National Academies Press: OpenBook

Risk Assessment Method to Support Modification of Airfield Separation Standards (2011)

Chapter: Taxiway and Taxilane Deviation Modeling

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Suggested Citation:"Taxiway and Taxilane Deviation Modeling." National Academies of Sciences, Engineering, and Medicine. 2011. Risk Assessment Method to Support Modification of Airfield Separation Standards. Washington, DC: The National Academies Press. doi: 10.17226/14501.
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Page 25
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Suggested Citation:"Taxiway and Taxilane Deviation Modeling." National Academies of Sciences, Engineering, and Medicine. 2011. Risk Assessment Method to Support Modification of Airfield Separation Standards. Washington, DC: The National Academies Press. doi: 10.17226/14501.
×
Page 26
Page 27
Suggested Citation:"Taxiway and Taxilane Deviation Modeling." National Academies of Sciences, Engineering, and Medicine. 2011. Risk Assessment Method to Support Modification of Airfield Separation Standards. Washington, DC: The National Academies Press. doi: 10.17226/14501.
×
Page 27
Page 28
Suggested Citation:"Taxiway and Taxilane Deviation Modeling." National Academies of Sciences, Engineering, and Medicine. 2011. Risk Assessment Method to Support Modification of Airfield Separation Standards. Washington, DC: The National Academies Press. doi: 10.17226/14501.
×
Page 28
Page 29
Suggested Citation:"Taxiway and Taxilane Deviation Modeling." National Academies of Sciences, Engineering, and Medicine. 2011. Risk Assessment Method to Support Modification of Airfield Separation Standards. Washington, DC: The National Academies Press. doi: 10.17226/14501.
×
Page 29

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25 Variable LDVO TOVO1 Variable LDVO TOVO1 Adjusted Constant 3.088 15.612 User Class G 1.682 2.094 Aircraft Class A/B 0.770 0.852 Aircraft Class D/E/F 0.252 0.091 Visibility less than 2 SM2 2.143 2.042 Visibility from 2 to 4 SM 3 0.808 Visibility from 4 to 8 SM 3 1.500 Xwind from 5 to 12 kt 0.653 0.102 Xwind from 2 to 5 kt 0.091 3 Xwind more than 12 kt 2.192 0.706 Tailwind from 5 to 12 kt 0.066 3 Tailwind more than 12 kt 0.98 3 Temp less than 5°C 0.558 0.988 Temp from 5 to 15°C 0.453 0.420 Temp more than 25°C 0.291 0.921 Icing Conditions 2.67 3 Rain 0.126 1.541 Snow 0.548 0.963 Frozen Precipitation 0.103 3 Gusts 0.036 3 Fog 1.740 3 Turboprop 2.517 1.522 Foreign Origin/Destination 0.334 0.236 Hub/Non-Hub Airport 3 0.692 Log Criticality Factor 4.318 1.707 Night Conditions 1.360 3 1LDVO = landing veer-off; TOVO = takeoff veer-off. 2 SM = statute miles. 3 Blank cells indicate that there are no coefficients associated with these parameters. Where Equipment Class Ref: C Large jet of MTOW 41k-255k lb (B737, A320 etc.) Heavy Acft AB Heavy jets of MTOW 255k lb+ Commuter Acft D Large commuter of MTOW 41k-255k lb (small RJs, ATR42 etc.) Medium Acft E Medium aircraft of MTOW 12.5k-41k lb (biz jets, Embraer 120 Learjet 35 etc.) Small Acft F Small aircraft of MTOW 12.5k or less (small, single or twin engine Beech90, Cessna Caravan etc.) User Class Ref: C = Commercial or F = Cargo or T/C = Taxi/Commuter User Class G G = GA Turboprop Turboprop engine(yes/no) – Ref: Turbojet Ceiling Height feet Visibility statute miles Crosswind knots Tailwind knots Gusts Yes/No – Ref: No Icing Conditions Yes/No – Ref: No Snow Yes/No – Ref: No Rain Yes/No – Ref: No Frozen Precipitation Yes/No – Ref: No Fog Yes/No – Ref: No Air Temperature deg C Turboprop Aircraft Yes/No – Ref: No NonhubApt Yes/No – – Ref: Yes for hub airport Log Criticality Factor If Log(CF) > 0, available runway distance is smaller than required distance Night Conditions Night, Dawn or Dusk – Ref: Daylight Notes: Ref: indicates the reference category against which the odds ratios should be interpreted. Non-hub airport: airport having less than 0.05% of annual passenger boardings Table 7. Independent variables for veer-off frequency models. for each model. Relatively low R2 values are the norm in logis- tic regression (Ash and Schwartz, 1999), and they should not be compared with the R2 of linear regressions (Hosmer and Lemeshow, 2000). A better parameter to assess the predictive capability of a logistic model is the C-value. This parameter represents the area under the sensitivity/specificity curve for Model R2 C LDVO 0.32 0.88 TOVO 0.14 0.82 Table 8. Summary statistics for veer-off frequency models.

