Assessment of railway noise in an urban setting

Bunn, Fernando; Zannin, Paulo Henrique Trombetta

doi:10.1016/j.apacoust.2015.10.025

Cited by 124 publications

(82 citation statements)

References 27 publications

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“…These quantities can be obtained initially from the data recorded over the few previous working days (running or moving averages) during the operation of Dynamap. In the case in which a variation δ τ g,j (t) falls outside the group A.I., the contribution of the (g, j)-th station is considered to be an outlier and it is not included in Equation (14). Then, the average is performed over the remaining active stations.…”

Section: Starting Operations Of Dynamap From Four Monitoring Stationsmentioning

confidence: 99%

“…. , N 2 ), such that N 1 + N 2 = 24), we need to rearrange the variations obtained from Equation (14) according to the indices C 1,k1 and C 2,k2 , which we denote as δ τ C1,k1 (t) and δ τ C2,k2 (t) within each cluster, C 1 and C 2 . Then, we calculate the mean variations, δ τ C1 (t) and δ τ C2 (t), for each cluster according to,…”

Section: Clustering Of the 24 Recording Stationsmentioning

confidence: 99%

“…In this context, the END's prescription for noise maps and action plans, impairment [5][6][7], and hypertension ischemic heart disease [8][9][10]. In this context, the END's prescription for noise maps and action plans, have prompted many efforts by the member states and the scientific community to high level study and propose new mitigation systems for the main sources of noise: road traffic [11,12], railway traffic [13,14], airports [15,16], industrial centers [17,18], plus other sources recognized as highly annoying [19,20]. In particular, mitigation measures in urban context need to be identified according to a realistic picture of noise distribution over urban areas.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring and Prediction of Traffic Noise in Large Urban Areas

et al. 2018

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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.

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Section: Starting Operations Of Dynamap From Four Monitoring Stationsmentioning

confidence: 99%

Section: Clustering Of the 24 Recording Stationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Monitoring and Prediction of Traffic Noise in Large Urban Areas

et al. 2018

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“…Since such a monitoring is expensive and it is not possible to install instruments everywhere in the cities, the adoption of predictive models is common in order to help policy makers in environmental protection decisions. For instance, in [8][9][10][11][12][13][14] some of the different approaches to predict the noise produced by trains and railways are presented. Road traffic noise in urban areas has a very large literature, with a great variety of methods: the authors contribution to this topic is partially presented in [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

A hybrid predictive model for acoustic noise in urban areas based on time series analysis and artificial neural network

Guarnaccia¹,

Quartieri²,

Tepedino³

2017

AIP Conference Proceedings

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Abstract. The dangerous effect of noise on human health is well known. Both the auditory and non-auditory effects are largely documented in literature, and represent an important hazard in human activities. Particular care is devoted to road traffic noise, since it is growing according to the growth of residential, industrial and commercial areas. For these reasons, it is important to develop effective models able to predict the noise in a certain area. In this paper, a hybrid predictive model is presented. The model is based on the mixing of two different approach: the Time Series Analysis (TSA) and the Artificial Neural Network (ANN). The TSA model is based on the evaluation of trend and seasonality in the data, while the ANN model is based on the capacity of the network to "learn" the behavior of the data. The mixed approach will consist in the evaluation of noise levels by means of TSA and, once the differences (residuals) between TSA estimations and observed data have been calculated, in the training of a ANN on the residuals. This hybrid model will exploit interesting features and results, with a significant variation related to the number of steps forward in the prediction. It will be shown that the best results, in terms of prediction, are achieved predicting one step ahead in the future. Anyway, a 7 days prediction can be performed, with a slightly greater error, but offering a larger range of prediction, with respect to the single day ahead predictive model.

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“…High and dangerous acoustic levels are mainly caused by anthropic activities, in particular vehicular traffic and other transportation infrastructures. Therefore, it is clear that it is useful to implement analytical models that can provide a reliable assessment of the levels of pollution (see for instance [3][4][5][6][7][8][9][10][11][12][13]). If a reliable forecast is available, it is possible to limit the overcoming of certain levels of pollution by mitigation measures, also acting on the sources, before physical agents can affect the population.…”

Section: Introductionmentioning

confidence: 99%

Development of Seasonal ARIMA Models for Traffic Noise Forecasting

et al. 2017

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Abstract. In this paper, a time series analysis approach is adopted to monitor and predict a traffic noise levels dataset, measured in a site of Messina, Italy. In general, acoustical noise shows a high prediction complexity, since its slope is strongly related to the variability of the sources and to intrinsic randomness. In the analysed site the predominant source is road traffic, that has a periodic and non-stationary behaviour. The study of the time evolution of this hazardous agent is very useful to assess the impact to human health and activities. The time series models adopted in this paper are of the stochastic seasonal ARIMA class; these types of model are based on the strong periodicity registered in the acoustical equivalent levels. The observed periodicity is related to the highly variability of urban traffic in the different days of the week. Three different seasonal ARIMA models are proposed and calibrated on a rich dataset of 800 sound level measurements. The predictive capabilities of these techniques are encouraging. The implemented models show a good forecasting performances in terms of low residuals, i.e. difference between observed and estimated noise values. The residuals are analysed by means of statistical indexes, plots and tests.

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Assessment of railway noise in an urban setting

Cited by 124 publications

References 27 publications

Monitoring and Prediction of Traffic Noise in Large Urban Areas

Monitoring and Prediction of Traffic Noise in Large Urban Areas

A hybrid predictive model for acoustic noise in urban areas based on time series analysis and artificial neural network

Development of Seasonal ARIMA Models for Traffic Noise Forecasting

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