Analysis Of Traffic Noise Research Articles

Case-study analysis of traffic noise at Vesterbrogade, Aarhus, Denmark

This paper analyses the livability, concerning traffic noise, in apartment buildings along one of the most heavily trafficked streets in Aarhus, Denmark. The case-study site is analyzed with noise prediction models and different improvements on the sound environment are proposed through qualitative aspects. The analyses are conducted using two different noise prediction models, opeNoise in QGIS and SoundPLAN. OpeNoise uses the noise prediction method CNOSSOS and SoundPLAN uses Nord2000. Noise maps 1,5 m above the terrain and facade noise levels are calculated. The results show noise levels on the facade far above WHO's limit, of . The noise pollution in the open space next to the roads also shows noise levels far above the recommendations, while the inner courtyard shows acceptable noise levels. Several subjective improvements of the sound environment are proposed, such as vegetation between the road and the building. Furthermore, different designs were proposed to reduce the amount of noise-polluted facades. This research work emphasizes the importance of considering qualitative parameters simultaneously with the noise prediction models.

Noise Pollution in Urban Areas : Measurement and Analysis of Traffic Noise Impacts on Health and Education Services in Bandung City

Noise Pollution in Urban Areas : Measurement and Analysis of Traffic Noise Impacts on Health and Education Services in Bandung City

Traffic Projections in Noise Analysis

Traffic volumes are an integral part of any highway traffic noise analysis. The defining procedure for federal regulation for highway transportation noise analysis is 23 CFR 772. Section 23 CFR 772.9 Traffic Noise Prediction (d) states "In predicting noise levels and assessing noise impacts, traffic characteristics that would yield the worst traffic noise impact for the design year shall be used." States have the discretion to determine how to interpret what traffic metrics they use in their federally approved noise analyses. For example, some states use Level of Service (LOS) traffic volumes, while others use peak hour or design hour volume. Some states use an in-depth method to determine loudest hour which incorporates traffic volumes for a twenty four hour period and their corresponding operating speed per hour. This paper will discuss the time and effort associated with each procedure. Case studies will be presented to determine which of these traffic metrics best matches the current traffic counts.

Streets classification models by urban features for road traffic noise estimation

Road traffic is the primary source of environmental noise pollution in cities. This problem is also spreading due to inadequate urban expansion planning. Hence, integrating road traffic noise analysis into urban planning is necessary for reducing city noise in an effective, adaptable, and sustainable way. This study aims to develop a methodology that applies to any city for the stratification of urban roads by their functionality through only their urban features. It is intended to be a tool to cluster similar streets and, consequently, traffic noise to enable urban and transportation planners to support the reduction of people's noise exposure. Three multivariate ordered logistic regression statistical models (Model 1, 2, and 3) are presented that significantly stratify urban roads into five, four, and three categories, respectively. The developed models exhibit a McFadden pseudo-R2 between 0.5 and 0.6 (equivalent to R2 >0.8). The choice between Model 1 or 2 depends on the scale of the city. Model 1 is recommended for developed cities with an extensive road network, while Model 2 is most suitable in intermediate and growing cities. On the other hand, Model 3 could be applied at any city scale but focused on local management of transit routes and for designing acoustic sensor installations, urban soundwalks, and identification of quiet areas. Urban features related to road width and length, presence of transport infrastructure, and public transport routes are associated with increased traffic noise in all three models. These models prove useful for future action plans aimed at reducing noise through strategic urban planning.

Daytime Vehicular Traffic Noise Analysis and Modeling at Selected Roads in Port Harcourt

Using linear regression techniques, the daytime vehicle traffic noise levels at four significant crossings in Port Harcourt were analyzed and predicted. These are the crossroads of Rumuokwuta, Agip, 1st Artillery, and GRA. These crossroads are often quite active with both traffic and pedestrian activity. The sound levels, relative humidity and wind speed along with temperature were all measured using a sound level meter, relative humidity meter, and anemometer, respectively. Data on traffic volume was also gathered throughout the sample period. This was carried out for a total of 10 hours each day beginning at 7 am throughout the course of three days at each site. For the different sites, noise indices including equivalent noise level (Leq), statistical measure (Ln), noise climate (NC), traffic noise index (TNI), and noise pollution level (NPL) were calculated. With the exception of the GRA junction, statistical analysis reveals that there is no change in the data recorded at the various times of the day (p>0.05). The estimated indices from all sites were compared, and there was no discernible difference (p>0.05). Agip had the lowest TNI at 44.04 dB(A), while Rumuokwuta junction had the highest at 49.7 dB(A). The maximum Leq of 74.9 dB(A) was observed at 1st Artillery while the minimum of 74.1 dB(A) was recorded at Agip. GRA junction recorded the maximum NPL as 76.99 dB(A), while Rumuokwuta had the lowest as 76.3 dB(A). A linear model idea for forecasting noise from independent variables, including the atmospheric conditions and traffic count, was calibrated using multiple linear regression modeling. The coefficient of determination was discovered to vary between 0.5 and 0.95 after dimension reduction by the plotting of standardized charts.

