Analysis of Aircraft Overruns and Undershoots for Runway Safety Areas (2008) / Chapter Skim
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; • Wildlife Hazards (WH) ; • Weather Conditions (W)
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Visual illusion was a significant factor only for landing undershoots. The presence of rain, gusting, crosswind, and low ceiling conditions were most predominant for these accidents when compared to incidents.
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Logistic regression, discriminant analysis, and probit analysis were evaluated for modeling the probability of aircraft overrun and undershoot events. Discriminant analysis was not used because it involves numerous assumptions, including requirements of the independent variables to be normally distributed, linearly related, and to have equal variance within each group (Tabachnick and Fidell, 1996)
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Every risk factor available in both Accident/Incident database and NOD were used to build each model. Table 7 shows the final parameters retained by the backward stepwise logistic regression as relevant independent variables for each of the frequency models.
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. Frequency Distribution of Anomalies for Landing Undershoots 12% 0% 59% 29% 67% 2% 2% 44% 38% 24% 31% 65% 0% 10% 20% 30% 40% 50% 60% 70% 80% Aircraft System Fault Wildlife Hazard Weather Condition Human Error Runway Conditions Approach/Takeoff Procedures % A CC /IN C w ith A no m al y ACC INC Figure 12.
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P{Accident_Occurrence} = s the probability (0-100%) of an accident type occurring given certain operational conditions; Xi = independent variables (e.g.
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Logistic regression is relatively free from assumptions, especially compared to ordinary least squares regression. However, a number of assumptions still apply.
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A test for multicollinearity is required for multivariate logistic regression. Collinearity among the predictor variables was assessed by conducting linear regression analyses to obtain the relevant tolerance and Variance Inflation Factor (VIF)
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Summary results for under-reported incidents. Variable LDOR LDUS TOOR Aircraft Weight/Size X X X Aircraft user class X X Ceiling X X X Visibility X X X Fog X X Crosswind X X Gusts Icing Conditions X X X Snow X X X Rain X Temperature X X X Electrical Storm X Turboprop/Jet X Foreign Origin/Destination X X Hub/Non-hub airport X Table 7.
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Visibility<2SM < 2 SM Visibility2-4SM 2-4 SM Visibility4-6SM 4-6 SM Visibility6-8SM 6-8 SM Crosswind Ref:< 2 knots Xwind2-5knts 2-5 knots Xwind5-12knts 5-12 knots Xwind>12knts >12 ElectStorm Electrical storm (yes/no) – Ref: no IcingConditions Icing conditions (yes/no)
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Appendix M provides the results for multivariate logistic regression analysis used to obtain the model coefficients described earlier. Accident Location Models Based on the accident/incident data for wreckage locations, three sets of complementary cumulative probability distribution (CCPD)
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LDOR location model using normalized distances. Normalized Lateral Distances Model for LDOR 0% 20% 40% 60% 80% 100% 0 500 1000 1500 2000 2500 Distance Y from Extended Runway Axis (ft)
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LDUS location model using raw (nonnormalized) distances.
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LDUS location model using normalized distances. Normalized Lateral Distances Model for LDUS 0% 20% 40% 60% 80% 100% 0 400200 600 800 1000 1200 1400 Distance Y from Extended Runway Axis (ft)
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TOOR location model using normalized distances.
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TOOR lateral location model using normalized distances. Type of Accident Type of Data Model Eq.# R2 # of Points Raw 955175.0003871.0}{ xexdP (12)
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Based on Equation 11 for transverse distance, the probability the aircraft axis is within this range can be calculated as follows: (24) where Psc = the probability of high consequences; b, m = regression coefficients for y-location model; Yc = the critical aircraft location, relative to the obstacle, closest to the extended runway axis; and P e e sc byc m by f m = − − − 2 Type of Event Sample Size Spearman R Kendall Tau LDOR 224 0.62 0.49 LDUS 68 0.30 0.23 TOOR 67 0.55 0.44 All 359 0.56 0.44 Table 13.
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The probability and location models should provide a quantitative assessment based on operating conditions for a specific airplane landing or takeoff at a specific runway. The consequences model should provide a qualitative assessment of the severity of an accident, based on the location model and the existing runway characteristics, to include dimensions of existing RSA, airplane weight, type, location and size of obstacles, and the topography of the surrounding terrain.
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The total consequences were estimated in terms of total direct costs for injuries, aircraft damage, and accident investigation. Figure 33 depicts the average cost by type of accident and by severity.
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