Model development for traffic noise annoyance prediction

Aşcıgil-Dincer, Mine; Demirkale, Sevtap Yılmaz

doi:10.1016/j.apacoust.2021.107909

Cited by 15 publications

(9 citation statements)

References 42 publications

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“…As expected, the results show that annoyance is largely determined by noise disturbance and the noisiness perceived by citizens. Finally, in [33] an approach to develop a road traffic noise annoyance prediction model is presented, and it takes into account: (i) social aspects, (ii) characteristics of traffic and (iii) urban development. It is based on the creation of a local model, with a pilot in Istanbul (Turkey), which uses all the information gathered for the creation of the noise maps as an input, and provides annoyance levels prediction as an output, complementing the noise maps that provide no subjective indicator.…”

Section: Annoyance Predictionmentioning

confidence: 99%

Multilevel Annoyance Modelling of Short Environmental Sound Recordings

Orga¹,

Mitchell

Freixes³

et al. 2021

Sustainability

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The recent development and deployment of Wireless Acoustic Sensor Networks (WASN) present new ways to address urban acoustic challenges in a smart city context. A focus on improving quality of life forms the core of smart-city design paradigms and cannot be limited to simply measuring objective environmental factors, but should also consider the perceptual, psychological and health impacts on citizens. This study therefore makes use of short (1–2.7 s) recordings sourced from a WASN in Milan which were grouped into various environmental sound source types and given an annoyance rating via an online survey with N=100 participants. A multilevel psychoacoustic model was found to achieve an overall R2=0.64 which incorporates Sharpness as a fixed effect regardless of the sound source type and Roughness, Impulsiveness and Tonality as random effects whose coefficients vary depending on the sound source. These results present a promising step toward implementing an on-sensor annoyance model which incorporates psychoacoustic features and sound source type, and is ultimately not dependent on sound level.

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Section: Annoyance Predictionmentioning

confidence: 99%

Multilevel Annoyance Modelling of Short Environmental Sound Recordings

Orga¹,

Mitchell

Freixes³

et al. 2021

Sustainability

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“…The method calculating MA or %HA of noise by the prediction model can save the cost above. Previous studies have developed many prediction models of MA or %HA for different types of traffic noise, industrial noise, community noise, etc., 9,10 while these models used different acoustic parameters. Based on loudness ( N ), sharpness ( S ), fluctuation strength ( F ), and roughness ( R ), Zwicker 4 established a psychoacoustic annoyance ( PA ) model suiting for different types of noise.…”

Section: Introductionmentioning

confidence: 99%

A study on the conversion relationship of noise perceived annoyance and psychoacoustic annoyance—a case of substation noise

Chen

Yao

et al. 2022

Journal of Low Frequency Noise, Vibration and Active Control

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The level of annoyance is an important basis to determine the acceptable degree of noise and develop noise standards. Psychoacoustic annoyance ( PA) calculated by Zwicker’s model and perceived annoyance (such as mean annoyance, MA and the percentage of highly annoyed population, % HA) obtained through individual self-reports are widely used. PA and MA (or %HA) cannot be directly compared because the ranges of their values are different. Thus, the conversion relationship of PA and MA (or %HA) needs to be developed. As a case study, the model between PA and MA (or %HA) of substations noise was established and the rationality of model was verified. Results showed that the maximum value of the difference of MA (or % HA) between the calculation result of model and experimental result was less than 0.89 (or 15%). In this way, the perceived annoyance of substation noise samples can be determined by calculation without experiments.

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“…Using pre-defined ERRs reduces the cost and time to measure annoyance compared to conducting field surveys. On the other hand, calculating noise annoyance via ERRs may lead to inaccurate results, as these relations overlook non-acoustic factors of annoyance that can impact noise perception more than the noise level itself [8]. Some of the factors influencing annoyance level include noise source type (traffic, railroad or air) and personal aspects such as age, emotion [9], expectations regarding the noise [10] and noise sensitivity of the individual [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…L night is correlated with the percentage of people suffering from sleep disturbance. Additionally, if socio-acoustic surveys are performed, new dose-effect relations can be constructed to give insights into annoyance and sleep disturbance for each particular case [8]. Once areas exposed to excessive noise levels are identified via strategic noise mapping, the END requires action plans to manage the adverse effects of noise exposure.…”

Section: Introductionmentioning

confidence: 99%

“…Although many studies have focused on the objective noise reduction of road traffic noise interventions, only a limited number of studies have examined the effectiveness of road traffic noise interventions on human health, as stated in the systematic review paper [27]. The decrease of annoyance is mostly disregarded or calculated only through pre-defined ERRs, as the qualitative nature of annoyance is more complex to assess [8]. Many national and regional governments invest substantial public funding into noise interventions resulting from various noise action plans.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Impact of Thin Asphalt Layers as a Road Traffic Noise Intervention in an Urban Environment

2021

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Low-noise thin asphalt layers (TALs) are a feasible solution to mitigate road traffic noise in urban environments. Nevertheless, the impacts of this type of noise intervention are reported mostly regarding noise levels, while non-acoustic aspects influencing the population perception are still little-known. This study investigates the implementation of TALs in two streets of Antwerp, Belgium. The effectiveness of the intervention was measured via noise modelling and acoustic measurements of road traffic noise. A reduction of 2.8 dB in noise exposure was observed in Lden and Lnight, while SPB measurements showed decreases up to 5.2 dB on the roadside. The subjective impacts of the TALs were evaluated via self-administered surveys and compared to results from control streets. The annoyance indicators were positively impacted by the TALs implementation, resulting in annoyance levels similar or lower than in the control streets. The TALs did not impact the reported physical complaints, sleep quality, and comfort level to perform activities.

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Model development for traffic noise annoyance prediction

Cited by 15 publications

References 42 publications

Multilevel Annoyance Modelling of Short Environmental Sound Recordings

Multilevel Annoyance Modelling of Short Environmental Sound Recordings

A study on the conversion relationship of noise perceived annoyance and psychoacoustic annoyance—a case of substation noise

The Impact of Thin Asphalt Layers as a Road Traffic Noise Intervention in an Urban Environment

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