Traffic Noise Monitoring Research Articles

Autonomous monitoring of traffic, rail, and industrial noise using acoustic vector beamformers based on 3D MEMS accelerometers

Fully unattended acoustic monitoring has been hindered by the need to automatically discriminate specific sources from overall noise levels at a measurement location, even if that noise is very close in proximity. Existing 3D microphone-based acoustic intensity or camera solutions are unwieldy and expensive for real-time deployments. A new system has been developed for autonomously monitoring noise levels in transportation and industrial settings, managed within an IoT network. Each measurement node has one or two Acoustic Real-time Event Sensors (ARES) and a beamformer algorithm using 3D MEMS accelerometer-enabled Acoustic Vector Sensing (AVS) technology. Beamforming enables the system to focus on specific areas or sources of noise, delivering more precise monitoring and identification of noise sources, useful for noise reduction efforts and compliance with noise regulations. Deploying 3D accelerometers, rather than microphone arrays, in the beamformer provides improved system performance and environmental protection, with reductions in array size, cost, and unwanted sidelobes. ARES beamformer array apertures occupy just 1 cm for a single sensor, or 13 cm for 2 sensors, and can distinguish sources in frequencies from 50 to 2 kHz with excellent angular resolution, in real-time. Example traffic and rail noise applications are presented.

Road traffic noise monitoring in a Smart City: Sensor and Model-Based approach

This paper aims to propose a novel Road Traffic Noise Model (RTNM), capable of dynamically assessing road traffic noise levels from reliable data (hourly traffic volumes and speed), supporting or replacing noise sensor networks, and addressing noise pollution concerns. RTNM is composed of two parts: i) the Vehicle Noise Specific Power model (for the assessment of the sound power level – Lw of passenger cars, considering speed and motorization information) coupled with the CNOSSOS model (for the estimation of heavy-duty vehicles’ Lw); and ii) a sound propagation model (to evaluate noise levels at the receiver point). Inputs for the RTNM are retrieved from a radar installed in Aveiro city, Portugal, while the model estimations are validated by comparing them with levels recorded by a noise sensor installed close to the radar. It was found that the resulting noise estimations are robust, with associated mean absolute percentage errors not exceeding 5.8%.

Determining the Number of Measurements and Bootstrap Samples Required to Estimate of Long-Term Noise Indicators

The minimum size of the bootstrap algorithm input parameters have been determined for estimation of long-term indicators of road traffic noise. Two independent simulation experiments have been performed for that purpose. The first experiment served to determine the impact of original random sample size, and the second to determine the impact of number of the bootstrap replications on the accuracy and uncertainty of estimation of long-term noise indicators. The inference has been carried out based on results of non-parametric statistical test at significance level α = 0.05. The simulation experiments have shown that estimation of long-term noise indicators with uncertainty below ±1 dB(A) requires all-day noise measurements during three randomly selected days during the year in a dense urban development. The maximum size of original random sample should not exceed n = 50 elements. The minimum number of bootstrap replications necessary for estimation should be B = 5000. The data used to the simulation experiments and carry out the analysis were results of continuous monitoring of road traffic noise recorded in 2009 in one of the main arteries of Krakow in Poland.

High Signal-to-Noise Ratio MEMS Noise Listener for Ship Noise Detection

Ship noise observation is of great significance to marine environment research and national defense security. Acoustic stealth technology makes a variety of ship noise significantly reduced, which is a new challenge for marine noise monitoring. However, there are few high spatial gain detection methods for low-noise ship monitoring. Therefore, a high Signal-to-Noise Ratio (SNR) MEMS noise listener for ship noise detection is developed in this paper. The listener achieves considerable gain by suppressing isotropic noise in the ocean. The working principle and posterior end signal processing method of the listener are introduced in detail. A gain of 10 dB over the sound pressure detector is obtained by detecting the standard sound source. In addition, the traffic vessel noise monitoring experiment verifies that the listener can detect the ship noise. The results show that the listener has a very broad application prospect in the field of low-noise ship observation.