the model, which is known as the receiver operating charac- teristic (ROC) curve. Sensitivity and specificity are statistical measures of the performance of a binary classification test. Sensitivity mea- sures the proportion of true positives that are correctly iden- tified as such (the percentage of accidents and incidents that are identified when using the model). Specificity measures the proportion of true negatives that are correctly identified (the percentage of normal operations that the model can identify as non-incident). These two measures are closely related to the concepts of Type I and Type II errors. A theoretical, opti- mal prediction can achieve 100-percent sensitivity (i.e., pre- dict all incidents) and 100-percent specificity (i.e., not predict a normal operation as an incident). To assess how successful the models are in classifying flights correctly as “incident” or “normal,” and to find the appropri- ate cut-off points for the logistic regression model, the ROC curves were defined for each model to calculate the C-value as shown in Table 8. The values achieved for the veer-off model are considered very good, with the area under the curve rep- resenting a C-value higher than 80 percent. The frequency models developed under this study will require the use of historical information on operations and weather for the specific airport. The necessary information on operations includes the time of the flight, runway used, type of aircraft, type of flight, and whether the operation was an arrival or departure. In addition, it is necessary to collect the weather information for the same period that operational data are available, usually for 1 year. Weather information can be acquired directly from the National Oceanic and Atmospheric Administration (NOAA) database for the weather station located at the airport. How- ever, the information on operations, particularly for non- towered airports, may be harder to obtain, particularly the identification of the runway used. For towered airports oper- ational data can be requested from the FAA. Another challenge is running the analysis because computations can be made only with the help of a computer and specific software that incorpo- rates these models. To facilitate the analysis, a series of plots were developed based on average veer-off incident rates for the United States. The rates are presented in Table 9 and were combined with the location models to build the risk plots presented in Appendix A. The average incident rates are based on the number of accidents and incidents, and the total traffic of relevant operations from 1982 to 2009. From Table 9, one can see that landing veer-offs are approximately four times more likely to occur than takeoff veer-offs. Event Location The farthest location of the veer-off path from the runway edge was used to develop the location models. The probabil- ity of this distance during an incident is not equal for all loca- tions measured from the runway centerline or runway edge. The probability of veer-off with lateral deviation in the prox- imity of the runway edge is higher than at larger distances from that boundary. This dependence is represented by the accident location model, which is the second main element of the risk assessment approach. The accident location models are based on historical accident and incident data for aircraft veer-offs. Worldwide data on accidents and incidents were used to develop the location models. The model structure is an expo- nential decay function similar to that used in the research reported in ACRP Report 3 (Hall et al., 2008). Based on the accident/incident location data, two cumulative probabil- ity distribution models were developed. With the functions obtained, the fraction of accidents involving locations exceed- ing a given distance from the runway edge can be estimated. When the probability estimated with the location model is multiplied by the frequency of accident occurrence, it is possible to quantify the overall frequency of incidents involving locations exceeding a given distance from the runway edge. Figure 19 shows the runway edge origin location used to rep- resent veer-off incidents. The reference location of the aircraft is its nose wheel. The y-axis origin is the edge of the runway, not necessarily the edge of the paved area when the runway has shoulders. The model structure for the location models is the following: where P{Location > y} is the probability that the veer-off distance from the runway edge is greater than y, P Location y e bym>{ } = − 26 Type of Incident Number of Incidents Incident Rate per Operation Incident Rate in Operations per Incident LDVO 512 1.195E-06 837,000 TOVO 111 2.590E-07 3,861,000 Table 9. Average veer-off incident rates (1982–2009).

