Evaluation of tyre/road noise and texture interaction on rubberised and conventional pavements using CPX and profiling measurements

León, Gonzalo; Pizzo, Alessandro Del; Teti, Luca; Moro, Antonino; Bianco, Francesco; Fredianelli, Luca; Licitra, Gaetano

doi:10.1080/14680629.2020.1735493

Cited by 53 publications

(15 citation statements)

References 24 publications

(23 reference statements)

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“…RTN (road traffic noise) in the most impacting noise source of the modern lifestyle (Ruiz-Padillo et al 2016 ). Although the parameters influencing the emission of noise usually considered include vehicle engines and flow composition, studies have also established “acoustic impedance of road surfaces” (Bianco et al 2020 ; Praticò et al 2021 ), “type of vehicle tyres” (Licitra et al 2017 ), “pavement ageing” (Teti et al 2020 ), and “pavement texture and material” (Praticò and Anfosso-Lédée 2012 ; Praticò 2014 ; Del Pizzo et al 2020 ; de León et al 2020 ) as other important parameters that should be considered.…”

Section: Introductionmentioning

confidence: 99%

A study on the assessment of traffic noise induced annoyance and awareness levels about the potential health effects among residents living around a noise-sensitive area

Gilani

Mir

2021

Environ Sci Pollut Res

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The present cross-sectional study aimed to evaluate the risk factors for traffic noise–induced annoyance and also assess the awareness levels among the exposed population concerning the health impacts caused by traffic noise. Field measurements were made to validate the application of the standard noise models, which were later used to present the acoustical environment and assess the exposure level around a super-speciality hospital surrounded by a residential zone. Results from the noise maps and façade maps revealed that the area was exposed to noise levels exceeding the upper safe limits by more than 10 dB(A). The effect of exposure in the form of annoyance and the awareness level were evaluated using a questionnaire survey in a sample of 565 residents. Attention questions were incorporated in the questionnaire, and the awareness level was evaluated using the mean awareness index score. Respondents living in noisy areas were having a higher risk for annoyance as compared to those living in quiet areas (OR = 4.06; 95% CI = 2.79–5.88). Reporting poor sleep quality, being sensitive to noise, and noise perception at home were the significant risk factors for annoyance. Most of the respondents were classified as having no/little awareness about serious health ailments caused by traffic noise. Lower awareness levels, despite a higher literacy rate and a higher percentage of the young population, imply that there is a need for undertaking mass awareness programmes so that the impacts can be reduced to a minimum, if not eliminated. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-021-15208-3.

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Section: Introductionmentioning

confidence: 99%

A study on the assessment of traffic noise induced annoyance and awareness levels about the potential health effects among residents living around a noise-sensitive area

Gilani

Mir

2021

Environ Sci Pollut Res

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“…Besides engine noise, tire/road interaction-also known as tire/road noise-is the main generation mechanism acting in the speed range cars usually have in urban and suburban contexts [16]. Tire/road noise depends on both the tire model [17] and the type of road surface [18,19], since the effect of its properties such as road texture [20][21][22] and its job mix formula [23,24] can be considered when optimizing pavements from an acoustic point of view. Low-noise pavements represent an effective tool in road traffic noise mitigation, not only because they reduce noise acting on the sound power of the source, but also because, compared to other solutions-such as ordinary noise barriers-they yield a smaller impact on the surrounding environment and on the citizens.…”

Section: Introductionmentioning

confidence: 99%

Traffic Flow Detection Using Camera Images and Machine Learning Methods in ITS for Noise Map and Action Plan Optimization

Fredianelli

Carpita²,

Bernardini

et al. 2022

Sensors

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Noise maps and action plans represent the main tools in the fight against citizens’ exposure to noise, especially that produced by road traffic. The present and the future in smart traffic control is represented by Intelligent Transportation Systems (ITS), which however have not yet been sufficiently studied as possible noise-mitigation tools. However, ITS dedicated to traffic control rely on models and input data that are like those required for road traffic noise mapping. The present work developed an instrumentation based on low-cost cameras and a vehicle recognition and counting methodology using modern machine learning techniques, compliant with the requirements of the CNOSSOS-EU noise assessment model. The instrumentation and methodology could be integrated with existing ITS for traffic control in order to design an integrated method, which could also provide updated data over time for noise maps and action plans. The test was carried out as a follow up of the L.I.S.T. Port project, where an ITS was installed for road traffic management in the Italian port city of Piombino. The acoustic efficacy of the installation is evaluated by looking at the difference in the acoustic impact on the population before and after the ITS installation by means of the distribution of noise exposure, the evaluation of Gden and Gnight, and the calculation of the number of highly annoyed and sleep-disturbed citizens. Finally, it is shown how the ITS system represents a valid solution to be integrated with targeted and more specific sound mitigation, such as the laying of low-emission asphalts.

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“…Other than tyres, the acoustic performance depends on various pavement characteristics including pavement texture [17] and pavement age [18]. Understanding such noise characteristics helps in devising mitigating strategies, including pavement noise reduction with use of new technologies in design and materials [19], such as rubberized asphalt [20,21]. Understanding the noise factors helps in analysing the noise profile of a specific location along with the location characteristics.…”

Section: Introductionmentioning

confidence: 99%

Traffic Noise Modelling Using Land Use Regression Model Based on Machine Learning, Statistical Regression and GIS

et al. 2021

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This study estimates the equivalent continuous sound pressure level (Leq) during peak daily periods (‘rush hour’) along the New Klang Valley Expressway (NKVE) in Shah Alam, Malaysia, using a land use regression (LUR) model based on machine learning, statistical regression, and geographical information systems (GIS). The research utilises two types of soft computing methods including machine learning (i.e., decision tree, random frost algorithms) and statistical regression (i.e., linear regression, support vector regression algorithms) to determine the best approach to create a prediction Leq map at the NKVE in Shah Alam, Malaysia. The selection of the best algorithm is accomplished by considering correlation, correlation coefficient, mean-absolute-error, mean-square-error, root-mean-square-error, and mean absolute percentage error. Traffic noise level was monitored using three sound level meters (TES 52A), and a traffic tally was done to analyse the traffic flow. Wind speed was gauged using a wind speed meter. The study relied on a variety of noise predictors including wind speed, digital elevation model, land use type (specifically, if it was residential, industrial, or natural reserve), residential density, road type (expressway, primary, and secondary) and traffic noise average (Leq). The above parameters were fed as inputs into the LUR model. Additional noise influencing factors such as traffic lights, intersections, road toll gates, gas stations, and public transportation infrastructures (bus stop and bus line) are also considered in this study. The models utilised parameters derived from LiDAR (Light Detection and Ranging) data, and various GIS (Geographical Information Systems) layers were extracted to produce the prediction maps. The results highlighted the superior performances by the machine learning (random forest) models compared to the statistical regression-based models.

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Evaluation of tyre/road noise and texture interaction on rubberised and conventional pavements using CPX and profiling measurements

Abstract: Licitra (2020) Evaluation of tyre/road noise and texture interaction on rubberised and conventional pavements using CPX and profiling measurements,

Cited by 53 publications

References 24 publications

A study on the assessment of traffic noise induced annoyance and awareness levels about the potential health effects among residents living around a noise-sensitive area

A study on the assessment of traffic noise induced annoyance and awareness levels about the potential health effects among residents living around a noise-sensitive area

Traffic Flow Detection Using Camera Images and Machine Learning Methods in ITS for Noise Map and Action Plan Optimization

Traffic Noise Modelling Using Land Use Regression Model Based on Machine Learning, Statistical Regression and GIS

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