Development of methodological approaches to conducting monitoring of traffic noise and its assessment using acoustic modelling methods

Introduction. At present, considerable attention is paid to the problems of noise pollution in urban and rural settlements. Transport is the main source of noise in the structure of complaints about unfavourable living conditions in dense urban areas. Material and methods. Materials for selecting the most significant criteria or grouping the objects of surveillance were in situ instrumental measurements of noise levels and the results of acoustic calculations obtained during the processing of an acoustic model. In situ measurements of noise levels were made in St. Petersburg on the territory of residential areas. The acoustic models were built using the software “Acoustics 3D”. Noise source parameters were set according to the data obtained during field measurements. Results. During the development of methodological approaches, the criteria for grouping the objects of surveillance were defined. In accordance with the selected criteria, were formed 8 surveillance groups. As a result of the measurements, the difference in noise levels between the typical points within the group was no found to exceed 2 dB. To verify the developed criteria here acoustic modelling was performed. Limitations. On the territory of St. Petersburg over the year, study included one thousand measurements. Conclusion. The developed criteria make it possible to expand the list of control objects while reducing labour costs for conducting socio-hygienic monitoring, while maintaining the objectivity of the results of the acoustic pollution levels of the territory.

Traffic noise monitoring and modelling - an overview.

Noise has emerged as a leading environmental problem and is an underestimated threat. The most significant source of noise pollution is road traffic. Road traffic noise problem has reached alarming levels. This proves the severity and necessity of mitigating the traffic noise from every delicate corner possible. Noise monitoring is required to check the noise levels and effectiveness of control methods implemented. Road traffic noise control can be exercised with the help of prediction models. This paper presents the traffic noise status of developing countries and a quantitative review and comparison of traffic noise prediction models developed by researchers for various cities. Findings suggest that most of the researchers have used regression modelling and use of evolutionary computing methods like genetic algorithm, fuzzy systems, and neural networks to develop traffic noise prediction model is lacking. The effect of many important variables affecting traffic noise like pavement type, vegetation along roads, road surface roughness, and gradient still needs to be studied. Further, studies are required to measure in vehicle noise levels on same roads to compare the noise levels tolerated by residents, road users, and the commuters; this will help in formulating traffic noise regulations.

Urban road traffic noise monitoring, mapping, modelling, and mitigation: A thematic review

Abstract This article analyses the most recent studies on urban traffic noise. About 67 relevant articles on urban road traffic noise and its mitigation strategies were preferred for a critical review. Only 5.97% of items describe how to monitor and record the noise measurement for urban roads, while 7.46% of articles enumerated urban traffic noise pollution exposure. 29.85% of articles proposed a model to evaluate noise reduction effects and predict the noise level. Also, many articles reported noise map generation and its analysis. 56.71% of articles described the noise mitigation strategies in detail, concerning noise control by green vegetation, land use planning, low noise tire and pavement material, noise reduction through façade shielding. Noise pollution standards are being breached in all areas. There is a need for the proper implementation of rules and regulations. Therefore, noise mitigation strategies such as designing noise barriers and other noise control materials are needed. Finally, it is summarized that economic and low-cost optimized noise pollution mitigation strategies like ingeniously made noise barriers, vegetation and landscaping are need of the hour for urban areas of developing countries.

Treatment of road traffic noise in EU countries with a focus on Directive 2002/49/EC

The trend of continuous increase for mobility requirements results in a progressive increase in the use of vehicles of all modes of transport, which contributes to a significant increase in noise levels, especially in urban areas. The most significant noise in urban areas is traffic noise, where road traffic contributes the most. This paper reviews the treatment of road traffic noise in the European Union with a focus on Directive 2002/49 / EC. The paper presents the basics of the mechanisms of the institutional and management framework in the field of road traffic noise monitoring in EU countries. Also, the results of road traffic noise monitoring in EU countries, indicators of population exposure to road traffic noise, as well as indicators of the impact of road traffic noise on the health of the population, were presented.

