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Monitoring and Prediction of Traffic Noise in Large Urban Zones

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Acoustics and Vibrations".

Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 12307

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Special Issue Editors

Dr. Hector Eduardo Roman

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Guest Editor

Department of Physics, University of Milano-Bicocca, Piazza della Scienza 3, 20126 Milan, Italy
Interests: environmental noise monitoring networks; road traffic noise and mapping predictions; sensor networks and their optimization; cloud and fog computing applications
Special Issues, Collections and Topics in MDPI journals

Dr. Roberto Benocci

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UNIMIB · Department of Earth and Environmental Sciences, Università degli Studi di Milano-Bicocca, Milan, Italy
Interests: soundscape in urban and natural areas; eco-acoustics; soundscape in marine habitats; noise mapping
Special Issues, Collections and Topics in MDPI journals

Dr. Giovanni Zambon

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Special Issue Information

Dear Colleagues,

Road traffic noise is the cause of challenging health-related issues in large urban environments. In Europe, for example, there are more than hundred million people exposed to Lden (day-evening-night) levels higher than 55dB (A). Indeed, long-term noise exposure to traffic noise is responsible for a variety of health-related problems, such as sleep disorder and tiredness, increased cardiovascular risk and hypertension, mental performance, and also its relationships between annoyance and exposure to transportation noise. The increasing awareness on these issues has promoted new EU policies through the Environmental Noise Directive (END) of 2002 and the WHO Environmental noise guidelines in 2018, by encouraging the use of distributed monitoring systems and noise mapping as an advanced tool for the control of noise exposure.

For this Special Issue, we invite authors actively working in the field of traffic noise and related areas to submit original papers and reviews of scientific works covering different aspects of traffic noise in large urban environments. We encourage submissions especially in the field of sensor networks for monitoring and controlling traffic noise. Statistical models of traffic noise, innovative analysis of health-related issues associated with noise and relationships between annoyance and exposure to urban noise, with particular emphasis on the detection of “disturbing” sound events, are also welcome.

Dr. Hector Eduardo Roman
Dr. Roberto Benocci
Prof. Giovanni Zambon
Guest Editors

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Keywords

Road traffic noise
Large urban zones
Health-related issues
Noise sensor networks
Traffic noise modelling

Published Papers (5 papers)

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Research

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21 pages, 10239 KiB

Open AccessArticle

Traffic Noise Reduction Strategy in a Large City and an Analysis of Its Effect

by Hsiao Mun Lee, Wensheng Luo, Jinlong Xie and Heow Pueh Lee

Appl. Sci. 2022, 12(12), 6027; https://doi.org/10.3390/app12126027 - 14 Jun 2022

Cited by 8 | Viewed by 2377

Abstract

A noise reduction strategy was proposed for Panyu District of Guangzhou City based on its traffic noise map, which considered both road and railway traffic noise. Commercial software was applied to compute the noise maps with and without noise barriers based on the field traffic flow measurements. Noise compliance maps were further developed to assess the effects of noise barriers on the quality of the sound environment. The change in populations exposed to unhealthy traffic noise with noise barriers was calculated, and the group noise indicators G_dn and G_night, which indicate the populations of highly annoyed and sleep-disturbed people, were evaluated. The results showed that the traffic noise level was significantly decreased after the implementation of noise barriers. It was predicted that with the strategy of applying suitable noise barriers, the overall areas under heavy noise pollution could be reduced by 24.5 and 24.3 km² during daytime and nighttime, respectively. Meanwhile, the total areas that could meet the regulated standard for noise levels were increased by 97.4, and 66.9 km², corresponding to compliance rate increments of 18.38% and 12.62%, respectively, in daytime and nighttime. The results further revealed that the installation of noise barriers could significantly reduce the population of highly annoyed people, while the reduction of the population experiencing sleep disturbances was insignificant. Full article

(This article belongs to the Special Issue Monitoring and Prediction of Traffic Noise in Large Urban Zones)

