Innovative bootstrap approach for the estimation of minimum measurement time interval in road traffic noise evaluation

Liguori, Consolatina; Ruggiero, Alessandro; Russo, Domenico; Sommella, Paolo

doi:10.1016/j.measurement.2016.12.008

Cited by 23 publications

(3 citation statements)

References 27 publications

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“…Moreover, it has been observed that in order to characterize environmental noise in situ by means of

measurements, mainly two periods of time should bee considered [ 10 , 29 ]: diurnal reference time (

) or nocturnal reference time (

), using a pre-set window with a closed observation period. The main goal is to determine the minimum measurement time interval to obtain an accurate estimation of

values, improving the stability of the measurements [ 31 ]. This is crucial when these values are used to update a dynamic noise map, which combines real-time measurements and data processing to assess the acoustic impact of noise sources.…”

Section: Related Workmentioning

confidence: 99%

WASN-Based Day–Night Characterization of Urban Anomalous Noise Events in Narrow and Wide Streets

Álías

Socoró

Alsina-Pagès

2020

Sensors

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In addition to air pollution, environmental noise has become one of the major hazards for citizens, being Road Traffic Noise (RTN) as its main source in urban areas. Recently, low-cost Wireless Acoustic Sensor Networks (WASNs) have become an alternative to traditional strategic noise mapping in cities. In order to monitor RTN solely, WASN-based approaches should automatize the off-line removal of those events unrelated to regular road traffic (e.g., sirens, airplanes, trams, etc.). Within the LIFE DYNAMAP project, 15 urban Anomalous Noise Events (ANEs) were described through an expert-based recording campaign. However, that work only focused on the overall analysis of the events gathered during non-sequential diurnal periods. As a step forward to characterize the temporal and local particularities of urban ANEs in real acoustic environments, this work analyses their distribution between day (06:00–22:00) and night (22:00–06:00) in narrow (1 lane) and wide (more than 1 lane) streets. The study is developed on a manually-labelled 151-h acoustic database obtained from the 24-nodes WASN deployed across DYNAMAP’s Milan pilot area during a weekday and a weekend day. Results confirm the unbalanced nature of the problem (RTN represents 83.5% of the data), while identifying 26 ANE subcategories mainly derived from pedestrians, animals, transports and industry. Their presence depends more significantly on the time period than on the street type, as most events have been observed in the day-time during the weekday, despite being especially present in narrow streets. Moreover, although ANEs show quite similar median durations regardless of time and location in general terms, they usually present higher median signal-to-noise ratios at night, mainly on the weekend, which becomes especially relevant for the WASN-based computation of equivalent RTN levels.

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“…Moreover, it has been observed that in order to characterize environmental noise in situ by means of

measurements, mainly two periods of time should bee considered [ 10 , 29 ]: diurnal reference time (

) or nocturnal reference time (

), using a pre-set window with a closed observation period. The main goal is to determine the minimum measurement time interval to obtain an accurate estimation of

Section: Related Workmentioning

confidence: 99%

WASN-Based Day–Night Characterization of Urban Anomalous Noise Events in Narrow and Wide Streets

Álías

Socoró

Alsina-Pagès

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…To create a noise map based on measurements, the levels shown in the noise map must be representative, which requires having a large amount of data in both the duration of the measurements [1][2][3][4] and in sampling sites [5,6]. The measurement equipment should fulfill minimum requirements regarding its accuracy which makes more expensive the sampling process.…”

Section: Introductionmentioning

confidence: 99%

A low-cost noise measurement device for noise mapping based on mobile sampling

2019

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Section: Introductionmentioning

