Modelling traffic noise-induced annoyance at intersections and its association with psychological health

Abstract

This study develops a model to examine traffic noise-induced annoyance and its mediating effect on the psychological health of shopkeepers and workers at intersections in mid-sized cities. Several hypotheses are proposed to explore effects of variables based on 487 data samples collected at 15 intersections in India. The integrated Partial Least Squares-Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) approach is employed in the present research. Noise sensitivity and perception to honk are ranked first and second regarding their importance in affecting annoyance. Moreover, personal (education), situational (perception to traffic jams and exposure hours), and acoustic factors (equivalent noise level and intermittency ratio) significantly affect annoyance. Further, the study findings indicate that annoyance has a partial mediation effect on psychological health due to traffic noise. The study results can be implemented to identify variables affecting annoyance and its effects on psychological well-being at intersections in mid-sized cities. © 2024 Elsevier Ltd