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21 pages, 3145 KiB

Open AccessArticle

WASN-Based Spectro-Temporal Analysis and Clustering of Road Traffic Noise in Urban and Suburban Areas

by Joan Claudi Socoró, Francesc Alías and Rosa Ma Alsina-Pagès

Appl. Sci. 2022, 12(3), 981; https://doi.org/10.3390/app12030981 - 19 Jan 2022

Cited by 1 | Viewed by 1296

Abstract

Environmental noise has become one of the principal health risks for urban dwellers and road traffic noise (RTN) is considered to be the main source of these adverse effects. To address this problem, strategic noise maps and corresponding action plans have been developed throughout Europe in recent years in response to the European Noise Directive 2002/49/EC (END), especially in populated cities. Recently, wireless acoustic sensor networks (WASNs) have started to serve as an alternative to static noise maps to monitor urban areas by gathering environmental noise data in real time. Several studies have analysed and categorized the different acoustic environments described in the END (e.g., traffic, industrial, leisure, etc.). However, most of them have only considered the dynamic evolution of the A-weighted equivalent noise levels

L_{A e q}

over different periods of time. In order to focus on the analysis of RTN solely, this paper introduces a clustering methodology to analyse and group spectro-temporal profiles of RTN collected simultaneously across an area of interest. The experiments were conducted on two acoustic databases collected during a weekday and a weekend day through WASNs deployed in the pilot areas of the LIFE+ DYNAMAP project. The results obtained show that the clustering of RTN, based on its spectro-temporal patterns, yields different solutions on weekdays and at weekends in both environments, being larger than those found in the suburban environment and lower than the number of clusters in the urban scenario. Full article

(This article belongs to the Special Issue Monitoring and Prediction of Traffic Noise in Large Urban Zones)

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18 pages, 5111 KiB

Open AccessArticle

Methods for Noise Event Detection and Assessment of the Sonic Environment by the Harmonica Index

by Rosa Ma Alsina-Pagès, Roberto Benocci, Giovanni Brambilla and Giovanni Zambon

Appl. Sci. 2021, 11(17), 8031; https://doi.org/10.3390/app11178031 - 30 Aug 2021

Cited by 6 | Viewed by 2064

Abstract

Noise annoyance depends not only on sound energy, but also on other features, such as those in its spectrum (e.g., low frequency and/or tonal components), and, over time, amplitude fluctuations, such as those observed in road, rail, or aircraft noise passages. The larger these fluctuations, the more annoying a sound is generally perceived. Many algorithms have been implemented to quantify these fluctuations and identify noise events, either by looking at transients in the sound level time history, such as exceedances above a fixed or time adaptive threshold, or focusing on the hearing perception process of such events. In this paper, four criteria to detect sound were applied to the acoustic monitoring data collected in two urban areas, namely Andorra la Vella, Principality of Andorra, and Milan, Italy. At each site, the 1 s A-weighted short L_Aeq,1s time history, 10 min long, was available for each hour from 8:00 a.m. to 7:00 p.m. The resulting 92-time histories cover a reasonable range of urban environmental noise time patterns. The considered criteria to detect noise events are based on: (i) noise levels exceeding by +3 dB the continuous equivalent level L_AeqT referred to the measurement time (T), criteria used in the definition of the Intermittency Ratio (IR) to detect noise events; (ii) noise levels exceeding by +3 dB the running continuous equivalent noise level; (iii) noise levels exceeding by +10 dB the 50th noise level percentile; (iv) progressive positive increments of noise levels greater than 10 dB from the event start time. Algorithms (iii) and (iv) appear suitable for notice-event detection; that is, those that (for their features) are clearly perceived and potentially annoy exposed people. The noise events detected by the above four algorithms were also evaluated by the available anomalous noise event detection (ANED) procedure to classify them as produced by road traffic noise or something else. Moreover, the assessment of the sonic environment by the Harmonica index was correlated with the single event level (SEL) of each event detected by the four algorithms. The threshold value of 8 for the Harmonica index, separating the “noisy” from the “very noisy” environments, corresponds to lower SEL levels for notice-events as identified by (iii) and (iv) algorithms (about 88–89 dB(A)) against those identified by (i) and (ii) criteria (92 dB(A)). Full article

