Yu Wolf Song, Anna S Reichherzer, Xinhe Yao, Gerbera Vledder, Britta Herbig, Michael Bellmann, Victor Norrefeldt, Peter Vink, Neil Mansfield
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引用次数: 0
Abstract
Background: A quantitative comfort model will aid in evaluating comfort levels of various target groups before the actual flight of an airplane. However, constructing the model is always a challenge due to the complexity of the phenomenon.
Objectives: In this paper, we present quantitative comfort models to predict the (dis)comfort of passengers flying with turboprops based on objective measures.
Methods: Ninety-seven participants took part in two experiments conducted during real flights, during which forty of them had environmental and personal factors recorded using (self-developed) measurement tools. The collected data were analyzed to model the relations between objective measures and subjective feelings.
Results: Two preliminary models based on gradient boosting regression were developed. The models were able to predict the changes in comfort and discomfort of individual passengers with an accuracy of 0.12±0.01 and 0.21±0.01 regarding normalized comfort and discomfort scores, respectively. Additionally, contributions of different factors were highlighted.
Conclusion: The outcomes of the models show that we took a step forward in modeling the human comfort experience using objective measurements. Anthropometry (including age), seat positions, time duration, and row (noise) emerged as leading factors influencing the feeling of (dis)comfort in turboprop planes.
期刊介绍:
WORK: A Journal of Prevention, Assessment & Rehabilitation is an interdisciplinary, international journal which publishes high quality peer-reviewed manuscripts covering the entire scope of the occupation of work. The journal''s subtitle has been deliberately laid out: The first goal is the prevention of illness, injury, and disability. When this goal is not achievable, the attention focuses on assessment to design client-centered intervention, rehabilitation, treatment, or controls that use scientific evidence to support best practice.