Annual traffic noise levels estimation based on temporal stratification

Abstract

This paper proposes a temporal sampling strategy that increases the accuracy of long-term noise level estimation and allows to establish the estimation error according to the number of sampled days. Days of the week are stratified into working days and weekend days. This research shows how to use measurements of L on working days to estimate the corresponding values for weekend days. This is possible because working days have higher noise levels and less variability than weekend days. The improvement in accuracy allows for a reduction in the number of required sampled days compared to taking samples randomly, which would help to reduce the uncertainty in environmental noise assessment. As a reference, to obtain a 90% confidence interval of ±1 dB for L, the proposed sampling strategy reduces the required measurement days by more than 38%. For L, the reduction is close to 18% of the total number of days. The proposed strategy could be adapted to different environments by simply changing a few parameters.

Characteristics and evaluation of urban soundscapes worthy of preservation

“…Improving the sound environment is an essential way to improve the overall urban environmental quality, and to reflect the characteristics of a city (de la Prida et al, 2019;Kang, 2006). However, with the rapid process of urbanisation, the urban sound environment is deteriorating (Hong and Jeon, 2014;Iglesias et al, 2014;Quintero et al, 2018). For example, noise complaints in China accounted for 39.2% of the total number of environmental complaints in 2013, and it increased to 50% in 2017 (Han et al, 2018).…”

Time-Series Prediction of Environmental Noise for Urban IoT Based on Long Short-Term Memory Recurrent Neural Network

“…IoT must overcome some challenges to extract new insights from data [49]. In previous studies [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36], environmental noise prediction mainly focused on the spatial propagation of noise, and there were few studies focused on the variation of noise in short-term. The noise data set used in this study is in seconds, which is more random.…”

“…The research by Frank et al [30] shows that combining the rules or patterns mined in the monitoring data with the acoustic theoretical calculation model can effectively improve the prediction accuracy of noise. In terms of reflecting regional noise levels, sometimes sampling strategies are put forward based on considerations of saving resources and improving data acquisition efficiency [31]. Giovanni et al [32] found that considering saving the time cost and the accuracy of the results, a non-continuous 5-7 days observation of noise is reasonable for long-term noise prediction.…”

Temporal and spatial stratification for the estimation of nocturnal long-term noise levels

“…There are many other studies also indicating that land use can affect urban noise level, such as (Doygun and Kuşat Gurun, 2008, Tsai et al, 2009, Wang et al, 2016, Han et al, 2018, specially in case of leisure activities for the night period (Barrigón et al, 2005b,a, Romeu et al, 2010, Ballesteros et al, 2015, Ottoz et al, 2018. Concretely, a spatial stratification of streets based on traffic characteristics fails to improve the estimation of night levels (Quintero et al, 2018) as it does for day and evening periods, probably due to the presence of leisure activities nearby the streets.…”

“…Random sampling is taken in most cases as a benchmark strategy (Gaja et al, 2003, Makarewicz and Galuszka, 2011, Can et al, 2011, Barrigón and Prieto, 2014, Prieto et al, 2016, Hueso et al, 2017. Some procedures related to extrapolate short time measurements to long-term values are shown in (Safeer et al, 1972, Gaja et al, 2003, Ng and Tang, 2008, Romeu et al, 2011, Brocolini et al, 2013, Prieto and Barrigón, 2015 and some works suggest that a spatial stratification based on street traffic (Prieto et al, 2016, Quintero et al, 2018 or the role of the streets within the city regarding traffic distrubution (Barrigón et al, 2005b, Romeu et al, 2006 led to a reduction of sampling points.…”