Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
7The development of this study included 11 tasks. These steps are illustrated in Figure 5. The project started with a kick-off meeting and collection of updated information, particularly to review the literature asso- ciated with runway veer-off incidents, which was not part of the previous ACRP study. Following the literature review, the re- search team collected information to develop the risk models, including accident and incident information, aircraft data to build a criticality factor into the frequency models, as well as complementing the normal operations data (NOD) for general aviation (GA) flights of aircraft with MTOW below 12,000 lb. Three parallel tasks were carried out after the model data were completed and reviewed: the development of risk models for aircraft overruns, veer-offs, and undershoots; the develop- ment of a test plan to validate the approach, the models, and the analysis software; and the development of a software outline to present to the panel. An interim report was prepared and sub- mitted to the panel for discussion during the interim meeting. Following the meeting, the research team pursued tasks on two fronts. The first was the development, testing, and review of the analysis software, and the second consisted of the prepa- ration of data and actions to validate the study. The approved software framework was implemented using Microsoft .Net and Microsoft Office tools (Excel and Access), and a user manual was developed. Eight industry volunteers were selected to test the beta version and provide comments to enhance the solution and eliminate bugs. In parallel, the software team conducted tests to identify and eliminate bugs. A revised version of the software was used to run the analysis for airports selected for validation. Eight airports were selected to run the analyses for valida- tion. Accident and incident data for these airports, as well as operations and weather information covering 1 year, were col- lected. The risk estimates were then compared to the actual ac- cident and incident rates for the airports. The research tasks, the models, and the results are summarized in this report, the last task in this study. Functional Hazard Analysis As part of the literature review for this project, the research team reviewed information on operational experience to de- velop a functional hazard analysis (FHA) for the types of in- cidents relevant to this study. A similar analysis conducted by Eddowes et al. (2001) was used for overruns and undershoots in the ACRP Report 3 study, and a summary is presented in Appendix A. An FHA is a formal and systematic process for the identi- fication of hazards associated with an activity. The purpose of the FHA was to determine relevant causal and contributing factors of veer-off, overrun, and undershoot accidents and hazards to aircraft associated with aerodrome operations and the physical design of airfields. Overrun, veer-off, and undershoot incidents may be consid- ered in terms of the deviation of the aircraft from its intended path. The definition of the deviation for each incident type may be summarized as follows: â¢ For overrun incidents, the âlongitudinal deviationâ is de- scribed by the longitudinal distance traveled beyond the expected accelerate/stop distance (for takeoff events) and beyond the landing distance available (for landing events). â¢ For veer-off incidents, the âlateral deviationâ is described by the lateral distance traveled from the runway longitudi- nal edge. â¢ For undershoot incidents, the âlongitudinal deviationâ is described by the longitudinal distance from the point where the aircraft actually touched down to the runway threshold. â¢ For both overrun and undershoot events, the âlateral de- viationâ is the lateral distance to the extended runway centerline. The identification of factors associated with aircraft over- runs, undershoots, and veer-off was an important step prior to collection of accident and incident data, as this information was required to develop the risk models presented in this study. C H A P T E R 2 Research Approach
Accident and Incident Data Accident and incident data were collected from the following sources: â¢ FAA Accident/Incident Data System (AIDS). â¢ FAA/National Aeronautics & Space Administration (NASA) Aviation Safety Reporting System (ASRS). â¢ National Transportation Safety Board (NTSB) Accident Database & Synopses. â¢ MITRE Corporation Runway Excursion Events Database V.4 (2008). â¢ Transportation Safety Board of Canada (TSB). â¢ International Civil Aviation Organization (ICAO) Accident/ Incident Data Reporting (ADREP) system. â¢ Australian Transport Safety Bureau (ATSB). â¢ Bureau dâEnquÃªtes et dâAnalyses pour la SÃ©curitÃ© de lâAvi- ation Civile (BEA). â¢ UK Air Accidents Investigation Branch (AAIB). â¢ New Zealand Transport Accident Investigation Commission (TAIC). â¢ Air Accident Investigation Bureau of Singapore. â¢ Ireland