Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 14
14 Table 3. Filtering criteria for accidents and incidents. Filter # Description Justification 1 Remove non-fixed-wing aircraft Study is concerned with fixed-wing aircraft entries. accidents and incidents only. 2 Remove entries for airplanes Cut-off criteria to maintain comparable with certified max gross weight level of pilot qualifications and aircraft < 6,000 lb. performance to increase the validity of the modeling. 3 Remove entries with unwanted Some FAR parts have significantly Federal Aviation Regulation different safety regulations (e.g., pilot (FAR) parts. Kept Part 121, qualifications). The following cases were 125, 129, 135, and selected Part removed: 91 operations. Part 91F: Special Flt Ops Part 103: Ultralight Part 105: Parachute Jumping Part 133: Rotorcraft Ext. Load Part 137: Agricultural Part 141: Pilot Schools Armed Forces 4 Remove occurrences for Study focus is the RSA. Situations when unwanted phases of flight. the RSA cannot help mitigating accident and incident consequences were discarded to increase model validity. 5 Remove all single-engine Piston-engine aircraft are used infrequently aircraft and all piston-engine in civil aviation and have been removed to aircraft entries. increase the validity of the modeling. Moreover, single- and piston-engine aircraft behave differently in accidents due to the lower energy levels involved. Finally, the major focus of this study is air carrier aircraft. 6 Remove all accidents and It would be infeasible to have an RSA incidents when the wreckage more than 1,000 ft from the runway final location is beyond 1,000 ft centerline; the gain in safety is not from runway centerline. significant. make queries about the data stored in the database; and produce tions that experience the factor benignly, singly, and in combi- reports summarizing selected contents. Figure 9 illustrates the nation can be calculated; risk ratios can be generated; and the database organization. importance of risk factors can be quantified. This assessment The database includes for each individual event or opera- may allow the prioritization of resource allocation for safety tion the reporting agency, the characteristics of the aircraft improvement. involved, the runway and environmental conditions, event The NOD from the research reported in ACRP Report 3: classification (accident or incident), and other relevant infor- Analysis of Aircraft Overruns and Undershoots for Runway Safety mation such as consequences (fatalities, injuries, and damage) Areas was used in this study (Hall et al., 2008). The database and causal or contributing factors. A unique identifier was was complemented with data for general aviation (GA) air- assigned to each event. craft with a maximum takeoff weight (MTOW) of less than 12,500 lb and greater than 6,000 lb. These data are a large and representative sample of disaggregated U.S. NOD covering a Normal Operations Data (NOD) range of risk factors associated with runway veer-offs, allow- Another key approach in this study was the use of normal ing their criticality to be quantified. The data on U.S. incidents operations (i.e., non-accident/incident flight) data for prob- and accidents were used as a sample to develop the frequency ability modeling of runway veer-offs. In the absence of infor- models for runway veer-offs only. A sample of the NOD data mation on risk exposure, even though the occurrence of a is available in Appendix E. factor (e.g., contaminated runway) could be identified as a Incorporating this risk exposure information into the contributor to many accidents, it is impossible to know how accident frequency model enhances its predictive power and critical the factor was since other flights may have experienced provides the basis for formulating more risk-sensitive and the factor without incidents. With NOD, the number of opera- responsive RSA assessments. Accident frequency models no