Parameter analysis and Reliability Evaluation of Road Traffic Noise Prediction Model for Highway Traffic Noise Evaluation

The purpose of this study is to evaluate the reliability of prediction models(KHTN, RLS-90, CRTN, NMPB-08) that are widely used in road traffic noise analysis. For this purpose, the accuracy and difference values of the prediction model were analyzed by comparing the measurement values performed at the total of 21 highway sites, reflecting various conditions such as road structure, road pavement type, and noise barrier installation. In addition, the correlation between commercial programs(SoundPlan, CadnaA) was compared and reviewed for each of the same prediction models. First of all, as a result of analyzing the accuracy of each prediction model, KHTN is rated as 92.8% the most accurate based on ±3 dB error range. And CRTN is rated as 74.0~76.8% the most accurate among prediction models inherent in commercial programs. And, as a result of analyzing the correlation between commercial programs for prediction models, CRTN is 100% highly correlated and NMPB has the lowest correlation by 69.6%.

Comparing Traffic Noise Levels between Signalized Intersections and Roundabouts in an Urban Environment

This study aims to investigate and compare the traffic noise generated from signalized intersections and roundabouts. Hourly equivalent traffic noise level, traffic volume, and weather data were collected at four signalized intersections and four roundabouts of different sizes (two-lane and three-lane). Data were collected for a total period of 32 h during one weekday and one weekend at each intersection. A comparative traffic noise analysis that focused on peak hours was conducted between the different types of intersections. When comparing intersections of similar type and size, intersections with higher traffic volume produced higher traffic noise levels. However, when comparing intersections of different types and sizes, the two-lane roundabouts had lower traffic volume than the three-lane roundabouts but generated much higher traffic noise levels. When compared with two-lane signalized intersections, the two-lane roundabouts generated a much higher traffic noise with a slightly higher traffic volume. Similarly, the three-lane signalized intersections performed better than the three-lane roundabouts with marginally higher traffic noise levels and a much higher traffic volume. The results indicated that signalized intersections can be a better option than roundabouts from a traffic noise perspective when traffic volume is expected to be high.

A Before-and-After Traffic Noise Analysis of a Roundabout Converted to a Signalized Intersection

A Before-and-After Traffic Noise Analysis of a Roundabout Converted to a Signalized Intersection

Traffic noise induces oxidative stress and phytohormone imbalance in two urban plant species

Traffic noise pollution is one of the major environmental concerns in crowded cities worldwide. The objective of the present study was to investigate the effects of traffic noise on growth, hormonal balance, oxidative damage, and activity of antioxidant systems in two urban plant species, Tagetes patula and Salvia splendens. Each of the plant species were equally divided into 2 groups (control and traffic noise treatment) and each group was grown under identical controlled conditions in two separate growth chambers for two months. Traffic noise was recorded during peak traffic hours in a highly congested area of the city. Frequency analysis of traffic noise was conducted on the samples during the recording process. Plants in the traffic noise treatment group were exposed to 16 h of road traffic noise each day for a total of 15 days, while the control group was kept under complete silence. Traffic noise exposure led to significant decrease in growth indices of both plant species. The content of H2O2 and malondialdehyde (MDA) significantly increased upon traffic noise treatment in both species, followed by an increase in DPPH (2,2-diphenyl-1-picrylhydrazyl) anti-oxidant activity and the activity of antioxidant enzymes including catalase, peroxidase, and ascorbate peroxidase. Road traffic noise significantly reduced the content of phytohormones including zeatin, salicylic acid, indole-3-acetic acid, and gibberellic acid. On the other hand, the content of stress-related hormones including abscisic acid and jasmonic acid significantly increased in response to road traffic noise in both species. Based on our results, daily traffic noise can negatively affect growth and physiology of plants by inducing of oxidative damage and interfering with hormonal balance.