Air Accident Investigation Unit (AAIU). â¢ Spain ComisiÃ³n de InvestigaciÃ³n de Accidentes e Incidentes de AviaciÃ³n Civil (CIAIAC). â¢ Indonesia National Transportation Safety Committee (NTSC). â¢ Netherlands Aviation Safety Board (NASB). More than 260,000 aviation accident and incident reports were screened from 11 countries to identify the cases relevant to this study. Out of those, more than 140,000 events were screened from U.S. databases. The relevant events were fil- tered prior to gathering data from each report. A list of accidents and incidents containing the cases used for model development is presented in Appendix B of this report. The list includes the accidents that occurred within 2000 ft of the runway ends and within 1000 ft of the runway centerline. The criteria represents the area where the overwhelming ma- jority of runway excursions and undershoots occur and are similar to those used in ACRP Report 3 and by the FAA (David 1990). Using such criteria, 1414 accidents and incidents were identified to provide the information used to develop the frequency and location models. Events that took place since 1980 and for which reports were available were included in the database. Part of the data used to develop the frequency models was complemented from other sources of information, particu- larly for aircraft, airport, and meteorological conditions. For example, in some cases the weather information during the incident was missing and the actual METAR for the airport was obtained. In other situations, the runway used was miss- ing and the FAA Enhanced Traffic Management System Per- formance Metrics (ASPM) was consulted. Filter Applied to the Data Criteria for filtering data were established to make the events comparable. The first filter was an attempt to use information from only specific regions of the world having accident rates that are comparable to the U.S. rate. This information was com- bined with U.S. data to develop the location models. For the frequency models, only U.S. data were used because compre- hensive incident records are only available in the United States. The criteria used are shown in Table 1. The accident and incident database was organized in Mi- crosoft Access. The ACRP Report 3 database was modified to simplify its use. The system provides the software tools needed to utilize the data in a flexible manner and includes the capa- bility to add, modify, or delete data from the database, make queries about the data stored in the database, and produce reports summarizing selected contents. Figure 6 shows the database organization. 8 Literature Review Collection and Preparation of Data Accident & Incident Aircraft Normal Operations Development of Risk Models Development of Test Plan Development of Software Outline Interim Meeting Development of Analysis SoftwareExecution of Test Plan Select airports Collect airport data Run analysis for selected airports Validate models & software Testing of Analysis Software Revised Software Final report Figure 5. Study tasks.
9Table 1. Filtering criteria for accidents and incidents. Filter # Description Justification 1 Remove non-fixed wing aircraft entries Study is concerned with fixed wing aircraft accidents and incidents only 2 Remove entries for airplanes with certified max gross weight < 6,000 lbs Cut off criteria to maintain comparable level of pilot qualifications and aircraft performance to increase the validity of the modeling 3 Remove entries with unwanted FAR parts. Kept Part 121, 125, 129, 135 and selected Part 91 operations. Some FAR parts have significantly different safety regulations (e.g., pilot qualifications). The following cases were removed: o Part 91F: Special Flt Ops. o Part 103: Ultralight o Part 105: Parachute Jumping o Part 133: Rotorcraft Ext. Load o Part 137: Agricultural o Part 141: Pilot Schools o Armed Forces 4 Remove occurrences for unwanted phases of flight Study focus is the runway safety area. Situations when the RSA cannot help mitigating accident and incident consequences were discarded to increase model validity. 5 Remove all single engine aircraft and all piston engine aircraft entries Piston engine aircraft are now used less frequently in civil aviation and therefore have been removed, to increase the validity of the modeling. Moreover single and piston engine aircraft behave differently in accidents due to the lower energy levels involved and the fact that the major focus of this study is air carrier aircraft. 6 Remove all accidents and incidents when the point of first impact and the wreckage final location is beyond 2000ft from runway end and 1000ft from runway centerline. It would be unfeasible to have an RSA with more than 2000ft beyond the threshold or 1000ft from the runway centerline, the gain in safety is not significant and both the previous ACRP study and the FAA study used the 2000ft criteria (David 1990). Figure 6. Accident and incident database for aircraft overruns, undershoots, and veer-offs.
