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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].