Please use this identifier to cite or link to this item: http://hdl.handle.net/10662/21496
Title: Streets classification models by urban features for road traffic noise estimation
Authors: Montegro, Alexandra Lyselott
Rey Gozalo, Guillermo
Arenas Bermúdez, Jorge P.
Suárez Silva, Enrique
Keywords: Road traffic noise;Urban sound environment;Sustainable urban planning;Multinomial ordered logistic regression;Ruido urbano;Plan urbano sostenible;Ruido de tráfico;Regresión logística multinomial
Issue Date: 2024
Publisher: Elsevier
Abstract: Road traffic is the primary source of environmental noise pollution in cities. This problem is also spreading due to inadequate urban expansion planning. Hence, integrating road traffic noise analysis into urban planning is necessary for reducing city noise in an effective, adaptable, and sustainable way. This study aims to develop a methodology that applies to any city for the stratification of urban roads by their functionality through only their urban features. It is intended to be a tool to cluster similar streets and, consequently, traffic noise to enable urban and transportation planners to support the reduction of people’s noise exposure. Three multivariate ordered logistic regression statistical models (Model 1, 2, and 3) are presented that significantly stratify urban roads into five, four, and three categories, respectively. The developed models exhibit a McFadden pseudo-R2 between 0.5 and 0.6 (equivalent to R2 >0.8). The choice between Model 1 or 2 depends on the scale of the city. Model 1 is recommended for developed cities with an extensive road network, while Model 2 is most suitable in intermediate and growing cities. On the other hand, Model 3 could be applied at any city scale but focused on local management of transit routes and for designing acoustic sensor installations, urban soundwalks, and identification of quiet areas. Urban features related to road width and length, presence of transport infrastructure, and public transport routes are associated with increased traffic noise in all three models. These models prove useful for future action plans aimed at reducing noise through strategic urban planning.
URI: http://hdl.handle.net/10662/21496
ISSN: 0048-9697
DOI: 10.1016/j.scitotenv.2024.173005
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