Quantification of Error for Rapid Fleet-Level Noise Computation Model Assumptions

Abstract

A rapid fleet-level noise computation model is assessed to quantify errors associated with its simplifying assumptions. A method for rapidly evaluating population exposure to significant noise by mapping census block counts to square Thiessen polygons corresponding to the dimensions of the airport noise grids is also introduced. Runway utilization factors and generic-vehicle noise grids were also introduced to the rapid model to quantify errors from additional simplifying assumptions. These assumptions are convenient for rapidly accounting for runway assignments when generating flight schedules for future scenarios. The errors introduced by these assumptions are quantified by comparing against an industry-standard tool. The results show that the airports with the largest residuals for contour areas under simplifying assumptions do not necessarily correspond to the airports with the largest population residuals. Only a handful of the airports account for a large percentage of the aggregate population-exposure residuals, and these mostly correspond to busier airports in metroplexes with high population density. The results show that the baseline assumptions for the model lean toward conservative overestimates of population exposure. The accuracy of the model can be improved by modeling diverging ground tracks at a small handful of major airports, but for most airports, the standard atmosphere and straight ground track assumptions are sufficient for scenario analysis.