The database includes, for each individual event or opera- tion, the reporting agency, the aircraft characteristics, the runway and environmental conditions, event classification (ac- cident or incident), and other relevant information such as consequences (fatalities, injuries, and damage) and causal or contributing factors required to develop the probability models. A unique identifier was assigned to each event. Summary of Data Figure 7 presents the summary of accidents and incidents by type, and Figure 8 shows the relative percentages for each type. Landing events accounted for 83% of the events. Over- runs (landing and takeoffs) accounted for 44% of accidents and incidents; veer-offs accounted for 48%; and undershoots accounted for only 8% of the total number of events. Figure 9 presents the number of incidents and accidents by year from 1978 to 2008. The number of events reported in the 1970s was relatively low, most likely due to underreporting and lower volumes of traffic. The number of events increased slowly, and there is a sharp drop during the past 3 years. It is possible that some events are still undergoing the investiga- tion and that reports were not available by the time data col- lection was completed. Figures 10 to 14 show the distribution of accidents and in- cidents according to their location. For overruns and under- shoots, the locations refer to the longitudinal distance from the runway end. For veer-offs, it is the lateral distance from the runway longitudinal edge. Five hundred one landing overrun events were identified. In approximately 95% of the events, the aircraft stopped within 1000 ft after overrunning the runway, and close to 77% stopped within 500 ft. One hundred eleven landing undershoot events were iden- tified, and in approximately 94% of the cases, the aircraft touched the terrain within 1000 feet of the runway arrival end. Approximately 85% touched down within 600 feet and 80% within 500 feet. Veer-off distances were measured from the runway edge. Of the 559 cases of landing veer-off identified, in approxi- mately 80% of the cases the fuselage of the aircraft deviated less than 175 feet from the runway edge. For 88% of the events, the aircraft was within 250 feet of the runway edge. A total of 123 takeoff overrun accidents and incidents were identified. For approximately 83% of the cases, the stop loca- tion was within 1000 feet of the runway departure end, and for 56%, the aircraft stopped within 500 feet. Of the 120 takeoff veer-off accidents and incidents, in approximately 76% of the cases the fuselage of the aircraft 10 Accidents/Incidents by Type 0 100 200 300 400 500 Type of Event # of E ve nt s ACC 138 51 111 61 22 INC 363 60 448 62 98 LDOR LDUS LDVOFF TOOR TOVOFF Figure 7. Summary of accidents and incidents by type. Events by Type LDOR 35% LDUS 8% LDVOFF 40% TOOR 9% TOVOFF 8% Figure 8. Percentage of accidents and incidents by type.
11 Reported Events per Year 0 10 20 30 40 50 60 70 19 78 19 80 19 82 19 84 19 86 19 88 19 90 19 92 19 94 19 96 19 98 20 00 20 02 20 04 20 06 20 08 Year N um be r o f O cc ur re nc es ACC INC Figure 9. Number of reported accidents and incidents from 1978 to 2008. Location Distribution for LDOR Events 0 20 40 60 80 100 120 10 0 30 0 50 0 70 0 90 0 11 00 13 00 15 00 17 00 19 00 Mo re Distance from Threshold (ft) # of E ve nt s Figure 10. Location distribution for landing overruns. Location Distribution for LDUS Events 0 5 10 15 20 25 30 35 19 00 17 00 15 00 13 00 11 00 90 0 70 0 50 0 30 0 10 0 Le ss Distance from Threshold (ft) # of E ve nt s Figure 11. Location distribution for landing undershoots. Location Distribution for LDVO Events 0 10 20 30 40 50 60 70 Distance from Runway Edge (ft) # of E ve nt s 50 10 0 15 0 20 0 25 0 30 0 35 0 40 0 45 0 50 0 Mo re Figure 12. Location distribution for landing veer-offs. Location Distribution for TOOR Events 0 5 10 15 20 25 30 # of E ve nt s 10 0 30 0 50 0 70 0 90 0 11 00 13 00 15 00 17 00 19 00 Mo re Distance from Threshold (ft) Figure 13. Location distribution for takeoff overruns.
