The LIFE DYNAMAP project: Towards a procedure for dynamic noise mapping in urban areas

Zambon, G; Benocci, R.; Bisceglie, A; Roman, H. E.; Bellucci, Patrizia

doi:10.1016/j.apacoust.2016.10.022

Cited by 67 publications

(63 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We will use also the notation δ C1 (h) and δ C2 (h) for the mean equivalent noise levels of Clusters 1 and 2 as a function of hour h. Cluster 1 has 56 elements and Cluster 2 has 37 components. Details on the measurements and analysis related to this figure are reported in [29,33,39].…”

Section: The Non-acoustic Parameter and Its Distribution Functionmentioning

confidence: 99%

“…The analysis of hourly traffic noise measurements from the 93 sites distributed over the city of Milan have been performed using standard clustering techniques which has been described extensively in [29,33,39]. The analysis resulted in the identification of two clusters, here denoted as Clusters 1 and 2, which display similar time dependences.…”

Section: The Equivalent Noise Level Clusteringmentioning

confidence: 99%

“…Dynamap is a co-financed project by the European Commission through the Life+ 2013 program designed to produce dynamic noise maps within a large urban area. The method is described for a given zone in the city of Milan (Zone 9, illustrated in Figure 1), consisting of about 2000 road arches, using 24 continuous measuring stations [29]. The idea that noise emission from a street generally depends on its activity, its use in the urban context rather than strictly from its geometric characteristics, suggested a different approach to monitoring sampling based on stratified sampling [30,31].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Monitoring and Prediction of Traffic Noise in Large Urban Areas

et al. 2018

Self Cite

View full text Add to dashboard Cite

Featured Application: The method discussed in this paper has applications in the context of predicting traffic noise in large urban environments. The system designed by the authors provides an accurate description of traffic noise by relying on measurements of road noise from few monitoring stations appropriately distributed over the zone of interest. A prescription is given of how to choose the location of the noise stations.Abstract: Dynamap, a co-financed project by the European Commission through the Life+ 2013 program, aims at developing a dynamic approach for noise mapping that is capable of updating environmental noise levels through a direct link with a limited number of noise monitoring terminals. Dynamap is based on the idea of finding a suitable set of roads that display similar traffic noise behavior (temporal noise profile over an entire day) so that one can group them together into a single noise map. Each map thus represents a group of road stretches whose traffic noise will be updated periodically, typically every five minutes during daily hours and every hour during night. The information regarding traffic noise will be taken continuously from a small number of monitoring stations (typically 24) appropriately distributed over the urban zone of interest. To achieve this goal, we have performed a detailed analysis of traffic noise data, recorded every second from 93 monitoring stations randomly distributed over the entire urban area of the City of Milan. Our results are presented for a restricted area, the urban Zone 9 of Milan. We have separated the entire set of (about 2000) stretches into six groups, each one represented by a noise map, and gave a prescription for the locations of the future 24 monitoring stations. From our analysis, it is estimated that the mean overall error for each group of stretches (noise map), averaged over the 24 h, is about 2 dB.

show abstract

Section: The Non-acoustic Parameter and Its Distribution Functionmentioning

confidence: 99%

Section: The Equivalent Noise Level Clusteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Monitoring and Prediction of Traffic Noise in Large Urban Areas

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…Pavement plays a key-role in noise emitted by road, as recently studied by many authors aiming to integrate noise reduction with green economy by recycling rubber from old tires into asphalts (rubber asphalts) [5][6][7]. Preventing is also mitigating, thus, innovative solutions, like real time monitoring, are actually studied using a wireless sensor network [8,9].On the other hand, road infrastructures companies are obliged to perform environmental impact analysis, including noise monitoring and estimation. In addition, when critical situations are highlighted, action plans must be performed, according to the END [2] and to the national regulations of each country.In any case, measurements are expensive and cannot be performed all over large areas, thus, road traffic noise predictive models (RTNMs) can be adopted to assess noise produced by vehicles.…”

mentioning

confidence: 99%

EAgLE: Equivalent Acoustic Level Estimator Proposal

Guarnaccia

2020

Sensors

View full text Add to dashboard Cite

Road infrastructures represent a key point in the development of smart cities. In any case, the environmental impact of road traffic should be carefully assessed. Acoustic noise is one of the most important issues to be monitored by means of sound level measurements. When a large measurement campaign is not possible, road traffic noise predictive models (RTNMs) can be used. Standard RTNMs present in literature usually require in input several information about the traffic, such as flows of vehicles, percentage of heavy vehicles, average speed, etc. Many times, the lack of information about this large set of inputs is a limitation to the application of predictive models on a large scale. In this paper, a new methodology, easy to be implemented in a sensor concept, based on video processing and object detection tools, is proposed: the Equivalent Acoustic Level Estimator (EAgLE). The input parameters of EAgLE are detected analyzing video images of the area under study. Once the number of vehicles, the typology (light or heavy vehicle), and the speeds are recorded, the sound power level of each vehicle is computed, according to the EU recommended standard model (CNOSSOS-EU), and the Sound Exposure Level (SEL) of each transit is estimated at the receiver. Finally, summing up the contributions of all the vehicles, the continuous equivalent level, Leq, on a given time range can be assessed. A preliminary test of the EAgLE technique is proposed in this paper on two sample measurements performed in proximity of an Italian highway. The results will show excellent performances in terms of agreement with the measured Leq and comparing with other RTNMs. These satisfying results, once confirmed by a larger validation test, will open the way to the development of a dedicated sensor, embedding the EAgLE model, with possible interesting applications in smart cities and road infrastructures monitoring. These sites, in fact, are often equipped (or can be equipped) with a network of monitoring video cameras for safety purposes or for fining/tolling, that, once the model is properly calibrated and validated, can be turned in a large scale network of noise estimators.

show abstract

“…Sound maps based on measurements can help to improve sound mapping (Zambon et al 2017;Asensio 34 2017; Hong and Jeon 2017; Harman, Koseoglu, and Yigit 2016). The recent development of small and 35 autonomous acoustic sensors contributes to this movement, and network-based sound monitoring systems 36 are being deployed in an increasing number of cities over the world, using either high-quality or low-cost 37 micropones (Mydlarz, Salamon, and Bello 2017; Asensio 2017; Sevillano et al 2016).…”

mentioning

confidence: 99%

Kriging-based spatial interpolation from measurements for sound level mapping in urban areas

Aumond

Can

Mallet³

et al. 2018

The Journal of the Acoustical Society of America

View full text Add to dashboard Cite

13Network-based sound monitoring systems are deployed in various cities over the world and mobile applications 14 allowing participatory sensing are now common. Nevertheless, the sparseness of the collected measurements, either 15 in space or in time, complicates the production of sound maps. This paper describes the results of a measurement 16campaign that has been conducted in order to test different spatial interpolation strategies for producing sound maps. 17Mobile measurements have been performed while walking multiple times in every street of the XIII'th district of

show abstract

The LIFE DYNAMAP project: Towards a procedure for dynamic noise mapping in urban areas

Cited by 67 publications

References 16 publications

Monitoring and Prediction of Traffic Noise in Large Urban Areas

Monitoring and Prediction of Traffic Noise in Large Urban Areas

EAgLE: Equivalent Acoustic Level Estimator Proposal

Kriging-based spatial interpolation from measurements for sound level mapping in urban areas

Contact Info

Product

Resources

About