Streets classification models by urban features for road traffic noise estimation.

Area of Science:

• Environmental Science
• Urban Planning
• Acoustics

Background:

• Road traffic is a major source of urban noise pollution, exacerbated by poor urban planning.
• Effective noise reduction strategies require integrating traffic noise analysis into urban development.

Purpose of the Study:

• To develop a universal methodology for stratifying urban roads by functionality using urban features.
• To create a tool for clustering similar streets and their associated traffic noise levels.

Main Methods:

• Utilized multivariate ordered logistic regression to develop three statistical models (Model 1, 2, and 3).
• Stratified urban roads into five, four, and three categories, respectively, achieving high predictive accuracy (McFadden pseudo-R² 0.5–0.6).

Main Results:

• Identified key urban features (road dimensions, transport infrastructure, public transit routes) linked to increased traffic noise.
• Model 1 is suitable for developed cities, Model 2 for growing cities, and Model 3 for local management and specific acoustic applications.

Conclusions:

• The developed models provide a data-driven approach for urban and transportation planners to reduce noise exposure.
• These models support strategic urban planning for effective, adaptable, and sustainable city noise reduction.