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 32
32 CHAPTER 6 Model Validation The improved risk models were validated by comparing less than 5600 lb and other movements non-relevant to this the results of the analysis for a sample of airports to their as- study. The volume and the number of accidents and incidents sociated historical accident rates. The eight airports listed in were used to estimate the frequency rates and accident rates Table 7 were selected using random stratified sampling tech- for each airport and type of event. niques to run the analysis with the new models and analysis The analysis software proved to work well for each case study. software; the results are compared to the actual rate of acci- There were no bugs identified during the software runs, and dents at the selected airports. None of the selected airports the results looked rational and within expected ranges for the were part of the NOD used to develop the risk models. individual airports. The analysis runs with the eight airports also served to test Table 8 contains the events for each airport occurring dur- the software. To run the risk analysis, one year of historical ing the analysis period. Figure 40 summarizes the total num- operations data were obtained for each airport. Data for air- ber of accidents and incidents occurring at the eight airports ports were collected and consolidated. Operations data were since 1981. The majority of the cases were landing veer-offs, retrieved from the FAA Operations & Performance Data and, for most types of events, the number of incidents was and Aeronautical Information Management Laboratory. The larger than the number of accidents. One notable exception weather data were obtained from the National Oceanic and was the case for TOORs. It is true that the number of cases is Atmospheric Administration (NOAA) database for the mete- quite small for a sample of eight airports; however it is notable orological stations serving each airport. that there were fewer TOOR incidents compared to accidents. Historical accident and incident information for the airport Approximately 50% of TOOR in the accident/incident data- was obtained from the NTSB, AIDS, and ASRS databases. base developed for this study were incidents, and it may be an Although the analysis was conducted to obtain risk assess- indication of higher severity for TOOR. When comparing the ment estimates, information on frequency calculated in the location models for TOOR and LDOR, the percentage of air- analysis also was used to compare expected and actual fre- craft stopping at any given distance is larger during the takeoff, quency rates for each type of incident. Similarly, actual and compared to landing overruns. estimated accident rates were compared to evaluate the need A summary of analysis results is presented in Table 9. More to make adjustments to the models. Table 8 presents the rel- details on the analysis and additional results are presented in evant accidents and incidents identified for the eight airports Appendix H. It is important to note that the RSA configurations selected. RSA's and obstacles were characterized using satel- used for the analysis at Yeager Airport were representative of lite pictures from Google Earth, and RSA files were created conditions prior to the recent improvements that included the for each runway. extension of RSA's and implementing EMAS. The main reason Relevant traffic volume information from 1981 to 2009 was for using these data for Yeager is that the plan was to compare estimated from information available in the FAA Air Traffic the analysis results with historical accident/incident rates. As Activity Data System (ATADS). Part of the annual air traffic expected, risk for Yeager was the highest because its RSAs volume was extrapolated to estimate the total volume for the before the recent improvements were considerably smaller sample period. An average annual growth rate of 5% was than current FAA standards. assumed for air traffic in the period between 1981 and 1999 For simplicity, all analyses were conducted using the av- when air traffic information was not available online. The erage annual operations during the past 10 years. The ex- volumes were adjusted to remove operations of aircraft with pected number of years between critical events is based
