Accuracy of calculation of road traffic noise

Hood, R A

doi:10.1016/0003-682x(87)90006-5

Cited by 13 publications

(11 citation statements)

References 1 publication

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“…Generally the closer a barrier or earth mound is to the source the more effective it becomes. For simple plane barriers the height and length are the most important factors determining the degree of screening achieved (8) . The shadow zone of the barrier is the region where the receiver cannot see the source and here the greatest reductions in noise levels are recorded.…”

Section: Transmissionmentioning

confidence: 99%

RWB Stephens Lecture 2004: Reducing Traffic Noise Disturbance

WATTS

2023

Autumn Conference Acoustics 2004

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Section: Transmissionmentioning

confidence: 99%

RWB Stephens Lecture 2004: Reducing Traffic Noise Disturbance

WATTS

2023

Autumn Conference Acoustics 2004

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“…Different models have been set up by different national institutions, resulting in heterogeneous results when applied in the same context. Among the most used, it is worth mentioning the CoRTN model [26], which is commonly adopted in the United Kingdom, the SonRoad model which has been implemented in Switzerland [27], the NMBP model used in France [28], the ASJ in Japan [29], and the RLS90 in Germany [30], the Harmonoise [31], and Quartieri et al [32]. Besides all these models, the European Union (EU) has implemented the CNOSSOS one [33,34], which provides a common procedure for the assessment of transportation and industrial noise levels and the consequent development of noise maps, aiming at implementing a stand-alone model for noise assessment in all the European Countries that should receive and use it (by adapting it in some aspects if necessary).…”

Section: Introductionmentioning

confidence: 99%

Coupling Different Road Traffic Noise Models with a Multilinear Regressive Model: A Measurements-Independent Technique for Urban Road Traffic Noise Prediction

Rossi,

Pascale,

Mascolo

et al. 2024

Sensors

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Road traffic noise is a severe environmental hazard, to which a growing number of dwellers are exposed in urban areas. The possibility to accurately assess traffic noise levels in a given area is thus, nowadays, quite important and, on many occasions, compelled by law. Such a procedure can be performed by measurements or by applying predictive Road Traffic Noise Models (RTNMs). Although the first approach is generally preferred, on-field measurement cannot always be easily conducted. RTNMs, on the contrary, use input information (amount of passing vehicles, category, speed, among others), usually collected by sensors, to provide an estimation of noise levels in a specific area. Several RTNMs have been implemented by different national institutions, adapting them to the local traffic conditions. However, the employment of RTNMs proves challenging due to both the lack of input data and the inherent complexity of the models (often composed of a Noise Emission Model–NEM and a sound propagation model). Therefore, this work aims to propose a methodology that allows an easy application of RTNMs, despite the availability of measured data for calibration. Four different NEMs were coupled with a sound propagation model, allowing the computation of equivalent continuous sound pressure levels on a dataset (composed of traffic flows, speeds, and source–receiver distance) randomly generated. Then, a Multilinear Regressive technique was applied to obtain manageable formulas for the models’ application. The goodness of the procedure was evaluated on a set of long-term traffic and noise data collected in a French site through several sensors, such as sound level meters, car counters, and speed detectors. Results show that the estimations provided by formulas coming from the Multilinear Regressions are quite close to field measurements (MAE between 1.60 and 2.64 dB(A)), confirming that the resulting models could be employed to forecast noise levels by integrating them into a network of traffic sensors.

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“…Some of the models adopted in national regulations are listed in Ref. [15]: the CoRTN model in the United Kingdom [16], the RLS90 model in Germany [17], the NMPB model in France [18], the ASJ model in Japan [19], and the SONROAD model in Switzerland [20]. In such a complex scenario, the European Union issued the European Noise Directive related to the assessment and management of environmental noise, in which the main definitions and rules are provided to member states [21].…”

Section: Introductionmentioning

confidence: 99%

“…Firstly, the model can be calibrated without any field measurements by using a computed set of data. It is known, in fact, that model efficiency usually decreases when applying models to a site different from the ones where they were calibrated [16]. Secondly, the model can be recalibrated quickly when needed to include different traffic road conditions.…”

Section: Introductionmentioning

confidence: 99%

Calibration and Validation of a Measurements-Independent Model for Road Traffic Noise Assessment

2023

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The assessment of road traffic noise is very important for the health of people living in urban areas. Noise is usually assessed by field measurements, and predictive models play an important role when experimental data are not available. Nevertheless, when they are based on regression techniques, predictive models suffer from the drawback of strong dependence on the calibration data. In this paper, the authors present a regressive model calibrated on computed noise levels without the need for field measurements. The independence from field measurements makes the model flexible and adjustable for any road traffic condition possible. A multilinear regression technique is applied to establish the correlation between the computed equivalent noise levels and several independent variables, including, among others, traffic flow and distance. The model is then validated on a large field measurement database to check its efficiency in terms of prediction accuracy. The validation is performed both via error distribution analysis and using different error metrics. The results are encouraging, showing that the model provides good results in terms of the average error (less than 2 dBA) and is not susceptible to the presence of outliers in the input data that correspond to unconventional conditions of the traffic flow.

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Accuracy of calculation of road traffic noise

Cited by 13 publications

References 1 publication

RWB Stephens Lecture 2004: Reducing Traffic Noise Disturbance

RWB Stephens Lecture 2004: Reducing Traffic Noise Disturbance

Coupling Different Road Traffic Noise Models with a Multilinear Regressive Model: A Measurements-Independent Technique for Urban Road Traffic Noise Prediction

Calibration and Validation of a Measurements-Independent Model for Road Traffic Noise Assessment

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