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Objective: Noise indicator Lden is used in Health Risk Assessment (HRA) to estimate noise effects using dose-effect relationships. Determination of its value is often impossible due to the unavailability of noise data in the evening. The aim of this paper is to propose estimation methods for noise indicator Lden based on Ldn indicator for road traffic in the conditions of the Czech Republic and to determine the uncertainties of this estimation.Methodology: A total of 217 individual 24-hour road traffic noise measurements were analysed. They were obtained within the framework of the activities of Czech National Institute of Public Health and Public Health Institute Ostrava. The median of calculated values of the difference between noise indicators Lden and Ldn for individual measuring (the coefficient k) is used to estimate indicator Lden according to the formula: Lden ≅ Ldn + k.Results: The coefficient for the whole data set k = 0.6 and subcoefficients k1, k2 and k3 depending on the difference LAeq,16h - LAeq,8h were calculated. The uncertainties of the estimates are expressed by 95% confidence intervals.Conclusion: It is recommended to estimate noise indicator Lden for road traffic by adding coefficient k = 0.6 to the values of indicator Ldn, or alternatively coefficients k1 = 0.5 (for LAeq,16h - LAeq,8h < 5 dB), or k3 = 0.7 (for LAeq,16h - LAeq,8h ≥ 8 dB) can also be used. Coefficient k2 (for LAeq,16h - LAeq,8h between 5 and 7,9 dB) is identical to coefficient k for the whole data set. The estimate can only be used after an evaluation of the local situation if a smooth pattern of sound pressure levels is expected during 24 hours with the decrease of daytime values towards night-time values and without fluctuations due to local impacts. [ABSTRACT FROM AUTHOR]

Low-noise surfaces have become a common mitigation action in the last decade, so much so that different methods for feature extraction have been established to evaluate their efficacy. Among these, the Close Proximity Index (CPX) evaluates the noise emissions by means of multiple runs at different speeds performed with a vehicle equipped with a reference tire and with acoustic sensors close to the wheel. However, signals acquired with CPX make it source oriented, and the analysis does not consider the real traffic flow of the studied site for a receiver-oriented approach. These aspects are remedied by Statistical Pass-By (SPB), a method based on sensor feature extraction with live detection of events; noise and speed acquisitions are performed at the roadside in real case scenarios. Unfortunately, the specific SPB requirements for its measurement setup do not allow an evaluation in urban context unless a special setup is used, but this may alter the acoustical context in which the measurement was performed. The present paper illustrates the testing and validation of a method named Urban Pass-By (U-SPB), developed during the LIFE NEREiDE project. U-SPB originates from standard SPB, exploits unattended measurements and develops an in-lab feature detection and extraction procedure. The U-SPB extends the evaluation in terms of before/after data comparison of the efficiency of low-noise laying in an urban context while combining the estimation of long-term noise levels and traffic parameters for other environmental noise purposes, such as noise mapping and action planning. [ABSTRACT FROM AUTHOR]

Predicting the status of particulate air pollution is extremely important in terms of preventing possible vascular and lung diseases, improving people's quality of life and, of course, actively counteracting pollution magnification. Hence, there is great interest in developing methods for pollution prediction. In recent years, the importance of methods based on classical and more advanced neural networks is increasing. However, it is not so simple to determine a good and universal method due to the complexity and multiplicity of measurement data. This paper presents an approach based on Deep Learning networks, which does not use Bayesian sub-predictors. These sub-predictors are used to marginalize the importance of some data part from multisensory platforms. In other words—to filter out noise and mismeasurements before the actual processing with neural networks. The presented results shows the applied data feature extraction method, which is embedded in the proposed algorithm, allows for such feature clustering. It allows for more effective prediction of future air pollution levels (accuracy—92.13%). The prediction results shows that, besides using standard measurements of temperature, humidity, wind parameters and illumination, it is possible to improve the performance of the predictor by including the measurement of traffic noise (Accuracy—94.61%). [ABSTRACT FROM AUTHOR]

