Analysis and Management of Current Road Traffic Noise

Streets classification models by urban features for road traffic noise estimation

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.

Drastic mobility restrictions during SARS-CoV-2 pandemic: an opportunity to learn about constraints on the way to a pollution-free city

Road traffic is one of the main sources of pollution in modern cities. If there is a desire to move towards healthier cities, it may be necessary to modify the current model of mobility. The SARS-CoV-2 pandemic, together with the measures applied by most governments in the world to control the mobility of citizens, offered a unique opportunity to assess the changes in pollution levels after a drastic reduction in road traffic. In this study, air and noise pollution levels and road traffic flow were analyzed in the city of Cáceres, Spain, before and during the state of emergency imposed by the Spanish government. The values obtained were compared with the quality limits established by both the Spanish government and the World Health Organization (WHO). A traffic noise prediction model has been employed to evaluate the acoustic impact resulting from the reduction in traffic flow. As a result of this study, it was found that air pollution was indeed reduced due to the mobility restrictions imposed to control the pandemic, but that the WHO's recommendations for the values of the day-evening-night noise indicator (Lden) and the night-time noise indicator (Ln) for road traffic noise, which should not be exceeded, were not met. These findings highlight the need to review current urban mobility models if the WHO's recommendations are to be reached with regard to reducing the effects of exposure of the population to urban noise.

The co-benefits of electric mobility in reducing traffic noise and chemical air pollution: Insights from a transit-oriented city

Traffic noise is a growing threat to the urban population. Prolonged exposure to traffic noise has been linked to negative health consequences such as annoyance, sleep disturbances and cardiovascular diseases. While electric vehicles are known to have lower noise profiles, the impacts of electric mobility on traffic noise, especially for electrified heavy-duty vehicles, have not been thoroughly examined. This study aims to examine the impacts of both electric light-duty vehicles and electric buses on traffic noise levels in a highly urbanized city. Traffic noise along the source line and pedestrian network was first estimated and mapped to illustrate its spatiotemporal variations. Then, scenario analysis was used to compare the impacts. Population potentially benefiting from reduced traffic noise in the neighbourhoods and the associated health impacts were also estimated. Results indicate that electric buses have a greater potential to reduce traffic noise, with a maximum reduction of 4.4 dBA during daytime in the urban cores. With all bus fleet electrified, around 60% of the population can benefit from a reduction of 1 dBA at the street environment, 15.3% for 1-2 dBA, and 4.3% for more than 2 dBA. The estimated reduction of preventable deaths and preventable cases of diseases per 100,000 population are 4.15 and 112.99 respectively. The findings shed important insights into prioritizing bus routes to be electrified in urban areas for maximizing health co-benefits.