Predictions of Sleep Disturbance for Different Nighttime Airport Operation Strategies Using a New Markov State Transition Sleep Model

Invited paper

Sarah McGuire

Division of Sleep and Chronobiology, Department of Psychiatry

Tuesday 2 june, 2015, 14:40 - 15:00

0.4 Brussels (189)

To balance benefits and costs of potential airport operation changes such as noise curfews, changes in flight schedules, or flight paths, models are needed which can predict the time varying nature of the effects of aircraft noise on sleep. While a Markov transition model has been developed which predicts the transitions between 6 sleep stages throughout the night (Wake, S1, S2, S3, S4, and REM), it has two limitations. The Markov model was developed based on data from a laboratory study in which a greater probability of aircraft noise- induced awakenings was found compared to field studies. In addition, the model predicts the same probability of awakening for all aircraft events, regardless of the noise level. To overcome these two limitations, a new Markov transition model was developed using data from a total of 483 nights from 63 subjects who participated in a polysomnographic field study that was conducted around Cologne-Bonn Airport. Similar to the previous Markov model, transition probabilities between sleep stages were calculated using 1st-order autoregressive multinomial logistic regression models. However, in addition to elapsed sleep time, the maximum noise level has been added to the model as an explanatory variable. This new Markov model was used to predict the number of awakenings and the time spent in each sleep stage for different nighttime noise mitigation strategies. The development of the model and differences and similarities in the predictions for the different operation scenarios will be discussed.

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