The number-of-events as a predictor variable in aircraft noise annoyance models.
详细信息
文摘
Aircraft noise may have a number of direct adverse effects on the communities surrounding airports, including annoyance. The annoyance reactions of individuals and communities to aircraft noise are predicted with annoyance models, which are normally functions of predictor variables that describe the noise exposure. The number of aircraft events that a person is exposed to (the number-of-events), has been hypothesized as a significant contributor to annoyance. However, most models of annoyance to aircraft noise are functions only of the average sound energy of the total noise exposure. The purpose of this research is to quantify the relative effects of sound level and number-of-events in historical noise survey data sets and to develop a survey simulation tool to help in the design of future surveys so that the collected data will be sufficient to compare the performance of proposed annoyance models. The models considered here are DNL and those that are a function of sound level and number-of-events. Seven historical data sets were collected and analyzed individually and in combination. Multiple linear regression models were estimated using the annoyance, sound level, and number-of-events variables in the data sets. The contributions of sound level and number-of-events to the prediction of annoyance were compared. Most regression models could not be distinguished from an equal-energy annoyance model. A general-purpose tool was developed to simulate annoyance surveys around airports. Monte Carlo simulations were performed to evaluate the effectiveness of survey sampling approaches. Annoyance surveys were simulated around three airports in the United States. The use of stratification, as opposed to simple random sampling, resulted in more robust estimation of annoyance models.