Evaluating Methods for Counting Aircraft Operations at Non-Towered Airports (2015) / Chapter Skim
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From page 59... ...
A P P E N D I X A Appendix A provides information for those readers of the report that desire more details on the work performed to support the findings in Chapter 3, Methods for Estimating Annual Airport Operations, on OPBA and extrapolation from a sample count. OPBA Method to Estimate Annual Airport Operations The objective of this research task was to determine if there was a consistent number(s)
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A-2 to determine the effect that each of the variables in Table A-1 has on AvgOPBA in the STAD. The analysis includes: A
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A-3 Scaled) , Yearly Hours of Control Tower Operations (CTHrs)
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A-4 p value=0.000 Predictor Coef SECoef t p Constant 346.5 200.7 1.73 0.086 AvBA -1.2134 0.2559 -4.74 0.000 NFS 23.99 14.02 1.71 0.089 FSY/N 77.5 105.1 0.74 0.462 Pop Scaled 0.6027 0.1335 4.51 0.000 CTHrs 0.05338 0.03076 1.74 0.084 C -179.5 112.5 -1.59 0.112 EN -184.8 132.5 -1.39 0.165 NE -188.3 112.3 -1.68 0.095 NW -168.1 153.5 -1.09 0.275 S 43.7 103.4 0.42 0.673 SE -27.3 103.2 -0.26 0.791 SW 129.4 120.9 1.07 0.286 CM 85.5 100.5 0.85 0.396 RL 201.09 68.79 2.92 0.004 Prepared by: Purdue University Table A-3. Supplemental statistics showing significance of each variable in the full model using AvgOPBA.
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A-5 dots tracing over the blue line; and the top right graph (Fitted Value vs. Residual)
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A-6 200010000-1000 99.9 99 90 50 10 1 0.1 Residual Pe rc en t 2400180012006000 2000 1000 0 -1000 Fitted Value R es id ua l 2000160012008004000-400 80 60 40 20 0 Residual Fr eq ue nc y 200180160140120100806040201 2000 1000 0 -1000 Observation Order R es id ua l Normal Probability Plot Versus Fits Histogram Versus Order Residual Plots for AvgOPBA Prepared by: Purdue University Figure A-2. Residual plots for AvgOPBA for reduced model.
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A-7 The full model regression equation using log10AvgOPBA and log10AvgBA is as follows: log10AvgOPBA 3.95 0.681 log10AvgBA 0.000215 Pop Scaled 0.0246 NFS 0.0206 FS Y/N 0.000036 CTHrs 0.153 C 0.0921 EN 0.0716 NE 0.0421 NW 0.0704 S + 0.0079 SE 0.118 SW 0.0652 CM 0.0176 RL = − + + + + − − − − − + − − The regression is statistically significant at the 95% level (alpha equals 0.05)
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A-8 0.500.250.00-0.25-0.50 99.9 99 90 50 10 1 0.1 Residual Pe rc en t 3.23.02.82.62.4 0.50 0.25 0.00 -0.25 -0.50 Fitted Value R es id ua l 0.450.300.150.00-0.15-0.30-0.45 24 18 12 6 0 Residual Fr eq ue nc y 200180160140120100806040201 0.50 0.25 0.00 -0.25 -0.50 Observation Order R es id ua l Normal Probability Plot Versus Fits Histogram Versus Order Residual Plots for log10OPBA Prepared by: Purdue University Figure A-5. Residual plots of the transformed data.
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A-9 of annual operations, and therefore, it may not provide useful estimates in a practical application. If only approximately 50% of the variation of the AvgOPBA is explained by the variables in the equation (i.e., flight schools, population, climate, and airport category)
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A-10 log10AvgOPBA = 3.94 - 0.621 log10(10)
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A-11 able (in this case the OPS)
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A-12 statistical assumptions described above that must be met in order to use regression. The two assumptions are constant variance (right half of the residual plots in Figure A-9)
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A-13 dramatically better than the OPBA model (65.3% versus 27.5%) , but neither meet all the required statistical assumptions for valid regression.
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A-14 methods is typically done either by statistical extrapolation of sample operations counts or by extrapolation using monthly/ seasonal adjustment factors developed from towered airport operations data. The process and results of testing these two methods using data from small, towered airports are described in Chapter 3 of this report.
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A-15 from the STAD. Again, the random selection process was conducted separately for each airport.
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A-16 and each season has 13 weeks. To maintain seasonal representation and to get all 12 months into four seasons for that calendar year, the seasons were identified as winter (January– March)
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Table A-12. Estimates of annual operations using monthly/seasonal extrapolation and four sampling scenarios.
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A-18 B Two weeks in each season: For each of the 16 test airports, two weeks of OPSNET data for each season were collected.
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A-19 1 M on th Wi nte r 1 M on th Sp rin g, Su mm er, or Fa ll 2 W ee ks ea ch Se as on 1 W ee k ea ch Se as on 0.50 0.25 0.00 -0.25 -0.50 Pe rc en t D iff er en ce Boxplot of Percent Differences Using 4 Sampling Scenarios Prepared by: Purdue University Figure A-10. Box plot of sampling methods: 1 week each season; 2 weeks each season; 1 month spring, summer, or fall; and 1 month winter.
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A-20 winter (shown as Tukey Grouping A in Table A-15)
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