27 y Figure 19. Y origin for aircraft veer-offs. mbyeyLocationP }{ yDistance y from runway bordery1rwy border P{Loc > y1} Pr ob ab ilit y lo ca tio n Ex ce ed s y Figure 20. Typical model for aircraft veer-offs. Prob=exp((-.02568)*Y**(.803946)) R2=99.5% 0 200 400 600 800 1000 Distance Y from Runway Edge (ft) 0.0 0.2 0.4 0.6 0.8 1.0 Pr ob ab ilit y of S to pp in g Be yo nd Y Figure 21. Location model for landing veer-off. Type of Accident Type of Data Model R2 # of Points LDVO Y 803946.002568.0}{ yeydP 99.5% 126 TOVO Y 863461.001639.0}{ yeydP 94.2% 39 Table 10. Summary of veer-off location models. Prob=exp((-.01639)*Y**(.863461)) R2=94.2% 0 200 400 600 800 1000 Distance Y from Runway Edge (ft) 0.0 0.2 0.4 0.6 0.8 1.0 Pr ob ab ilit y of S to pp in g Be yo nd Y Figure 22. Location model for takeoff veer-off. y is a given location or distance from the runway edge, and b and m are regression coefficients. A typical transverse location distribution is presented in Figure 20. The actual model parameters are presented in Table 10 and illustrated in Figures 21 and 22.

28 MOS Case Studies One of the goals of this study was to evaluate the types of jus- tification used by airports when submitting MOS requests to the FAA, specifically when the non-compliance issue was related to airfield separation. A second objective was to test and validate the methodology applied to those cases to evaluate how the new methodology might help support such requests. To achieve these objectives, a survey was conducted of airports with MOS for airfield separations that have been approved by the FAA. MOS Survey The Airports Division of the FAA regional offices were contacted to obtain a list of airports that have received MOS approvals related to airfield separations. Fifty-nine cases were identified for which some information was available. Out of those cases approved by the FAA, 20 were selected to cover various regions and the spectrum of National Plan of Integrated Airport Systems (NPIAS) categories. The airports were characterized according to geographic region, aircraft operations, fleet mix, airport reference code, and NPIAS classification. The list of MOS cases selected for this study is shown in Table 11. More details about each case are pre- sented in Appendix H. Methodology Applied to MOS Cases To validate the methodology and the models developed in this study, the analysis procedures presented in Appendix A were applied to each of the 20 selected MOS cases. A summary of results is presented in Table 12. For each case, the following information is provided: • The non-standard situation and the compliance standards; • The analysis of the separation using the risk analysis methodology; • Comparison of estimated and historical incident/accident rates, when available; and • Major conclusions on risk level and acceptance. Additional details for each analysis are presented in Appendix H. The following describes the information con- tained in each column of Table 12: • Airp.—FAA code of the airport. • ADG—Aircraft Design Group that defines the separation. • Type of MOS—Type of procedure used in the analysis. • Risk Level—Probability for risk severity considered. Cata- strophic consequences were considered for every analysis involving runway separation, and major consequences were considered for the remaining cases. The definitions for severity and likelihood classifications are based on the FAA risk matrix (FAA, 2010) • Expected # Yrs—Number of years that an accident is expected to occur. • Risk < 1.0E-7—Yes, if risk is lower than 1 in 10 million operations. • Risk < 1.0E-9—Yes, if risk is lower than 1 in 1 billion operations. • Expected Severity—Worst credible consequence expected for the accident, based on categories defined in the FAA risk matrix (FAA, 2010). • FAA Risk Classification—Level of risk according to clas- sification based on FAA risk matrix (FAA, 2010). • Acceptable—Yes, if level of risk is medium or low; how- ever, for medium risk, measures to mitigate and control may be necessary. Figures 23 and 24 present a summary of the most frequent justifications and restrictions, respectively, when submitting MOS requests related to airfield separations. As observed in Table 12, the methodology would help support the analysis of MOS. With the exception of one case, C H A P T E R 5 Validating the Methodology