The existing traffic noise prediction models in road intersections relate mainly to the typical solutions of intersection geometry and traffic organisation. There are no models for large and more complex intersections such as signalised roundabouts. This paper presents the results of studies on the development of a traffic noise prediction model for this type of intersection. The model was developed using a multiple regression method based on the results of field measurements of traffic parameters and noise levels in the vicinity of signalised roundabouts in Poland. The obtained model consists of two groups of variables affecting noise levels at the intersection. The first group determines in detail the inf luence of traffic and geometry of the closest entry. The second group shows the inf luence of more distant noise sources (traffic at the three remaining entries of the intersection) and the inf luence of the dimensions of the entire intersection. The developed model was verified through additional field measurements, as well as compared to the results of two methods of traffic noise prediction: the French 'NMPB-Routes-2008' and the German 'RLS-90'. The obtained results confirmed a higher accuracy of calculations performed using the developed model in the range of: -1.2 dB ÷ +1.0 dB, while the 'NMPB-Routes-2008' and 'RLS-90' calculate precision were respectively: -2.8 dB ÷ +1.3 dB, and + 0.8 dB ÷ +5.2 dB. Therefore, the developed model allows for a more accurate prediction of noise levels in the vicinity of signalised roundabouts in a f lat terrain without buildings and noise barriers. [ABSTRACT FROM AUTHOR]

Dynamap (DYNamic Acoustic MAPping) is an European Life project whose main scope is to implement "real-time" acoustic maps in a district of the city of Milan. Dynamap is based on the idea of finding a suitable sets of roads displaying similar traffic noise behavior so that one can group them together into single noise maps. Twenty-four monitoring sensors have been installed permanently in appropriate locations within the pilot zone (District 9) to obtain six dynamic noise maps. The project has come to its "Test and Fault Analysis" phase. A set of new traffic noise and traffic flow measurements have been recorded to shed light on the reliability of Dynamap prediction. Besides the expected error due to statistical fluctuations of traffic noise, our preliminary analysis show a systematic error inherent to the adopted traffic flow model, which is an essential ingredient of the whole approach. By correlating the traffic noise prediction error to the one due to the traffic flow model, we succeed in improving the Dynamap prediction thus limiting the overall error to within about 3 dB. We suggest that Dynamap scheme can be applied to large urban conglomerates in general, thus becoming a useful tool for monitoring traffic noise accurately and efficiently in large cities. [ABSTRACT FROM AUTHOR]

Introduction: With the development of transportation system and the economy, the rapidly increasing number of automobiles brings the associated problem of road traffic noise, especially in metropolitan and densely populated high-rise cities like Hong Kong. In Hong Kong, approximately one million people are affected by severe road traffic noise. Excessive noise exposure is hazardous to the health and wellbeing of people and therefore has drawn progressively more attention in Hong Kong. The Calculation of Road Traffic Noise (CRTN) has been adopted as the sole tool to evaluate road traffic noise in the form of descriptor LA10. The accuracy and suitability of the CRTN method for predicting road traffic noise in Hong Kong were evaluated in this study by comparing the prediction results and measured traffic noise levels. The results show that the CRTN method was able to provide adequate predictions with correlation coefficients of 0.8032 and 0.7626 between the predicted and measured LA10 for 2007 and 2017 respectively. The predicted traffic noise levels on different floors of seven selected residential buildings in 2017 were compared with those predictions for the same buildings in 2007. The worsening traffic noise exposure in these residential buildings was analysed and some suggestions and counter-measures to alleviate the traffic noise problems are put forward. Since the situation of Hong Kong is an example of what may happen in other cities, the present longitudinal study of the road traffic noise in Hong Kong hopes to contribute to a better urban acoustic environment worldwide.Context: Excessive noise exposure is hazardous to the health and wellbeing of people and therefore has drawn progressively more attention in Hong Kong. The urban road traffic noise exposure of residential buildings in Hong Kong over the past decade has been analysed.Aims: This study aims to assess the road traffic noise exposure of residential buildings over the past decade.Settings and Design: Measurements of traffic noise levels at some selected residential buildings were first conducted in 2007, and then repeated at the same buildings in 2017.Material and Methods: The CRTN was adopted to predict the traffic noise levels based on the recorded traffic flow data.Results: The exposure of these buildings to road traffic noise is higher in 2017 than in 2007. The study illustrates that the deterioration of the urban acoustic environment may not be caused by an increased total number of vehicles, but that heavy vehicles are dominantly responsible for the increased traffic noise levels. Restriction of vehicle velocity for urban street canyons is useless for road traffic noise control.Conclusions: This study shows the deterioration of traffic noise levels is mainly due to the increased heavy vehicles instead of the increased total number of vehicles. The alleviation of traffic noise levels by velocity restriction may not be obvious for urban street canyons and may only work with a certain velocity range. [ABSTRACT FROM AUTHOR]

