Abstract

The relationship between a noise exposure level of a supersonic flight event and the probability of an individual being highly annoyed by the event is quantified by a dose-response curve. Dose-response curve estimation is based on logistic regression modeling, and it is subject to bias when the dose is measured with error. In this paper, an evaluation study is conducted to assess the impact of measurement error on dose-response curve estimation. For this, hierarchical Bayes models using different specifications are fit to data collected by the National Aeronautics and Space Administration (NASA) in 2018, as part of a risk-reduction study of supersonic flights affecting Galveston, Texas. It is observed that the estimated dose-response curve appears sensitive to uncertainty in dose measurements, being subject to attenuation bias. As part of their development of the technology for low-boom supersonic flight, NASA is planning to conduct a set of supersonic aircraft annoyance surveys in select communities in the United States, to measure public perception of the reduced sonic boom. Unavoidable amounts of estimation error are expected in these upcoming noise measurements as well. Hence, the development of an estimation approach that accounts for such error when estimating the dose-response curve is imperative.