Spatio-temporal changes of road traffic noise pollution at ecoregional scale

Noise pollution is a pervasive factor that increasingly threatens natural resources and human health worldwide. In particular, large-scale changes in road networks have driven shifts in the acoustic environment of rural landscapes during the past few decades. Using sampling plots from the Spanish Landscape Monitoring System (SISPARES), 16 km2 each, we modelled the spatio-temporal changes in road traffic noise pollution in Ecoregion 1 of Spain (approximately 66,000 km2). We selected a study period that was characterised by significant changes in the size of the road network and the vehicle fleet (i.e. between 1995 and 2014) and used standard and validated acoustic computation methods for environmental noise modelling (i.e. European Directive, 2002/49/EC) within sampling plots. We then applied a multiple linear regression to expand noise modelling throughout the whole of Ecoregion 1. Our results showed that the noise level increased by 1.7 dB(A) in average per decade in approximately 65% of the territory, decreased by 1.3 dB(A) per decade in about 33%, and remained unchanged in 2%. This suggests that road traffic noise pollution levels may not grow homogeneously in large geographical areas, maybe due to the concentration of large fast traffic flows on modern motorways connecting towns. Our research exemplifies how landscape monitoring systems such as cost-effective approaches may play an important role when assessing spatio-temporal patterns and the impact of anthropogenic noise pollution at large geographical scales, and even more so in a global context of constricted resources and limited availability of historical data on traffic and environmental noise monitoring.

The urban traffic noise monitoring system based on LoRaWAN technology

Noise and air pollution are among the most important problems and challenges in urban environments. Noise can cause several problems in functioning within urban settlements and may result in a variety of health problems. Continuous monitoring, prevention, and active work on solving these problems are in the focus of research communities, local and national agencies and organizations, and many international organizations. Traffic noise on city roads has been identified as one of the major problems in urban settlements. Fortunately, it can be monitored and controlled in a way that can reduce its level and negative effects. One of the most effective and cheapest ways to monitor and control traffic noise is to use wireless sensor networks, IoT technology, and cloud-based architectures. This article presents an approach in designing urban traffic noise monitoring system based on LoRaWAN and microservices oriented architecture supported with open-source hardware and software solutions. Design of the system includes planning LoRaWAN network and development of the IoT system layered architecture. Methods for traffic routing and rerouting in urban areas, based on experiments that uses Dijkstra and Floyd–Warshall algorithms are presented. The system implementation and evaluation of the proposed routing modifications are presented in the case of the city of Zrenjanin in Serbia. Discussion of extensions and improvements of the presented system, as well as future research directions, are also presented.

Evaluation of Single Vehicle Noise Emission in Different Speed Conditions

Because of the increase of vehicles number, the evaluation and monitoring of road traffic noise assume a significant role for decreasing risks for human health and managing environmental pollution in urban areas. Noise measurements cannot be performed everywhere, or even in a large number of sites, because of high costs and time consumption. For this reasons, Road Traffic Noise predictive Models (RTNMs) can be implemented to estimate the noise levels, knowing certain parameters needed as input. This paper describes the comparison of the results, in terms of sound power level emitted by a single vehicle, obtained with the application of some Emission Models (EMs) to different simulations of driving conditions. After a preliminary comparison between the models, the evaluation of two indicators, namely the average and total source power level, will be discussed in relation to different conditions of the vehicle kinematics.

Comprehensive study of the response of inexpensive low energy wireless sensors for traffic noise monitoring

Precision noise monitoring can prove to be prohibitively expensive and labour intensive. Low cost, wireless, environmental sensor systems, referred to as motes, are being developed as alternatives. Whilst considered of lower accuracy than precision sound level meters, they can be deployed for much longer periods and monitor continuously at one minute resolution. This paper reports the performance of two commercially available motes, the eMote developed to monitor traffic and the nMote to monitor airport noise, in both controlled laboratory conditions, and deployment in the field. Under controlled conditions the performance was assessed by co-location with precision sound level meters (Brüel and Kjær Type, 2260) and exposed to coloured (‘white’ and ‘pink’) noise at different levels. Both units exhibited strong linear relationships (R2 > 0.98) for LAeq when compared to the precision instruments. The performance of three individual eMotes, in terms of similarity and variability, were broadly consistent across the tested units, though levels were systematically overestimated by approximately ±3 dBA when uncorrected. The eMote units were found to exhibit a lower detection limit of ~55 dBA, potentially limiting their application to (a) urban daytime conditions when levels generally exceed that threshold and (b) at other times and locations as screening tools for excessive noise levels, to justify more expensive precision monitoring confirming findings of Marouf et al. (2018). Further research here demonstrates that the systematic error could be corrected by an algorithm resulting in a random error of better than ±1.5dBA. Both nMote units exhibited a lower detection limit (~40dBA). One nMote was electronically calibrated at the manufacturing stage and found to slightly underestimate at lower and overestimate at higher levels. Some improvement was made by developing an algorithm giving overall measurement accuracy better than ±2dBA. The other nMote not calibrated at the manufacturing stage underestimated values at low levels. This could be corrected resulting in a random error of better than ±2.5dBA. Similar results for both Mote types were observed in the field but with more variability due to the data synchronisation, local reflection and refraction etc. and the continually varying noise characteristics of traffic. Whilst the nMote was designed for airport noise, it is considered along with the eMote suitable for monitoring traffic noise in urban areas, and demonstrates much potential to be incorporated into existing Intelligent Transport System technologies to measure, manage and control population exposure to traffic noise (e.g. as a ‘noise camera’ analogous to the existing speed camera). Given that deriving correction algorithms for motes in controlled test environments is impractical, the knowledge gained from these studies has led to modifications to the design and with electronic calibration as part of the manufacturing process measurement errors of better than ±2dBA across the range of sensitivity can be achieved. With advancements in technologies and if power consumption was not an issue, then nowadays a device could quite easily be designed with a dynamic range and linearity quite close to the precision instruments for little extra cost.

