the aviation
system may mitigate the consequences of pilot fatigue.
11 For details, see http://asrs.arc.nasa.gov/overview/summary.html [May 2011].