29 Case # Airport ID NPIAS 1 MOS Type 2 FAA Region 3 1 Philadelphia, PA PHL LH TW Y/TWY AEA 2 Anchorage, AK ANC MH TW Y /OBJ AAL 3 Addison, TX ADS RL RWY/TWY ASW 4 Bridgeport, CT BDR GA RWY/TWY ANE 5 Accomack, VA MFV GA RWY/OBJ AEA 6 Lincoln Park, NJ N07 RL TLN /OBJ AEA 7 New York JFK, NY JFK LH TW Y/TWY AEA 8 Newark, NJ EW R LH TW Y/TWY AEA 9 Minneapolis, MN MSP LH TW Y/TWY AGL 10 Chicago, IL ORD LH TW Y/TWY AGL 11 Chicago, IL ORD LH TWY/OBJ AGL 12 Barnstable, MA HYA NH RWY/TWY ANE 13 Laconia, NH LCI GA RWY/TWY ANE 14 Seattle-Tacoma, WA SEA LH RWY/TWY ANM 15 Seattle-Tacoma, WA SEA LH TW Y/TWY ANM 16 Aspen, CO ASE NH RWY/OBJ ANM 17 Nantucket, MA ACK NH TW Y/TWY ANE 18 New Castle, DE ILG GA TW Y/TWY AEA 19 Leesburg, VA JYO RL RWY/OBJ AEA 20 Taunton, MA TAN GA RWY/TWY ANE 1National Plan of Integrated Airport Systems (NPIAS) Classification • LH: Primary, Large Hub • MH: Primary, Medium Hub • SH: Primary, Small Hub • NH: Primary, Non-Hub • RL: Reliever • GA: General Aviation 2 MOS Type • RWY/TWY: runway to taxiway separation • TWY/TWY: taxiway to taxiway separation • TWY/TLN: taxiway to taxilane separation • TWY/OBJ: taxiway to object separation • TLN/TLN: taxilane to taxilane separation • TLN/OBJ: taxilane to object separation • RWY/OBJ: runway to object separation 3 FAA Regions with Relevant MOS Cases • AEA: Eastern • AAL: Alaska • ASW: Southwest • ANE: New England • AGL: Great Lakes • ANM: Northwest Mountain Table 11. Airports included in the MOS survey. the analysis results provided an insight into the quantitative risk associated with each case. Moreover, the results helped identify the level of risk and consequently the need to include additional measures to control risk. The cases with low levels of risk are satisfactory without ad- ditional measures, except to keep the conspicuity of taxiway and taxilane centerlines under any weather conditions. Based on these results, some important conclusions can be drawn: • The suggested level of risk criterion for taxiway/taxilane/ object separations is one accident in 10 million move- ments. This criterion was met for each of the case studies that does not involve runway separations. This is also the maximum risk for the range defined in the FAA risk matrix for accidents of major severity, the worst credible consequence for taxiway/taxilane collisions (FAA, 2010). • The same criterion is suggested to evaluate runway separa- tions because it was the basis for defining design standards using the FAA/ICAO CRM. It should be noted that the most credible consequence for aircraft veering off a runway is cat- astrophic. As such, the acceptable risk level is one accident in 1 billion operations, and according to the FAA, the risk is classified as medium. • The FAA also added criteria specific for airports, and the maximum risk for accidents of major consequences is one in every 2.5 million departures or 4 × 10−7; however, based on the results of the analysis using the risk methodology, a more conservative level may be used, and a level of one accident in 10 million operations for cases involving taxiway/taxilane/ object is recommended.

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TRB’s Airport Cooperative Research Program (ACRP) Report 51: Risk Assessment Method to Support Modification of Airfield Separation Standards is intended to be used to support requests for modification of standards in those circumstances where the design criteria for separations between taxiways/taxilanes and other taxiways/taxilanes and fixed or movable objects as well as separations between taxiways and runways cannot be met.

The following appendices, included in the pdf and print version of the report, will be helpful in understanding the methodology.

  • Appendix A: Risk Assessment Methodology presents a methodology for five different types of circumstances: taxiway/taxilane to taxiway, taxiway to object, taxilane to taxilane, taxilane to an object, and runway to taxiway/taxilane or object;
  • Appendix F: Aircraft Database Summary presents a summary of aircraft characteristics by model; and
  • Appendix H: Analysis of MOS Cases summarizes information collected in the modification of standards survey and presents results of application of the methodology described in Appendix A to each modification of standards case.

Other report appendices, which are available online only, provide detail and information on the development of the methodology.

In addition, the project developed a

PowerPoint presentation

that may be useful for introducing and explaining the methodology to stakeholders.

In July 2021, an errata was posted for this publication: In Table 7 on page 25, the LDVO coefficient was changed from -3.088 to -13.088. The online version of the report has been corrected.

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