Search NASA⌕ Search
Home/Archive/NASA NTRS

NASA NTRS · 20230005305

Dose-Response Data Considerations for the NASA Quesst Community Test Campaign

Aaron B. Vaughn·William J. Doebler
View original source ↗Export metadata ↓

Abstract

Key outcomes for NASA's Quesst mission are noise dose and perceptual response data to inform regulators on their decisions regarding noise certification standards for the future of overland commercial supersonic flight. Dose-response curves are commonly utilized in community noise studies to describe the annoyance of a community to a particular noise source. The X-59 aircraft utilizes shaped-boom technology to demonstrate low noise supersonic flight. For X-59 community studies, the sound level from X-59 overflights constitutes the dose, while the response is an annoyance rating selected from a verbal scale, e.g., “slightly annoyed” and “very annoyed.” Dose-response data will be collected from individual flyovers (single event dose) and an overall response to the accumulation of single events at the end of the day (cumulative dose). There are quantifiable sources of error in the noise dose due to uncertainty in microphone measurements of the sonic thumps and uncertainty in predicted noise levels at survey participant locations. Assessing and accounting for error in the noise dose is essential to obtain an accurate dose-response model. There is also a potential for error in the perceptual response. This error is due to the ability of participants to provide their response in a timely manner and participant fatigue after responding to up to one hundred surveys over the course of a month. This talk outlines various challenges in estimating noise dose and perceptual response and the methods considered in preparation for X-59 community tests.

Explore related subjects

dose-responseX-59Quesst missioncommunity test

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Aaron B. Vaughn, William J. Doebler. Dose-Response Data Considerations for the NASA Quesst Community Test Campaign. https://ntrs.nasa.gov/citations/20230005305

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

NASA NTRS2023

Multilevel Logistic Regression with Random Slope for Community Annoyance Survey Data

This paper documents recent dose-response modeling work at NASA in anticipation of follow-on work by a contactor. Specifically, this paper compares the results of a Bayesian MLR model with a fixed slope to one with a random slope using WSPR and QSF18 data. Previously reported dose-response modeling efforts of WSPR and QSF18 data have used a MLR model with a fixed slope term. A random slope may more accurately depict the dose-response relationship of individuals in the efforts to produce a population summary dose-response curve. Results of a fixed versus random slope model with WSPR and QSF18 data indicate minimal difference between the modeling methods. The simpler fixed slope model is preferable for these data, but these results do not preclude consideration of a random slope term in modeling efforts of future X-59 community test data.

dose-response↗
NASA NTRS2022

Comparison of Two Statistical Models for Low Boom Dose-response Relationships with Correlated Responses

This study compares two statistical models to construct summary dose-response curves for low boom community noise surveys. Data from two NASA field surveys are used that consist of multiple responses per survey participant. These data require an approach that accounts for the correlation among repeated annoyance observations from the same participant. A multilevel model accounts for the correlation by allowing estimated parameters to vary with each survey participant. On the other hand, a population average model utilizes generalized estimating equations and accounts for the correlation via a userspecified within-subject correlation structure. A visual comparison of the dose-response curves for these two methods reveals similar results. When comparing specific points along the summary curves, the multilevel model yields more precise confidence bounds than the population average model. The similarity between the summary curves derived from each model lends validity to both approaches for approximating a population representative summary curve, though modeling assumptions may lend favor to the multilevel logistic modeling approach over the population average model.

dose-response↗