Annual traffic noise levels estimation based on temporal stratification

An energy-based framework for predicting vehicle noise source intensity: From energy consumption to noise

The vehicle noise source strength prediction model is a crucial component in the field of traffic noise prediction. Despite the establishment of noise source strength localized models in various countries, the theoretical underpinnings of the sound power level models within these frameworks remains unclear. This study addresses this gap by analyzing the correlation between vehicle noise and energy consumption. An energy-based source strength model framework (E-SSIM) is proposed, focusing on developing nonlinear models for basic noise level. E-SSIM is built on acoustical principles and the energy flow of vehicles, integrating noise and energy consumption through the application of multivariate regression theory, characterized by a transient or simplified mathematical framework. Furthermore, sensitivity analysis and road experiments are conducted to validate the proposed framework. The findings reveal that E-SSIM effectively integrates vehicle energy flow and principles of acoustics, thereby providing a theoretical foundation for the logarithmic mathematical structure in classical noise source strength models. The study reveals that in low-speed driving conditions (17-40 km/h), the sensitivity of noise energy to aerodynamic drag energy consumption reaches its peak. Specifically, the sensitivity of E-SSIM, as assessed by the A-weighted sound level, progressively decreases with increasing speed. On the contrary, for the Z-weighted sound level, the sensitivity initially decreases before rising again, reaching its peak stability and robustness at a speed of 23.8 km/h. E-SSIM exhibits superior precision in predicting A/Z-weighted sound pressure levels. Compared to classic logarithmic structural prediction models, the mean absolute percentage error of E-SSIM was reduced by 4.19% and 0.07%. Compared to typical models such as ASJ developed by the Acoustical Society of Japan and CNOSSOS-EU used by the European Commission, E-SSIM yielded a mean absolute percentage error reduction of 68% and 67%. Interestingly, as vehicle internal energy consumption increases, the prediction deviations of E-SSIM, ASJ, and CNOSSOS-EU gradually decrease, possibly because vehicle operating conditions approach stability. E-SSIM can utilize abundant vehicle data to develop generic models, promoting the advancement of noise prediction.

Useful tools for integrating noise maps about noises other than those of transport, infrastructures, and industrial plants in developing countries: Casework of the Aburra Valley, Colombia

The behavior of environmental noise in developing countries is conditioned by characteristics that are not only linked to transport, infrastructures, and industrial plants in the annuity (common representation in noise maps), but also to other types of sources and periodicities that can influence significantly in noise levels. For this reason, this work proposes different temporal analyzes during the annuity that can be linked to the noisy activities typical of developing tropical countries. To do this, a noise monitoring network composed of seven monitors representing different sources present in the Aburrá Valley (AV) in Colombia is analyzed with measurements of LAeq, every hour, in a period between August 2016 and July 2019. The results show that AV noise is strongly influenced by leisure activities related to high-power sound systems, different celebrations, and continuous noise from car traffic that affect the population mainly on weekends and nights. This work marks a clear path to precisely address noise pollution in the action plans of developing countries.

Algorithm for Reducing Truck Noise on Via Baltica Transport Corridors in Lithuania

The section of Via Baltica going through the territory of the Republic of Lithuania is the most traffic intensive land logistics corridor in the country. The annual transportation volume has been increasing on this road; thus, the reduction of pollution caused by vehicles has become important. If gas emissions are regulated, and carriers have to pay pollution taxes, this does not apply to noise levels. The article presents the traffic intensity in this logistics corridor, measurements of the noise level at the characteristic points, its relation to the number of vehicles passing through it and an expert evaluation of proposed methods for noise energy reduction. Environmental noise is an unwanted or harmful sound that propagates in terms of both duration and geographical coverage. Noise is associated with many human activities, but road, rail and air traffic noises have the greatest impact. Due to irrationally arranged transport network, the transit flow of freight transport crosses residential areas of the city, places of rest and recreation of the population, causing high noise levels in adjacent areas. This is the biggest problem for the urban environment. Environmental noise affects many Europeans and is therefore considered by society to be one of the biggest environmental problems. This article presents an assessment of a new traffic noise algorithm. The presented expert survey on noise energy reduction allows choosing the most appropriate method for reducing noise energy in Via Baltica transport logistics corridor. Based on the expert survey, a hierarchical table for noise energy reduction was compiled. It will allow assessing the validity of individual noise energy reduction solutions. It has become relevant for improving infrastructure of other transport corridors and choosing the most appropriate solutions to reduce vehicle noise pollution. A further application of this model can be focused on economic evaluation, forecasting of expected benefits and so on.

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

Noise pollution is one of the major urban environmental pollutions, and it is increasingly becoming a matter of crucial public concern. Monitoring and predicting environmental noise are of great significance for the prevention and control of noise pollution. With the advent of the Internet of Things (IoT) technology, urban noise monitoring is emerging in the direction of a small interval, long time, and large data amount, which is difficult to model and predict with traditional methods. In this study, an IoT-based noise monitoring system was deployed to acquire the environmental noise data, and a two-layer long short-term memory (LSTM) network was proposed for the prediction of environmental noise under the condition of large data volume. The optimal hyperparameters were selected through testing, and the raw data sets were processed. The urban environmental noise was predicted at time intervals of 1 s, 1 min, 10 min, and 30 min, and their performances were compared with three classic predictive models: random walk (RW), stacked autoencoder (SAE), and support vector machine (SVM). The proposed model outperforms the other three existing classic methods. The time interval of the data set has a close connection with the performance of all models. The results revealed that the LSTM network could reflect changes in noise levels within one day and has good prediction accuracy. Impacts of monitoring point location on prediction results and recommendations for environmental noise management were also discussed in this paper.

Characteristics and evaluation of urban soundscapes worthy of preservation

Improvement of the sound environment is essential for improving the overall quality of the urban environment and for reflecting the characteristics of a city. Unfortunately, some soundscapes in cities are gradually being destroyed by the rapid development. Therefore, it would appear that the preservation of soundscapes is as necessary and crucial as noise control in cities. In this paper two field studies were carried out at two stages to explore the types and characteristics of urban soundscapes that should be preserved, based on the example of Tianjin, China. At stage one, 2504 respondents participated in social surveys to recommend the soundscapes worthy of preservation in the city. At stage two, a group soundscape walk with twenty-three participants was carried out to explore the characteristics of soundscapes worthy of preservation suggested by the respondents at the first stage. The following results were obtained: (1) regarding the attitude towards the urgency of soundscape preservation, 79% of the respondents thought it was urgent or very urgent, with an average score of 4.04 (five-point numeric scale was used and five meant very urgent). (2) The majority of soundscapes worthy of preservation were located in urban parks and historic districts with respective proportions of 66% and 20%. The top three sound source categories of these soundscapes were social/communal sounds (talking, bells, laughter, and sounds from human activities), animal sounds (birdsong and sounds from non-domesticated animals) and water sounds. (3) Relaxation, vibrancy, representativeness, strength, and richness were principal characteristics of the soundscapes worthy of preservation. (4) Based on their characteristics, these soundscapes were classified into five clusters using hierarchical cluster analysis (HCA). They were clusters of relaxing nature soundscape, vibrant nature soundscape, vibrant human activity soundscape, culture-related soundscape and traditional soundscape. The dominant characteristic of each cluster was driven by its sound mark rather than the dominant sound.