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![Fig. 6. The signal-to-noise ratio [dB (A)] against detection levels and...](profile/Catarina_Mendonca2/publication/235984584/figure/fig5/AS:340777386430473@1458259069004/The-signal-to-noise-ratio-dB-A-against-detection-levels-and-logistic-fit_Q320.jpg)
In this paper, annoyance ratings from traffic noise recorded on cobblestones, dense asphalt, and open asphalt rubber pavements are assessed with regard to car speeds and traffic densities. It was found that cobblestones pavements are the most annoying; also while open asphalt rubber pavement imposes less annoyance than dense asphalt it is not signi...
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This article presents different modes of a sports vehicle allowed to be used on public roads on a dry asphalt surface during intensive acceleration with the use of two launch control systems. The vehicle was thoroughly characterized with paying extra attention to power train and braking system. The result of carried out experimental research and an...
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... Speed limit reductions and quieter road surfaces not only reduce overall noise emissions but also change the spectral and psychoacoustic characteristics of tyre/road noise (Barros et al., 2023), which are closely related to the human auditory perception (Altinsoy, 2022). This effect has been confirmed via laboratory-based listening tests, where traffic noise from higher speeds or uneven surfaces caused significantly more annoyance than expected given the overall differences in noise levels only (Freitas et al., 2012;Fiebig & Jakobs, 2022). ...
... The deteriorated AC enabled assessing damage-related effects on rolling noise, while the cobblestones allowed evaluating the influence of a higher megatexture. Despite causing considerable rolling noise emission, cobblestones are still commonly found in historical urban centers, associated with architectural heritage (Freitas et al., 2012). ...
... The rise in annoyance due to speed is less expressive in AC deteriorated and the smallest for AC new. Similar, but slightly stronger, speed impacts on noise annoyance were reported from listening tests (Freitas et al., 2012): approximately 0.08 points (out of 10) per 1 km/h for cobblestones and 0.05 for dense asphalt concrete, although the authors explored a wider and more granular speed range (30-70 km/h in 10 km/h steps). The stimuli presentation order, an experimental artefact, increased annoyance as the experiment progressed. ...
The acoustic environment in urban spaces is often dominated by human-made noise sources, with road traffic noise as the most pervasive. Meanwhile, urban planning often overlooks how soundscape can impact citizens' well-being. This study combined virtual reality, biometric sensing, and questionnaires to evaluate how urban design measures targeting road traffic noise affect, beyond acoustic characteristics, psychological and physiological stress indicators. Participants (N=37) were immersed in a virtual urban environment with passing vehicles at different speeds (20,30, 50 km/h) over different road surface types and maintenance levels (new vs. deteriorated asphalt concrete and cobblestones) and varying green infrastructure (Green View Index: 0%, 14%, 26%). Noise stimuli were captured through CPX measurements and subsequently auralized, resulting in signals with LAeq spanning a 20 dBA range. Phasic skin conductance (SC), heart rate (HR), and high-frequency heart rate variability (HF-HRV) were recorded, while noise annoyance and cognitive performance were measured through self-report. Noise annoyance consistently increased with poorer pavement conditions and higher speeds. Speed was linked to high phasic SC and HR, while road surface type increased phasic SC and reduced HF-HRV from new to deteriorated asphalt and cobblestones, indexing heightened physiological stress impacting the autonomic nervous system regulation in response to less favourable road/speed conditions. Greenery, at the GVI levels studied, did not impact physiological responses or cognition but minimally reduced noise annoyance. These findings suggest that enforcing lower speed limits and ensuring smoother, well-maintained road surfaces in urban areas can lessen the biological alert state activation while reducing psychological stress.