Аналіз методів штучного інтелекту для виявлення шуму від руху рейкового транспорту

Nowadays, many cities all over the world suffer from noise pollution. Noise is an invisible danger that can cause health problems for both people and wildlife. Therefore, it is essential to estimate the environmental noise level and implement corrective measures. There are a number of noise identification techniques, and the choice of the most appropriate technique depends upon the information required and its application. Analyzing audio data requires three key aspects to be considered such as time period, amplitude, and frequency. Based on the above parameters, the source of noise can be identified. This research paper suggests the utilization of artificial intelligence and machine learning algorithms for the traffic noise detection process. Computational methods are the fastest and most innovative way to analyze raw data sets and predict results. Identifying patterns in these methods requires a large amount of data and computing power. Machine learning models can be trained using three types of data: experimental sound libraries, audio datasets purchased from data providers, and data collected by domain experts. In the scope of the study, an experimental dataset was used to train a model that predicts the correct outcomes based on the inputs, using supervised learning. Developing an accurate model requires high-quality data input. However, incorrect data collection can cause noise in feature sets, as can human error or instrument error. Traffic sound events in the real environment do not usually occur in isolation but tend to have a significant overlap with other sound events. A part of this paper is dedicated to the problems that may arise during traffic noise detection, like incorrect data processing and data collection. It also discusses the ways to improve the quality of the input data. The study also states that the field of transport noise detection would greatly benefit from the development of a centralized railway database based on constructive railroad data, and from a centralized database with railway-specific datasets. Based on preliminary results of traffic noise analysis, modernization of the tram lines was proposed to reduce the environmental noise.

Determination of passenger car noise equivalent for mid-sized cities in India

The heterogeneity in traffic flow composition increases the complexity of road traffic noise analysis for mid-sized in India. This study aims to determine a passenger car noise equivalent (PCNE) with respect to the average traffic stream speed that represents the number of a particular vehicle category with reference to an identified vehicle based on their noise emission characteristics. In the present study, vehicles are classified as bus, truck, light commercial vehicles (minibus, minitruck), three-wheelers (vikram-rickshaw), two-wheelers (bike/scooter), car, e-rickshaw and auto-rickshaw, and tractor-trailer. Car is taken as a reference vehicle for estimation of PCNE in our study due to its high percentage in traffic stream. Data has been collected on both bituminous and concrete pavement in Kanpur city, India, to analyze the differential effect of pavement on the noise level. As per this study, tractors-trailers, trucks, three-wheelers, and buses had a higher PCNE value, while two-wheelers and cars had almost similar PCNE value. A comparative analysis of PCNE value at concrete pavement is also conducted by considering car running on the bituminous pavement as reference vehicle. The study suggests to employ PCNE value in traffic noise analysis as it converts the divergent traffic volume in terms of the car.

Analysis of Traffic Noise in Two Cross-Sections at the Road Crossing The City

The paper presents an analysis of the noise recorded by the two road traffic noise-monitoring stations. The stations were located in Kielce, Poland, at the road No. 74: on the outskirts of the city and near the center. Based on the experimentally recorded data, an equivalent sound level and acoustic pressure were determined for three sub-intervals of the day: nights, days and evenings. The conducted analyses showed that the average annual values (depending only on the time sub-intervals) of the median do not differ significantly between stations. A similar conclusion can be drawn based on simulations of the median and the C90 percentile of the sound pressure . However, the maximum relative differences in the C99 percentile of the acoustic pressure between stations are around 13%. The maximum relative differences in median pressure between stations are around 15% (for the time sub-interval nights).

Analysis of Traffic Noise for Single and Double Layered Porous Pavement with SPB Method -National Route 1, Sejong-Si Section-

최근 도로 교통소음 대책으로 다공성포장이 새로운 대안으로 주목받고 있다. 다공성포장은 아스팔트 혼합물 속에 세립자의 비율을 줄여서 여러 개의 공극을 형성하게 하는 포장을 말한다. 이 공극으로 물이 배수되게 하여 교통사고를 줄이고 타이어 노면의 마찰 소음을 줄여주는 역할을 하게 된다. 저소음포장이 일반적으로 활발히 적용되기 위해서는 소음저감 효과에 대한 많은 검증이 필요하다. 본 연구에서는 단층 저소음포장과 복층 저소음포장의 소음저감 효과를 분석하기 위해서 수행되었다. 먼저 단틍 저소음포장과 복층 저소음포장이 시공되어 있는 세종구간을 선정하고 도로변에서 도로교통 소음을 측정하는 방식인 SPB방법으로 교통소음을 측정하였다. 교통영향요소로는 교통량, 속도 그리고 중차량 혼입률을 측정하였다. 측정결과, 복층저소음포장이 단층저소음포장보다 평균 7.95dB(A) 작게 평가되었다. 95% 신뢰도 수준에서는 7.57dB(A) 작게 평가되었다. 도로교통 소음에 미치는 영향요소로는 속도나 대형차 혼입률 보다는 교통량이 가장 크게 영향을 미쳤다. 교통량이 증가할수록 소음은 모두 증가했지만 두 포장 사이의 소음차이는 점차 감소하였다. 도로교통소음에 영향을 미치는 요소로는 교통요소보다 도로포장의 차이가 가장 크게 나타났다. 본 연구를 통해서 도로교통 소음을 줄이기 위한 대책으로 교통속도를 줄이거나 중차량의 통행을 제한하거나 교통량을 줄이는 것보다 도로포장을 바꾸는 것이 가장 효과적인 방법임을 알았다. 이를 검증하고 정량화하기 위한 광범위한 연구가 필요하다.