confidence: 99%

Approach to acoustic mapping through continuous mobile monitoring

Pérez¹

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For the production of representative noise maps, a large amount of information is necessary, which includes, among others, on-site measurements of environmental noise. Thus, for noise maps based on measurements, mobile sampling emerges as a possible solution for the enhancement of data acquisition. The present research proposes a complete framework to perform mobile sampling. Since the normative requires long-term values to be presented in a noise map, a sampling strategy based on temporal stratification, which reduces the required sampled days to estimate the annual equivalent noise level, is presented. Furthermore, to compute long-term values for the night period, since they are usually affected by noise sources different to traffic, specifically leisure noise, a complementary temporal and spatial stratification is also presented. Then, the statistical requirements to perform mobile noise measurements using bicycles is evaluated. The vehicles and bicycles journeys are reproduced based on micro-traffic simulation and then coupled with an acoustic modeling. The estimation error of LAeq for the mobile sampling is compared to reference static samples, in terms of the Root Mean Square Error (RMSE), and is computed for different aggregation radius of mobile receivers, and as a function of the number of passes-by and to the distance to its nearest cross street. To perform the mobile sampling on a real scenario, a low-cost noise monitoring device with the aim of performing georeferenced noise sampling, is developed. The accuracy tests suggest that it is able to acquire noise levels with an equivalent accuracy as a Class 2 sound level meter. Finally, to validate the results obtained through the modeling framework, a noise monitoring device is mounted on a bicycle and on-site mobile measurements are performed simultaneously to reference static ones. The same scenario is again recreated based on micro-simulation of traffic complemented with acoustic modeling. Then, for the simulated framework and the on-site measurements, the RMSE of the estimation of LAeq for different aggregation radius of mobile samples is compared to the reference static ones. It is confirmed that mobile sampling is a solution to improve noise data acquisition, which reduces the resources required to produce a noise map without sacrificing the accuracy and representativeness. Para la producción de mapas de ruido representativos, una gran cantidad de información es necesaria, que incluye, entre otras, mediciones de ruido ambiental en sitio. Por lo tanto, para los mapas de ruido basados en mediciones, el muestreo móvil surge como una posible solución para mejorar la adquisición de datos. La presente investigación propone un marco completo para realizar el muestreo móvil. Dado que la normativa requiere de valores de largo plazo para realizar un mapa de ruido, se propone una estrategia de muestreo basada en estratificación temporal que reduce los días de muestreo necesarios para estimar el nivel de ruido equivalente anual. Además, para calcular los valores a largo plazo para el período nocturno, ya que generalmente se ve afectado por fuentes de ruido distintas al tráfico, específicamente de ruido de ocio, se presenta una estratificación temporal y espacial complementaria. Por otra parte, se evalúan los requisitos estadísticos para realizar mediciones de ruido móviles utilizando bicicletas. Los trayectos de los vehículos y las bicicletas se reproducen en base a una microsimulación de tráfico para después combinarse con modelado acústico. El error de estimación del indicador LAeq calculado con el muestreo móvil se compara, en términos del error cuadrático medio (RMSE), con muestras de referencia obtenidas de forma estática para diferentes radios de agregación de receptores móviles, y también en función del número de pases y de la distancia al cruce de calles más cercano. Para realizar el muestreo móvil en un escenario real, se desarrolla un dispositivo de monitoreo de ruido de bajo costo con el objetivo de realizar un muestreo de ruido georreferenciado. Las pruebas de precisión muestran que es capaz de adquirir niveles de ruido con una precisión equivalente a un sonómetro de Clase 2. Finalmente, para validar los resultados obtenidos a través de las simulaciones, se equipa una bicicleta con el dispositivo de monitoreo y se realizan mediciones móviles en sitio al mismo tiempo que mediciones estáticas de referencia. El mismo escenario se recrea basándose nuevamente en una microsimulación de tráfico complementada con modelado acústico. Después, para los niveles de ruido simulados y las mediciones en sitio, el RMSE de la estimación del indicador LAeq para distintos radios de agregación de muestras móviles se compara con el muestreo estático de referencia. Con ello, se confirma que el muestreo móvil es una solución para mejorar la adquisición de datos de ruido, lo que reduce los recursos necesarios para producir un mapa de ruido sin sacrificar la precisión y la representatividad.

show abstract

Innovative bootstrap approach for the estimation of minimum measurement time interval in road traffic noise evaluation

Cited by 23 publications

References 27 publications

WASN-Based Day–Night Characterization of Urban Anomalous Noise Events in Narrow and Wide Streets

WASN-Based Day–Night Characterization of Urban Anomalous Noise Events in Narrow and Wide Streets

A low-cost noise measurement device for noise mapping based on mobile sampling

Approach to acoustic mapping through continuous mobile monitoring

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