Association between traffic noise-induced psychophysiological, and socio-demographic factors of motorcycle riders. (July 2022)

• Record Type: Journal Article
• Authors: Prasad Das, Chidananda; Goswami, Shreerup; Kumar Swain, Bijay; Das, Mira
• Is Part Of: Applied acoustics. Volume 196(2022)
• Publication Date: 2022-07
• Subjects: Structural Equation Modeling -- Artificial Neural Network -- Annoyance -- Blood pressure -- Sleeping problems -- Depression -- Transportation noise; Acoustical engineering -- Periodicals; Periodicals; 620.2
• DOI: 10.1016/j.apacoust.2022.108898
• Languages: English
• ISSNs: 0003-682X

Abstract

Highlights: In this study, Structural equation modeling and artificial neural network is used. Traffic noise induced psychophysiological responses are considered. Link between physiological, psychological and demographical factors is explored. Annoyance is modeled considering psychophysiological responses using ANN. Abstract: This is an exposure effect research carried out in Bhubaneswar with 363 motor cycle riders (equal exposure hours) who go to and from work every day on a specific path. The acoustic environment of 14 major squares along this route was measured, and the equivalent noise level was found to be higher than the permissible limit. The demographics and psychophysiological (annoyance, sleeping problem, tiredness, headache, and depression) responses of the participants were collected using standard questionnaires. Blood pressure responses were acquired by monitoring blood pressure before and after being exposed to traffic noise in the morning and evening for further analysis. Moreover, Structural Equation Model (SEM) revealed a significant relationship between "gender and blood pressure, " "gender and headache, " "gender and tiredness, " "marital status and blood pressure, " "profession and annoyance, " "sleeping problem and depression, " "annoyance and blood pressure, " "annoyance and depression, " "annoyance and headache, " "annoyance and sleeping problem, " "annoyance and tiredness, " "blood pressure and depression, " "blood pressure and sleeping problem." However, Artificial Neural Network (ANN) model found blood pressure as the most significant outcome of annoyance, followed by depression, sleeping problems, tiredness, and headache. Furthermore, depression and sleep problems are found to be the second most important outcomes, with a normalized importance of 91%.