Exposure to high levels of road traffic noise at the most exposed building facade is increasing, both due to urbanization and due to overall traffic increase. This study investigated how different noise reduction measures would influence the noise exposure on a city-wide scale in Gothenburg, a city in Sweden with approximately 550,000 inhabitants. Noise exposure was estimated under several different scenarios for the period 2015–2035, using the standardized Nordic noise prediction method together with traffic flow measurements and population statistics. The scenarios were based on reducing speed limits, reducing traffic flows, introducing more electrically powered vehicles and introducing low-noise tires and pavements. The most effective measures were introducing low-noise tires or pavements, which in comparison to business as usual produced between 13% and 29% reduction in the number of inhabitants exposed above 55 dB equivalent level. [ABSTRACT FROM AUTHOR]

Traffic noise exposure within a city varies over time and space. In this study, we developed a modified noise calculation method and used this method together with population and traffic data to estimate the time trend of noise exposure for the population in Gothenburg, Sweden, from 1975 to 2010. The noise calculation method was based on the standard Nordic method for road traffic noise with modifications using area-level statistics for population and building structures instead of precise geocoding of each inhabitant. Noise emission per vehicle was assumed to be constant over the period. The results show an increase in noise exposure over time. The number of inhabitants exposed at an equivalent level above 55 dB increased from 93000 to 146000 inhabitants between 1975 and 2010, and the percentage of the population exposed at this level increased from 22% to 29% over the same period. Traffic increase (1.4% per year) and population increase/concentration (0.50% per year) were approximately equally important factors behind this increase in exposure. [ABSTRACT FROM AUTHOR]

Sound unpleasantness due to urban road traffic at crossroads was investigated through a listening test performed in a laboratory environment. Thirty-two sound sequences were created. Four factors were studied: sound level, type of crossroads, traffic density and traffic composition. The results showed that sound unpleasantness was mainly influenced by sound level. Decreasing traffic density by a factor of 2 also reduced sound unpleasantness. The roundabout was judged less unpleasant than the crossroads with traffic lights, when traffic density was high. This outcome may be explained by differences in traffic flow dynamics between the crossroads with traffic lights and the roundabout: on the one hand, for the crossroads with traffic lights, the traffic flow resembled a constant pulsed flow, with alternate cycles of acceleration/deceleration; on the other hand, for the roundabout, the traffic flow was more similar to a constant fluid flow, with less marked cycles of acceleration/deceleration. For the roundabout only, sound unpleasantness increased with the presence of heavy vehicles (or buses). Actually, sound sequences with only light vehicles were rated less unpleasant for the roundabout than for the crossroads with traffic lights. This could be due to differences in their spectral pattern. In particular, for traffic-light scenarios, higher levels within the frequency range [400, 3000] Hz occurred when vehicles passed by, in connection with higher vehicle speeds, and higher levels in the high frequencies (around 16 kHz) occurred more often when vehicles stopped, indicating the presence of braking noise with a shrill character. [ABSTRACT FROM AUTHOR]

Abstract: The paper proposes a frequency spectrum analysis approach to improve measurements of traffic oscillation properties (e.g., periodicity, magnitude) from field data. The approach builds on standard signal processing techniques to effectively distinguish useful oscillation information from noise and nonstationary traffic trends. Compared with conventional time-domain methods, the proposed methodology systematically provides a range of information on oscillation properties. This paper also shows how to estimate oscillations experienced by drivers using detector data. Applications to real-world data from two sites show that the dominant oscillation period remains relatively invariant at each site when an oscillation propagates. Although the average oscillation periods displayed in detector data significantly vary across sites, the range of oscillations experienced by drivers are found to be more consistent. [Copyright &y& Elsevier]

The objective of this study is to develop an empirical noise prediction model for the evaluation of equivalent noise levels (Leq) under interrupted traffic flow conditions. A new factor tendency to blow horn (AH) was introduced in the conventional federal highway administrative noise prediction (FHWA) model and a comparative study was made between FHWA and modified FHWA models to evaluate the best suitability of the model. Monitoring and modeling of Leq were carried out at four selected intersections of Jaipur city. After comparison of the results, it was found that the modified FHWA model could be satisfactorily applied for Indian conditions as it gives acceptable results with a deviation of ±3 dB (A). In addition, statistical analysis of the data comprising measured and estimated values shows a good agreement. Hence, the modified FHWA traffic noise prediction model can be applied to the cities having similar traffic conditions as in Jaipur city. [ABSTRACT FROM AUTHOR]