Comparison of mathematical methods and measurements of traffic noise indices in pedestrian routes

Abstract In this study, we propose an analysis of the vehicular traffic noise indices and comparison between field measurements and prediction data obtained from mathematical models. The study area consists of two pedestrians routes of a medium-sized South American city. University students use these routes in displacements between their universities and residences. We monitored twenty-eight points along the two routes, performing three daytime measurements for each point. The calculated values were obtained from two mathematical predicted models: the English model CRTN (Calculation of Road Traffic Noise) and the French model NMPB-Routes (Nouvelle Methode de Prevision de Bruit). The measurements considered two noise descriptors: the A-weighted equivalent sound level (LAeq) and the noise pollution index (Lnp). The results show that the pedestrians are exposed to excessive levels of vehicle traffic noise along these routes. However, the analysis showed that the two mathematical models achieved good similarity and high performance in the prediction potential. The CRTN model has a better performance than NMPB, proving to be useful as an auxiliary tool in the monitoring of vehicle traffic noise. Finally, we used the CRTN (LAeq) predictions to generate the map of noise pollution indices.

3-year noise monitoring and strategic noise mapping in an extended motorway.

It is well known that the quality of the acoustic environment is very often degraded by major environmental interventions such as extended motorways. Considering this, the European Union has imposed relevant legislation on its member states to monitor these environmental changes/interventions and to develop strategic noise maps and action plans, in order to protect the natural and human environment. Egnatia Odos is a major motorway that crosses northern Greece and connects the country with the main European Motorway network. In the present work, the results of an extended traffic noise measuring campaign at selected sections of the Egnatia motorway are presented, together with the strategic noise maps and calculations of the population exposure. It was concluded that, although the noise levels in the countryside were low, in the suburban areas near the city of Thessaloniki the traffic noise was reported high during both the day and night. However, the extent of the traffic noise issue was found considerably greater during night-time. The number of exceedances was rather limited, though a considerable amount of population (11.5% and 5.4% during the day and night, respectively) seems to be affected by relatively high noise levels, highlighting the importance of traffic noise monitoring.

Modelling of road traffic noise

Effective implementation of the EC Communication relating to urban mobility is dependent on adopting relevant indicators that will allow comprehensive assessment of the measures undertaken under the urban mobility plan. One of the fundamental parameters for this assessment is the equivalent sound level indicator, measured over the 24-hour period, used to evaluate the annoyance of the road traffic noise. Not only is its value important but also its variability. This paper reports the results of the analysis of traffic noise along a major road, performed using various descriptors. The data recorded continuously throughout the year by the traffic rate and noise monitoring terminal were used to construct the measurement database. Analysis of the results was performed for the period between March and June in 2011 and 2016. The road was renovated between 2013 and 2015. The noise variability assessment was conducted using classical and positional indicators. Measurement uncertainties were evaluated.