Use of mathematical models and computer software for analysis of traffic noise

Abstract Noise measurement and evaluation of the existing noise situation is carried out in the vicinity of selected roads to demonstrate the need for the design of anti-noise measures or to assess the effectiveness of the measures. The selection and number of measuring points, time and intervals, the road noise measurement procedure and the measuring instruments used shall be used in accordance with the provisions of the STN ISO 1996-1 and STN ISO 1996-2 standards. During the measurement, it is also necessary to determine the microclimatic conditions of measurement, such as temperature and relative humidity, wind direction and speed, barometric pressure, duration and intensity of precipitation at the measuring point. The determinant for the definition of road traffic noise is the equivalent sound level A or the equivalent sound level in the third octave bands over a given time interval. In specific cases, it is possible to determine the equivalent sound pressure level from individual vehicle transit.

Traffic State Evaluation Using Traffic Noise

Traffic state information is widely applied into all aspects of Intelligent Transportation System (ITS), such as the macro-control of government departments, the implementation of traffic managers' plans, the decision-making of residents travel, and so on. At present, Mel Frequency Cepstrum Coefficient (MFCC) is generally used as characteristic of traffic noise to characterize different traffic states, and performs well in simple noise environment, but performs poorly in complex noise environment. Based on the analysis of traffic noise acquired from a roadside-installed acoustic acquisition equipment, the evaluation problem of traffic state in complex noise environment is considered in this paper. Traffic state is divided into three categories according to traffic speed in our work: free flow (40 km/h and above), saturated flow (10-40 km/h), and jammed flow (0-10 km/h). Teager Energy Operator (TEO) is introduced to improve the MFCC characteristic, thus a novel characteristic called T-MFCC is proposed. Principal Component Analysis (PCA) is introduced to reduce dimension of T-MFCC characteristic, thus a novel characteristic called PT-MFCC is proposed. Support Vector Machine (SVM) optimized by Particle Swarm Optimization (PSO) algorithm is applied as classifier to identify traffic state. Characterization capabilities of two modified characteristics and traditional MFCC characteristic for traffic state are compared in this paper. Experimental results demonstrate that the evaluation accuracy of traffic state based on T-MFCC characteristic is 3.685% higher than that based on MFCC characteristic, and the evaluation accuracy of traffic state based on PT-MFCC characteristic is 26.466% lower than that based on MFCC characteristic. Therefore, T-MFCC characteristic is superior to MFCC characteristic, while MFCC characteristic is superior to PT-MFCC characteristic, namely, T-MFCC characteristic can better characterize traffic state than MFCC characteristic, meanwhile, there are no redundancy attributes in T-MFCC characteristic, thus PCA is not needed to reduce the dimension of T-MFCC characteristic.

Integrated road traffic noise mapping in urban Indian context

Abstract Road traffic noise has been recognized as a serious issue that affects the urban regions. Due to urbanization and industrialization, transportation in urban areas has increased. Traffic noise characteristics in cities belonging to a developing country like India are highly varied compared to developed nations because of its heterogeneous conditions. The objective of the research study is to assess noise pollution due to heterogeneous traffic conditions and the impact of horn honking due to un-authorized parked vehicles on the main roadside. Noise mapping has been done using the computer simulation model by taking various noise sources and noise propagation to the receiver point. Traffic volume, vehicular speed, noise levels, road geometry, un-authorized parking, and horn honking were measured on tier-II city roads in Surat, India. The study showed not so significant correlation between traffic volume, road geometry, vehicular speed and equivalent noise due to heterogeneous road traffic conditions. Further, analysis of traffic noise showed that horn honking due to un-authorized parked vehicles contributed an additional up to 11 dB (A), which is quite significant. The prediction models such as U.K’s CoRTN, U.S’s TNM, Germany’s RLS-90 and their modified versions have limited applicability for heterogeneity. Hence, the noise prediction models, which can be used for homogeneous road traffic conditions are not successfully applicable in heterogeneous road traffic conditions. In this research, a new horn honking correction factor is introduced with respect to unauthorized parked vehicles. The horn honking correction values can be integrated into noise model RLS-90, while assessing heterogeneous traffic conditions.