12 Location Distribution for TOVO Events 0 2 4 6 8 10 12 14 16 18 20 # of E ve nt s Distance from Runway Edge (ft) 50 10 0 15 0 20 0 25 0 30 0 35 0 40 0 45 0 50 0 Mo re Figure 14. Location distribution for takeoff veer-offs. deviated less than 175 feet from the runway edge. In 85% of the events, the aircraft was within 250 feet of the runway edge. Normal Operations Data Another key approach in this study was the use of NOD for probability modeling. In the absence of information on risk exposure, even though the occurrence of a factor (e.g., contam- inated runway) could be identified as a contributor to many accidents, it is impossible to know how critical the factor is, since many other flights may have experienced the factor with- out incidents. With NOD, the number of operations that ex- perience the factor benignly, singly, and in combination can be calculated; risk ratios can be generated; and the impor- tance of risk factors can be quantified. This assessment may allow the prioritization of resource allocation for safety im- provement (Enders et al. 1996). The same NOD used in the ACRP Report 3 study was used in this study. The data were complemented with information for GA aircraft with MTOW lower than 12,500 lb and higher than 6,000 lb. The NOD database comprises a large and representa- tive sample of disaggregate U.S. NOD covering a range of risk factors, allowing their criticality to be quantified. The data and the information on U.S. incidents and accidents were used as a sample to develop the frequency models only. A small sample of the NOD used in this study is presented in Appendix C. Incorporating this risk exposure information into the acci- dent frequency model enhances its predictive power and pro- vides the basis for formulating more risk-sensitive and respon- sive RSA policies. Accident frequency models need no longer rely on simple crash rates based on just aircraft, engine, or op- eration type. As discussed in the following pages, factors pre- viously ignored by airport risk assessments and RSA regulations are accounted for using the models developed in this study. Aircraft Data One of the project goals was to incorporate a factor in the models to account for the impact of aircraft performance and available runway length on probability of incidents. When the distance available is close to the distance required, the safety margin is smaller during the aircraft landing or takeoff, and the likelihood is greater that an overrun or veer-off will occur. Compared to the ACRP Report 3 study, two new factors were included in the improved models: the runway distance avail- able for the operation (takeoff or landing) and the aircraft run- way distance required under the operation conditions. The runway available and required distances were gathered or esti- mated for each accident, incident, and normal operation, ac- cording to the procedures described in ensuing sections. The parameter introduced in the frequency models was the loga- rithm of the ratio between the distance required and the dis- tance available, to address the interaction between the two pa- rameters. When the criticality factor is close to zero, the ratio between the required and available distance is close to one. Aircraft dimensions and performance data were gathered from various sources, including aircraft manufacturersâ web- sites and other databases: â¢ FAA Aircraft Characteristics Database. â Source: FAA â Website: (http://www.faa.gov/airports/engineering/ aircraft_char_database/) â¢ Eurocontrol Aircraft Performance Database V2.0 â Source: Eurocontrol â Website: (http://elearning.ians.lu/aircraftperformance/). â¢ FAA Aircraft Situation Display to Industry (ASDI)âAircraft Types. â Source: FAA â Website: (http://www.fly.faa.gov/ASDI/asdidocs/aircraft_ types.txt). â¢ Boeing Airplane Characteristics for Airport Planning. â Source: The Boeing Company â Website: http://www.boeing.com/commercial/airports/ plan_manuals.html â¢ Airbus Airplane Characteristics for Airport Planning. â Source: Airbus Industrie â Website: airbus.com/Support/Engineering & Mainte- nance/Technical data/Aircraft Characteristics â¢ Embraer Aircraft Characteristics for Airport Planning. â Source: Embraer â Website: http://www.embraeraviationservices.com/ english/content/aeronaves/ Aircraft performance data used to develop the probability models also were incorporated into the analysis software. A summary of the aircraft database is presented in Appendix D.