OCR for page 32
33 Table 7. List of airports for model/software validation. State Airport Name Location ID City Hub FL Miami International MIA Miami L AK Ted Stevens Anchorage ANC Anchorage M International MO Lambert-St Louis International STL St Louis M WA Spokane International GEG Spokane S SD Joe Foss Field FSD Sioux Falls N WV Yeager CRW Charleston N AZ Deer Valley International DVT Phoenix GA FL Ft Lauderdale Executive FXE Ft Lauderdale GA Table 8. Accidents and incidents at selected airports. Aircraft Airport Date Country City/State Source Event Type Event Class ICAO Code Code 07/01/1981 US Saint Louis, MO NTSB LDVO Incident DC6 STL 07/24/1981 US Charleston NTSB LDUS Accident BE60 CRW 10/15/1981 US Saint Louis, MO AIDS LDVO Incident DC6 STL 2/24/1983 US Anchorage, AK AIDS LDVO Incident LJ24 ANC 10/26/1983 US Saint Louis, MO NTSB LDUS Accident CV3 STL 12/23/1983 US Anchorage, AK NTSB TOOR Accident DC10 ANC 9/28/1987 US Saint Louis, MO AIDS LDUS Incident MD80 STL 12/26/1987 US Fort Lauderdale, FL AIDS LDVO Incident AC11 FXE 10/14/1988 US Anchorage, AK AIDS LDVO Incident YS11 ANC 10/23/1989 US Anchorage, AK MITRE LDOR Incident B741 ANC 1/6/1990 US Miami, FL 4-01NTSB TOOR Accident L29A MIA 02/17/1991 US Spokane, WA NTSB LDVO Accident MU2B GEG 03/11/1993 US Saint Louis, MO NTSB LDVO Accident DC93 STL 8/28/1993 US Fort Lauderdale, FL AIDS LDVO Incident LJ23 FXE 08/29/1993 US Charleston NTSB LDOR Accident MU2B CRW 7/27/1994 US Sioux Falls, SD AIDS LDVO Incident T18 FSD 11/29/1994 US Spokane, WA AIDS TOVO Incident B731 GEG 06/23/1995 US Miami, FL NTSB LDVO Accident C402 MIA 11/19/1995 US Anchorage, AK AIDS LDUS Incident C441 ANC 12/19/1995 US Saint Louis, MO AIDS LDVO Incident DC91 STL 9/17/1996 US Miami, FL AIDS TOVO Incident BE18 MIA 11/15/1996 US Sioux Falls, SD MITRE LDOR Incident DC91 FSD 8/7/1997 US Miami, FL 4-01NTSB TOOR Accident DC85 MIA 2/19/1999 US Miami, FL 4-01ASRS LDUS Incident A30B MIA 10/15/2000 US Anchorage, AK NTSB TOOR Incident B741 ANC 10/16/2000 US Saint Louis, MO AIDS LDVO Incident MD80 STL 10/20/2000 US Saint Louis, MO ASRS LDOR Incident MD82 STL 04/07/2001 US Anchorage, AK AIDS TOVO Incident B190 ANC 01/01/2002 US Miami, FL 4-01NTSB LDOR Incident MD83 MIA 06/15/2002 US Fort Lauderdale, FL AIDS LDVO Incident SW3 FXE 12/01/2002 US Spokane, WA AIDS LDOR Incident DH8A GEG 12/20/2002 US Spokane, WA 4-01ASRS LDOR Incident DH8A GEG 8/16/1999 US Fort Lauderdale, FL MITRE LDVO Accident CL60 FXE 4/17/2003 US Fort Lauderdale, FL AIDS LDVO Incident SBR1 FXE 6/12/2003 US Fort Lauderdale, FL AIDS TOOR Incident LJ24 FXE 8/9/2003 US Fort Lauderdale, FL AIDS LDVO Incident SBR1 FXE 2/20/2004 US Fort Lauderdale, FL 4-01NTSB LDOR Accident LJ25 FXE 3/31/2004 US Fort Lauderdale, FL NTSB LDVO Accident C402 FXE 7/19/2004 US Fort Lauderdale, FL 4-01NTSB LDOR Accident LJ55 FXE 09/08/2004 US Charleston NTSB TOOR Accident C402 CRW 12/1/2005 US Sioux Falls, SD AIDS LDVO Incident SW4 FSD 6/6/2006 US Fort Lauderdale, FL AIDS TOVO Incident SW3 FXE 2/4/2007 US Miami, FL NTSB LDVO Incident DC87 MIA 11/1/2007 US Fort Lauderdale, FL AIDS LDOR Incident GLF2 FXE 1/27/2008 US Spokane, WA AIDS LDOR Incident B731 GEG 5/23/2008 US Fort Lauderdale, FL AIDS LDVO Incident SBR1 FXE 01/19/2010 US Charleston NTSB TOOR Accident CRJ2 CRW