... The growth of road transport is a global environmental problem. Fast and safe automobile travel, especially on highways, results in increased noise emissions (Can and Aumond 2018, Freitas et al. 2012, Li et al. 2016) along with air and roadside soil pollution (Bernardino et al. 2019, De Silva et al. 2021, Hołtra and Zamorska-Wojdyła 2022, Wang and Zhang 2018, Werkenthin et al. 2014, Yan et al. 2013 To mitigate noise nuisance, noise barriers are installed near roads to reduce sound intensity to legal limits (Vanhooreweder et al. 2017). Noise barriers can also affect the emissions of particulate pollutants from vehicles into the atmosphere (Amini et al. 2016, Baldauf et al. 2008, Ghasemian et al. 2017, Hagler et al. 2012, 2011, Jeong, 2015. ...
Road infrastructure has negative environmental effects, such as noise, vibration, disruption of ecosystem services, and pollution. Noise barriers are used to reduce air pollution and absorb sound waves, but studies have shown that they can impact pollutant concentrations. A study conducted in Poland analyzed the composition of dust collected from roads with and without noise barriers and road exits. The dust was tested using an energydispersive X-ray fluorescence spectrometer, and the results were analyzed statistically. The study found that road dust collected in areas without barriers had significantly higher levels of certain elements, such as calcium, chromium, copper, nickel, lead, sulfur, and zirconium. In contrast, dust collected from areas with noise barriers had lower pollutant levels. These findings highlight the effectiveness of noise barriers in reducing pollution levels in areas adjacent to roads
... Long-term effects of exposure to traffic noise also include increased instances of cardiovascular disease, risk of stroke, diabetes, hypertension and loss of hearing [19]. Higher traffic speeds are associated with higher noise pollution and perceived annoyance levels in people exposed to the sounds [24]. However, to our knowledge there has been no study to date that has examined the impact of lowering road traffic speeds on the sonic environment people are exposed to in urban environments and how this affects wellbeing. ...
In urbanised landscapes, the scarcity of green spaces and increased exposure to anthropogenic noise have adverse effects on health and wellbeing. While reduced speed limits have historically been implemented to address traffic safety, their potential impact on residents’ wellbeing, especially in relation to engagement with natural soundscapes, remains understudied. Our study investigates the influence of i) natural soundscapes, including bird song, and ii) the addition of traffic noise to natural soundscapes at two speeds (20 mi/h and 40 mi/h) on mood. We found that natural soundscapes were strongly linked with the lowest levels of anxiety and stress, with an increase in stress levels associated with mixed natural soundscapes with the addition of 20 mi/h traffic noise and the highest levels with 40 mi/h traffic noise. Higher levels of hedonic tone, indicative of positive mood, was noted with natural soundscapes, but diminished when combined with 40 mi/h traffic noise. Our results show that anthropogenic soundscapes including traffic sounds can mask the positive impact of natural soundscapes including birdsong on stress and anxiety. However, reducing traffic speeds in cities could be a positive intervention for enhancing access to nature. Technological solutions, such as the widespread adoption of hybrid and electric vehicles, and urban planning strategies like integrating green spaces into transit routes, offer potential opportunities to mitigate the impact of noise pollution and benefit humans in urban environments.
... In this sense, excessive exposure to traffic noise has been related to several negative impacts. From a health perspective, prolonged exposure to high noise levels can lead to stress, sleep disturbances, cognitive impairment, hypertension and cardiovascular disorders (EEA, 2020a;Freitas et al., 2012;Hammersen et al., 2016;Shepherd et al., 2010;WHO, 2011). ...
... For example, Vijay et al. (2015) reported that noise level increases by nearly 4-5 dB(A) for a speed increase from 35km/h to 55 km/h. Other studies found that the magnitude of traffic noise is influenced by traffic volume, traffic composition and road type (Subramani et al., 2012;Freitas et al., 2012). For example, Suthanaya (2015) and Abo-Qudais and Alhiary (2007) concluded that noise increases with the increase in traffic volume and percentage of trucks. ...