Influence of Honking on the Road Traffic Noise Generated at Urban Rotaries for Heterogeneous Traffic

Abstract This paper presents a study conducted at major rotaries for quantifying the traffic noise levels by considering the vehicle volume and their respective honking as governing parameters for heterogeneous traffic. Traffic volume and traffic noise data was collected using a digital video camera and a class 1 sound level meter, respectively. The traffic noise data was analysed using noise tools for identifying the noise level variation. The data collected was subjected to statistical analysis for light, medium and heavy vehicles, and their contribution towards noise levels is proven to be effective with the forthright fact that, heavy vehicles and their corresponding honking were majorly affecting the equivalent noise level compared to other vehicular proportion. An equivalent noise level [LAeq (dB)] rise of 2 to 6 dB (A) is solely caused by heavy vehicles, which is an important observation to be considered for traffic noise analysis at the rotaries. Based on the obtained results from one of the rotaries, noise prediction model is developed for estimating the LAeq (dB), which is able to predict the noise levels with good precision when validated with the data collected at second rotary intersection for different vehicle volumes.

An innovative approach of urban noise monitoring using cycle in Nagpur, India.

Noise is one of the most significant sources of environmental pollution in metro cities. Traffic noise plays an important role to contribute to ambient noise pollution. Noise pollution monitoring was carried out using sound level meter mounted on the cycle with the help of mould fixed on it. Cycle is a noiseless mode of transport and therefore, it was used for noise monitoring over 700 locations in Nagpur City. Noise levels were recorded at each location for 5 min during peak rush hours of traffic in the morning and in the evening at an interval of 1 s leading to around 300 readings each. Noise indices like Leq, L90, L10, Lmax and Lmin were calculated for noise level data analysis. TNI was also determined for analysis of traffic noise on intersections of roads. Equivalent noise levels were transferred into the Geographic Information System (GIS) and spatial analysis tool was used to create spatial and strategic noise maps. Based on the maps, it is revealed that noise levels are exceeded all over the city with respect to ambient noise standards. It was experienced that intersections of roads during peak rush time are very noisy due to honking. The study recommends the noise monitoring through cycle-mounted sound level meter since it covers unapproachable sites, more coverage in the city that helps in identifying hotspot area of concern for noise abatement strategies and planning.

Application of autoencoder to traffic noise analysis

The aim of an autoencoder neural network is to transform the input data into a lower-dimensional code and then to reconstruct the output from this representation. Applications of autoencoders to classifying sound events in the road traffic have not been found in the literature. The presented research aims to determine whether such an unsupervised learning method may be used for deploying classification algorithms applied to the automatic annotation of road traffic-related events based on noise analysis. Two-dimensional representation of traffic sounds based on 1D convolution was fed the core of autoencoder neural network, and after that classified with seven feed-forward classification subnetworks. Obtained results show that sound recordings can help determine the number of vehicles passing on the road. However, instead of being treated as independent, this method output should be combined with another source of data, e.g., video processing results or microwave radar data readings. Results of vehicle types classification and occupied lane obtained with the use of autoencoder are shown in the paper.

Simulation and Analysis of Road Traffic Noise among Urban Buildings Using Spatial Subdivision-Based Beam Tracing Method

In order to realize the simulation and evaluation of road traffic noise among urban buildings, a spatial subdivision-based beam-tracing method is proposed in this study. First, the road traffic source is divided into sets of point sources and described with the help of vehicle emission model. Next, for each pair of source and receiver, spatial subdivision-based beam-tracing method is used in noise paths generation. At last, noise distribution can be got by noise calculation of all receivers considering the complex transmission among urban buildings. A measurement experiment with a point source is carried out to validate the accuracy of the method; the 0.8 m height and 2.5-m height average errors are about 0.9 dB and 1.2 dB, respectively. Moreover, traffic noise analysis under different building layouts and heights are presented by case applications and conclusions can be reached: (1) Different patterns result in different noise distributions and patterns designed as self-protective can lead to an obvious noise abatement for rear buildings. Noise differences between the front and rear buildings are about 7–12 dB with different patterns. (2) Noise value might not show a linear variation along with the height as shielding of different layers is various in reality.