(This article belongs to the Special Issue Monitoring and Prediction of Traffic Noise in Large Urban Zones)

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13 pages, 3247 KiB

Open AccessArticle

Road Traffic Noise on the Santa Marta City Tourist Route

by Dámaris A. Jiménez-Uribe, Darwin Daniels, Zoë L. Fleming and Andrés M. Vélez-Pereira

Appl. Sci. 2021, 11(16), 7196; https://doi.org/10.3390/app11167196 - 04 Aug 2021

Cited by 2 | Viewed by 2872

Abstract

The objective of this study was to determine the influence of vehicular traffic on the environmental noise levels of the Santa Marta City tourist route on the Colombian coast. An analysis of vehicle types and frequencies at various times of the day over nearly a year helped to track the main sources of environmental noise pollution. Five sampling points were selected, which were distributed over 12 km, with three classified as peripheral urban and two as suburban. The average traffic flow was 966 vehicles/h and was mainly composed of automobiles, with higher values in the peripheral urban area. The noise level was 103.3 dBA, with background and peak levels of 87.2 and 107.3 dBA, respectively. The noise level was higher during the day; however, there were no differences between weekdays and weekends. The results from the analysis of variance showed that the number of vehicles and the noise levels varied greatly according to the time of day and sampling point location. The peak and mean noise levels were correlated with the number of automobiles, buses and heavy vehicles. The mean noise levels were similar at all sample points despite the traffic flow varying, and the background noise was only correlated for automobiles (which varied much more than the heavy vehicles between day and night). Full article

(This article belongs to the Special Issue Monitoring and Prediction of Traffic Noise in Large Urban Zones)

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Review

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29 pages, 2240 KiB

Open AccessReview

Optimized Sensors Network and Dynamical Maps for Monitoring Traffic Noise in a Large Urban Zone

by Roberto Benocci, H. Eduardo Roman and Giovanni Zambon

Appl. Sci. 2021, 11(18), 8363; https://doi.org/10.3390/app11188363 - 09 Sep 2021

Cited by 6 | Viewed by 2366

Abstract

We review a Dynamap European Life project whose main scope was the design, commissioning, and actual implementation of “real-time” acoustic maps in a district of the city of Milan (District 9, or Z9, composed of about 2000 road stretches), by employing a small number of noise monitoring stations within the urban zone. Dynamap is based on the idea of finding suitable sets of roads displaying similar daily traffic noise behavior, so that one can group them together into single dynamical noise maps. The Dynamap sensor network has been built upon twenty-four monitoring stations, which have been permanently installed in appropriate locations within the pilot zone Z9, by associating four sensors to each one of the six group of roads considered. In order to decide which road stretches belong to a group, a non-acoustic parameter is used, which is obtained from a traffic flow model of the city, developed and tested over the years by the “Enviroment, Mobility and Territory Agency” of Milan (EMTA). The fundamental predictive equation of Dynamap, for the local equivalent noise level at a given site, can be built by using real-time data provided by the monitoring sensors. In addition, the corresponding contributions of six static traffic noise maps, associated with the six group of roads, are required. The static noise maps can be calculated from the Cadna noise model, based on EMTA road traffic data referred to the ‘rush-hour’ (8:00–9:00 a.m.), when the road traffic flow is maximum and the model most accurate. A further analysis of road traffic noise measurements, performed over the whole city of Milan, has provided a more accurate description of road traffic noise behavior by using a clustering approach. It is found that essentially just two mean cluster hourly noise profiles are sufficient to represent the noise profile at any site location within the zone. In order words, one can use the 24 monitoring stations data to estimate the local noise variations at a single site in real time. The different steps in the construction of the network are described in detail, and several validation tests are presented in support of the Dynamap performance, leading to an overall error of about 3 dB. The present work ends with a discussion of how to improve the design of the network further, based on the calculation of the cross-correlations between monitoring stations’ noise data. Full article

(This article belongs to the Special Issue Monitoring and Prediction of Traffic Noise in Large Urban Zones)

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