• Daily noise indicators are inhomogeneous with regard to annual categorization. • Non-representative sample results in insufficient coverage probability. • For a biased sample, the coverage probability is worse for larger samples. This paper concerns the representativeness of a measurement sample for traffic noise measurements. On the basis of data coming from a fourteen-year-long constant noise monitoring in Kraków, there are statistically significant differences between noise levels of different categories across the year with regard to seasons, days of the week, months and working days vs. holidays and Sundays. These differences were proven to be substantive. When the measurement sample is drawn from a group of the days with an average sound level even slightly different from the global average, the coverage probability decreases by several to several dozen percent; the larger the sample size, the larger the decrease. Therefore, the commonly used methods, such as taking measurements for a few subsequent days or drawing the measuring days from a short time interval, result in a failure to meet the legal requirement for covering the actual value with a 95% uncertainty interval. [ABSTRACT FROM AUTHOR]

One of the enabling technologies for ITS is traffic flow measurement. Since current traffic flow measurement relies on local sensors, the development of image sensors that can treat information over wide areas has been undertaken by a number of organizations. Similarly, development of acoustic sensors is desirable, although no practical products have been realized. Few research examples on acoustic sensors exist. Recently, research on probe vehicles, in which ambient information is collected by on-board sensors, has been carried out extensively. Improvement of sensing technologies using mobile sensors is highly desirable. The present paper reports the results of traffic flow measurement that was conducted by counting the number of oncoming vehicles with on-board mobile acoustic sensors. By means of the discrete wavelet transform, the road traffic environment noise is divided into five levels. The component of each level is compared with the noise without oncoming vehicles. The variations are represented as the vertices of a pentagon. The oncoming traffic situation is judged by the change in the area of the pentagon. Experiments show that it is possible to count the number of oncoming vehicles with a recognition rate of 75.5%. Future prospects are discussed. © 2005 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 88(11): 1–10, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.20174 [ABSTRACT FROM AUTHOR]

Noise exposure can affect sleep, health and cognitive performance, and it disproportionately affects communities of color. This study has the objective of evaluating both conventional and supplemental noise metrics in a community noise survey examining Southwest Detroit, Michigan, a densely populated and industrialized area with extensive truck traffic on residential streets. Sound pressure level (SPL) monitors were deployed at 21 residential sites within 900 m of a major interstate highway. With assistance from youth volunteers, continuous SPL measurements were obtained for 1.5–7 days at each site, and short-term vehicle counts on local roads were recorded. We calculated conventional noise metrics, including the day-evening-night average sound level L DEN and the 90th percentile 1-hr maximum L 10 (h), and evaluated the effect of distance from highways, traffic volume, time-of-day, and other factors. Supplemental metrics potentially appropriate for intermittent traffic noise were calculated, including fraction of time over specific SPL thresholds using a new metric called F DEN , which is the fraction of time over 60, 65 and 70 dB during night, evening and daytime periods, respectively, and a peak noise metric called L 2P (h), which utilizes the 98th percentile SPL within time blocks to increase robustness. The conventional metrics indicated five sites that exceeded 70 dB, and the highest noise levels were found within ~50 m of truck routes, arterials and freeway ramps. The estimated impact of truck traffic ranged up to 17 dB for hourly averages and to 33 dB for 1-s peaks. The conventional metrics did not always capture short-term noise exposures, which may be especially important to annoyance and sleep issues. In addition to showing widespread exposure to traffic noise in the study community that warrants consideration of noise abatement strategies, the study demonstrates the benefits of supplemental noise metrics and community engagement in noise assessment. • A community noise survey was conducted in Detroit, Michigan at 21 residential sites. • Community members and youth assisted in making noise and traffic measurements. • Noise near truck routes, arterials and freeway ramps exceeded guideline levels. • Conventional noise metrics were supplemented with metrics for short-term noise exposure. • Traffic noise was widespread and noise abatement strategies should be considered. [ABSTRACT FROM AUTHOR]