Integrated Assessment of Urban Traffic Noise in Amman: A Survey-Based and Machine Learning Approach

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Key Points

The study aims to evaluate urban traffic noise levels and their impact on residents in Amman by combining survey data and predictive modeling.
Conducted a structured survey with 438 residents to gather data on perceived noise levels and health impacts.
Used machine learning techniques to develop predictive models for traffic noise and annoyance.
Evaluated noise annoyance across different activities and identified high-priority mitigation zones.
48.2% of respondents perceived traffic noise as a significant annoyance in their daily lives.
High levels of annoyance correlated with increased stress and reported health issues among residents.
Predictive models accurately forecasted noise levels based on vehicle density and other urban factors.

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A survey assessed urban traffic noise and its effects in Amman, indicating significant annoyance and health impacts.

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Ali Alqatawna (2026) studied this question.

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