Urban Noise Recorded by Stationary Monitoring Stations

The paper presents the analysis results of equivalent sound level recorded by two road traffic noise monitoring stations. The stations were located in Kielce (an example of a medium-size town in Poland) at the roads in the town in the direction of Łódź and Lublin. The measurements were carried out through stationary stations monitoring the noise and traffic of motor vehicles. The RMS values based on A-weighted sound level were recorded every 1 s in the buffer and the results were registered every 1 min over the period of investigations. The registered data were the basis for calculating the equivalent sound level for three time intervals: from 6:00 to 18:00, from 18:00 to 22:00 and from 22:00 to 6:00. Analysis included the values of the equivalent sound level recorded for different days of the week split into 24h periods, nights, days and evenings. The data analysed included recordings from 2013. The agreement of the distribution of the variable under analysis with normal distribution was evaluated. It was demonstrated that in most cases (for both roads) there was sufficient evidence to reject the null hypothesis at the significance level of 0.05. It was noted that compared with Łódź Road, in the case of Lublin Road data, more cases were recorded for which the null hypothesis could not be rejected. Uncertainties of the equivalent sound level measurements were compared within the periods under analysis. The standard deviation, coefficient of variation, the positional coefficient of variation, the quartile deviation was proposed for performing a comparative analysis of the obtained data scattering. The investigations indicated that the recorded data varied depending on the traffic routes and time intervals. The differences concerned the values of uncertainties and coefficients of variation of the equivalent sound levels.

Developing an Innovative Urban Traffic Noise Monitoring System

The transportation policies focusing on minimizing the environmental impact aim at an improved quality of life and health of the population, the urban one in particular. Noise pollution is one of the biggest problems associated with the European urban environment at the present moment, mainly because of the ever-increasing road traffic. SUNET system (System for Urban Noise and Eco-Traffic) was designed to improve the management of the noise pollution in Cluj-Napoca and to provide up-to-date public data on a user-friendly interface. The implementation of the application over the entire municipality is highly advantageous as it creates a link between the town’s administration (the local authorities, the City Hall of Cluj-Napoca) and the user (the citizens) through this system which is dynamic, modern and compliant with the European regulations. The graphic interface uses the HTML (HyperText Markup Language) code, while the database is set up in an SQL (Structured Query Language) format and contains information about the characteristics of the system and users alike, all organized in specific tables. The need for an online urban noise pollution monitoring system, such as the SUNET one, appears to allow the provision of fairly realistic and up-to-date information and encourage the community to actively assist in the application of proper action plans and the decision-making process when it comes to the population’s exposure to high levels of noise pollution.

Variability of road traffic noise recorded by stationary monitoring stations

The paper presents the analysis results of equivalent sound level recorded by two road traffic noise monitoring stations. The stations were located in Kielce (an example of a medium-size town in Poland) at the roads out of the town in the direction of Krakow and Warszawa. The measurements were carried out through stationary stations monitoring the noise and traffic of motor vehicles. The RMS values based on A-weighted sound level were recorded every 1 s in the buffer and the results were registered every 1 min over the period of investigations. The registered data were the basis for calculating the equivalent sound level for three time intervals: from 6:00 to 18:00, from 18:00 to 22:00 and from 22:00 to 6:00. Analysis included the values of the equivalent sound level recorded for different days of the week split into 24h periods, nights, days and evenings. The data analysed included recordings from 2013. The coefficient of variation and positional variation were proposed for performing comparative analysis of the obtained data scattering. The investigations indicated that the recorded data varied depending on the traffic routes. The differences concerned the values of coefficients of variation of the equivalent sound levels.

Assessment of traffic noise levels in urban areas using different soft computing techniques.

Available traffic noise prediction models are usually based on regression analysis of experimental data, and this paper presents the application of soft computing techniques in traffic noise prediction. Two mathematical models are proposed and their predictions are compared to data collected by traffic noise monitoring in urban areas, as well as to predictions of commonly used traffic noise models. The results show that application of evolutionary algorithms and neural networks may improve process of development, as well as accuracy of traffic noise prediction.

Confidence Intervals for the Long-Term Noise Indicators Using the Kernel Density Estimator

Abstract A non-classical model of interval estimation based on the kernel density estimator is presented in this paper. This model has been compared with interval estimation algorithms of the classical (parametric) statistics assuming that the standard deviation of the population is either known or unknown. The non-classical model does not have to assume belonging of random sample to a normal distribution. A theoretical basis of the proposed model is presented as well as an example of calculation process which makes possible determining confidence intervals of the expected value of long-term noise indicators Aden and LN. The statistical analysis was carried out for 95% interval widths obtained by using each of these models. The inference of their usefulness was performed on the basis of results of non-parametric statistical tests at significance level α = 0.05. The data used to illustrate the proposed solutions and carry out the analysis were results of continuous monitoring of traffic noise recorded in 2004 in one of the main arteries of Krakow in Poland.