Noise estimation model development using high-resolution transportation and land use regression

Omer Harouvi, Eran Ben-Elia, Roni Factor, Kees de Hoogh, Itai Kloog*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


Noise pollution is a common phenomenon of the 21st century. Noise prediction models tend to estimate noise levels mainly from road traffic sources (such as cars, public transportation etc.). This paper describes the adoption of land use regression (LUR) modeling methodology to assess noise pollution in two periods of the day (rush hour and off-peak), in two major cities in Israel (Tel Aviv and Beer Sheva). For both rush hour and off-peak times, 20 min short term measurements were used to develop a LUR noise estimation model. We used GIS-based predictors alongside commonly used traffic predictors. The findings show good fits for our model, with rush hour “out of sample” ten folds cross-validated R² of 0.79 (Tel Aviv) and 0.52 (Beer Sheva). The Tel Aviv model performance was also tested with independent monitoring data in an adjacent city (Bat Yam), presenting a good performance as well (R² of 0.93). The findings demonstrate the viability of using a LUR approach for applying high-resolution spatial data to estimate and map noise pollution for environmental noise assessment.

Original languageAmerican English
Pages (from-to)559-567
Number of pages9
JournalJournal of Exposure Science and Environmental Epidemiology
Issue number6
StatePublished - 1 Nov 2018

Bibliographical note

Publisher Copyright:
© 2018, Nature America, Inc., part of Springer Nature.


  • Epidemiology
  • Exposure assessment
  • Land use regression
  • Noise, Noise pollution
  • Transportation


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