... The results of this study confirmed the impact of traffic flows in both directions of travel on the generated noise. This result is consistent with previous findings, which indicated that traffic flow significantly increases the generated-noise level (Alkheder, 2023;Ibili et al., 2022;Mishra et al., 2021;Freitas et al., 2012). Also, the results of this study indicated that traffic composition greatly influences external noise. ...
... According to Eq.2, an increase in traffic speed from 35 km/h to 55 km/h would increase the maximum generated noise by about 2.7 dB(A). However, this increase is lower than that obtained in previous studies, which was from 4 dB(A) to 5 dB(A) (Vijay et al., 2015;Freitas et al., 2012). Again, this result is not illogical, because considerable percentages of electric-power and hybrid vehicles have been introduced in the vehicle fleet during the last two years. ...
Traffic-related noise pollution is a recurring problem in large and medium-sized cities. The objective of this study is to quantify traffic-noise levels along selected urban arterials and model the generated traffic noise based on traffic and pavement characteristics. Urban arterial and collector streets in Irbid city, Jordan, were taken as a case study. The city is considered an example of a medium-sized city. 65 urban arterial and collector sections were selected to achieve the stated objective. For each section, ten noise measurements were taken using a time interval of 2.5 minutes for each observation. The statistical pass-by method was used to measure 650 externalnoise observations. In addition, data on traffic, pavement and section geometric characteristics was obtained through field measurements. The collected traffic characteristics, including traffic flow, percentage of trucks and speed in each direction of travel, were obtained. In addition to the measurement of pavement macrotexture depth, an international roughness index was measured using a smartphone application. Investigation of the collected noise data indicated that urban streets experienced high noise levels, reaching maximum and average values of 82 and 77.2 dB(A), respectively. The results of the analyses showed that an increase in each of the included traffic characteristics resulted in significantly higher noise levels. For example, an increase in speed from 35 to 55 km/h would increase noise by 2.7 dB(A). In addition, the interaction term of roughness index and pavement macrotexture depth was found to increase the generated noise. Finally, the results of the analysis indicated that both multivariate linear -and exponential-regression models are suitable to model the generated traffic noise. Each model explained approximately 54% of the noise variability. Probably, traffic composition and vehicle-power type heterogeneity might reduce the level of explained variability. Keywords: Traffic noise, Urban arterials, Pass-by method, Noise modeling, Jordan.
... The lesser effect of speed is due to low engine and tyre-pavement interaction noise. Existing research also hints that speed has a low impact on L Aeq at speeds below 30 km/h (Freitas et al., 2012;Rossi et al., 2020). The result indicates that wind speed has an insignificant effect on traffic noise levels. ...
... Therefore, their effects are not dominant on L Aeq . Past research also indicates that climate conditions do not affect L Aeq for similar conditions (Freitas et al., 2012;Quiñones-Bolaños et al., 2016). However, Q ent , Q ext , H , CW ent , CW ext , D w , and J L are determined as the dominant influencing variables and considered for developing the traffic noise model. ...
Traffic noise has emerged as one major environmental concern, which is causing a severe impact on the health of urban dwellers. This issue becomes more critical near intersections in mid-sized cities due to poor planning and a lack of noise mitigation strategies. Therefore, the current study develops a precise intersection-specific traffic noise model for mid-sized cities to assess the traffic noise level and to investigate the effect of different noise-influencing variables. This study employs artificial neural network (ANN) approach and utilizes 342 h of field data collected at nineteen intersections of Kanpur, India, for model development. The sensitivity analysis illustrates that traffic volume, median width, carriageway width, honking, and receiver distance from the intersection stop line have a prominent effect on the traffic noise level. The study reveals that role of noise-influencing variables varies in the proximity of intersections. For instance, a wider median reduces the noise level at intersections, while the noise level increases within a 50-m distance from intersection stop line. In summary, the present study findings offer valuable insights, providing a foundation for developing an effective managerial action plan to combat traffic noise at intersections in mid-sized cities.
... Furthermore, there is a significant difference in daytime and nighttime sound levels for BOS and Santa Casa (p = 0.025 and p > 0.001). These differences are directly related to the number, types, and speeds of vehicles circulating (Freitas et al., 2012) and the physical characteristics of the external area of each hospital (Gozalo et al., 2020). ...
Noise pollution has become a public health problem in several countries worldwide. Noise maps are tools used in many cities, mainly on the European continent. In other regions, they are used in smaller areas, and few studies focus on hospital areas, considered noise-sensitive zones. In this context, this study aimed to perform sound measurements and noise maps for the day and night periods in the surroundings of three hospitals in Sorocaba, Brazil. Sound measurements occurred around the three hospitals based on NBR 10151 and ISO 1996 standards. The noise maps were drawn up using a calculation model based on ISO 9613–2. Results showed that the sound measurement points around the hospitals had levels above those recommended by the Brazilian standard for sensitive areas (LAeq 50 and 45 dB for day and night, respectively). The acoustic maps showed high sound levels on all faces of the hospital buildings, both during the day and at night. The worst scenario concerned the vicinity of the roads with the highest flow and speed of vehicles. We concluded that three different hospitals in the city have high sound levels in their surroundings above the recommended for sensitive areas.
... Another study indicated that medium-weight vehicles were the dominant vehicles resulting in higher noise levels (Covaciu et al., 2015;Rao & Tripathy, 2018). Traffic volume can be measured using an automatic counter (Hamad et al., 2017), analysis of video recordings (Mansourkhaki et al., 2018), or counted manually (Freitas et al., 2012;Kuşkapan et al., 2022). ...
This paper reviews the application of artificial intelligence (AI)-based models in modeling vehicular road traffic noise. A computerized search method was used to conduct the literature search. Fifty published articles from 2007 to 2023 were reviewed regarding observation time, input data, countries where studies were performed, and modeling techniques. Sixty-three percent of the studies used an observation period of 60 min, and 29% used 15 min. All the reviewed papers considered traffic flow as the major input parameter, followed by average speed, with 95% of the researchers using it as an input parameter. It was found that using AI-based models for traffic noise prediction was popular in countries with no established empirical models. The primary input parameters for the AI-based models are traffic volume and speed. Traffic volume is used either as total traffic volume or classified into sub-categories, and each category is used as an independent input parameter. Although AI-based models have demonstrated reliable performance regarding prediction error and goodness of fit, the accuracy of the AI-based models’ performance should be compared with the results of the empirical models in countries with established models, such as the UK (CoRTN) and the USA (FHWA).
... In addition, wheel noise occurs when the vehicle's wheels come into contact with the road surface. Therefore, as the speed of the vehicle increases, the wheels have the potential to generate more noise 12,13 . ...
... Measurement-based studies have been conducted on noise acquisition (Yilmaz andHocanli 2006, Mehdi et al. 2011), quantification (Doygun andKuşat Gurun 2008, Li et al. 2002), gradation (Banerjee et al. 2008, Al-Ghonamy 2009, classification (Barrig� on Morillas et al. 2005, Mansouri et al. 2006, Zambon et al. 2016, and source backtracking (Talotte et al. 2003, Singh andDavar 2004). In particular, the traffic noises have been studied at different road forms separately, such as at different road grades (Brambilla et al. 2020), pavement types (Freitas et al. 2012, Son et al. 2014, intersections (Okonofua et al. 2016), overpasses (Gao et al. 2015), and roadworks sites (Tah-Chew and Keung 1991). In addition, a number of key transport facilities have also emerged as research hotspots, such as subway exits (Gershon et al. 2006, Neitzel et al. 2009, Ghotbi et al. 2012, railway stations (Trombetta Zannin and Bunn 2014, Asfiati et al. 2020), and airports (Wallis 1997, Cohen et al. 2008. ...
