Traffic Noise Model Research Articles

Analisa Pengaruh Volume Kendaraan dan Kecepatan Terhadap Tingkat Kebisingan Lalu Lintan di Jalan Cikuray, Garut

Cikuray street, Garut is a collector road located 1 km from Garut Square, and on this road, there are many offices and shops for facilities and services. Therefore, Cikuray street experiences an increase in a traffic flow which causes noise. This study aims to analyze the level of traffic noise due to the volume and speed of vehicles. The method used in this research is a direct survey of the field and traffic noise modeling analysis using multiple linear regression analysis with the help of SPSS (Statistical Package of the Social Sciences) software version 26 for Windows. From the analysis, the highest volume of vehicles occurred on Saturday at 11,605 pcu/hour, with the average speed ranging from 34.00 – 38.00 km/hour and for a noise level of 84.55 dB(A) which means it has exceeded the standard. limit. permissible noise level. The relationship between volume and vehicle speed to traffic noise levels on Jalan Cikuray Garut using mathematical modeling is shown in the equation Y = 56.777 + 0.020X1 + 0.043X2 + 0.214X3 – 0.090X4 with R2 = 0.443. This shows that traffic noise (Y) can be explained by the volume of motorcycles (X1), the volume of light vehicles (X2), the volume of heavy vehicles (X3), and traffic speed (X4).

Modelling of traffic noise in the vicinity of urban road intersections

Traffic noise is continuously rising alongside roadways, especially at intersections, due to rapid urbanization, eventually affecting acoustical climate and quality of life. This present study develops a traffic noise model for intersections with minimal evidence of interrelationships among influential traffic noise factors. An integrated Bayesian networks and Partial least squares structural equation modelling approach has been employed on 342-hour field measurement data collected from nineteen intersections in Kanpur, India. The integrated approach developed the traffic noise prediction model with 62.4% explanatory power and identified direct and indirect effects of five influential factors on traffic noise. For instance, Traffic flow attributes, i.e., traffic volume and honking, are the most crucial ones to degrade acoustical climate at intersections. Besides, Built environment and Climate conditions induce only indirect effects on traffic noise. Thus, this study provides a useful basis for planners to understand traffic noise relationships and deploy noise mitigation strategies accordingly.

IMPROVING THE PERFORMANCES OF THE TRAFFIC NOISE MODEL USING MULTI-LINEAR REGRESSION

This research aims to examine the traffic noise levels and to improve the performances of the Calculation of Road Traffic Noise model (C.R.T.N.) by applying the statistical multiple linear regression approach. Research methods included traffic noise level measurements with a noise measuring device in an urban area, using a sampling method in different periods. An evaluation of the measured data and prediction results was performed. Based on the predicted values of the C.R.T.N. model and coefficient of determination (R2), multi-linear regression was carried out to determine statistically significant parameters. The obtained multi-linear regression equation defined a new form of C.R.T.N. model. When applying the new improved version based on the C.R.T.N. model, higher accuracy of prediction is achievable. It can be seen that by applying multi-linear regression, the obtained prediction values are acceptably equated with field measurements in the chosen research environment. So, in this way, the differences between the predicted values of the noise level and the values measured in the field were minimized. Finally it can be concluded that when applying the new improved version based on the C.R.T.N. model, higher accuracy of prediction is achievable.

Estimating traffic noise over a large urban area: An evaluation of methods

Unlike air pollution, traffic-related noise remains unregulated and has been under-studied despite evidence of its deleterious health impacts. To characterize population exposure to traffic noise, both acoustic-based numerical models and data-driven statistical approaches can generate estimates over large urban areas. The aim of this work is to formally compare the performances of the most common traffic noise models by evaluating their estimates for different categories of roads and validating them against a unique dataset of measured noise in Long Beach, California. Specifically, a statistical land use regression model, an extreme gradient boosting machine learning model (XGB), and three numerical/acoustic traffic noise models: the US Noise Model (FHWA-TNM2.5), a commercial noise model (CadnaA), and an open-source European model (Harmonoise) were optimized and compared. The results demonstrate that XGB and CadnaA were the most effective models for estimating traffic noise, and they are particularly adept at differentiating noise levels on different categories of road.

A physics-guided model for predicting spectral and temporal variability of road traffic noise

The National Transportation Noise Map (NTNM) provides daily averaged A-weighted equivalent sound levels across the continental United States (CONUS) due to road traffic. The NTNM maps the spatial variability of road traffic noise, but not the temporal or spectral variability. A physics-guided model was developed to predict the temporal and spectral variability of road traffic noise across CONUS. Empirical models were developed to predict hourly road traffic volume and vehicle class mix across CONUS based on publicly available traffic volume measurements and geospatial data. The Federal Highway Administration’s Traffic Noise Model characterizes average spectral levels by vehicle class; by combining the empirical model with the Traffic Noise Model’s characteristic vehicle class spectra, hourly traffic noise predictions across CONUS are made which include temporal and spectral variability. This physics-based modeling approach improves upon nation-wide mapping of road traffic noise. [Work supported by U.S. Army SBIR.]

An Automated Sound Barrier Inventory Method Using Mobile LiDAR

A sound barrier, also called a noise barrier, plays an irreplaceable role in traffic noise abatement. The Federal Highway Administration’s (FHWA) highway noise regulation requires each state highway agency to maintain a complete inventory of all constructed noise-abatement features. Although key information for most of the newly constructed sound barriers has been inventoried, public transportation agencies are still struggling to keep close track of the in-service barriers because their inventory information is nonexisting, and manual inventory remains time-consuming, labor-intensive, and often dangerous. Therefore, many agencies have shown more interest in exploring the possibility of using light detection and ranging (LiDAR) data for assisting in sound barrier inventory, thanks to the widely available data set and much-improved data quality. This study proposes a LiDAR-based sound barrier inventory method to automatically extract the sound barrier’s location and measure the corresponding geometry. The extraction of a sound barrier is achieved using its unique features after random sample consensus (RANSAC)-based ground extraction and region-growing segmentation. The geometry measurement of the sound barrier is performed by analyzing the detailed dimension of the extracted point cloud, including location, height, length, and lateral offset. The experimental test conducted near Carver, Massachusetts showed the results with a precision rate of 99.9% and a recall rate of 93.8%. Moreover, the outcome of the experimental test has demonstrated the robustness of the proposed method in different complexities of the background and sound barrier types (linear and zigzag). This study has demonstrated the feasibility of using LiDAR for effectively inventorying in-service barriers. Besides the critical application of asset management, the detailed location and geometry information provided by the proposed method can provide valuable insight for other critical applications, such as traffic noise modeling.

Evaluation and ANN Modelling of Traffic Noise on BRTS Corridors with Different Median Vegetation: A Case Study of Surat City (Western India)

Evaluation and ANN Modelling of Traffic Noise on BRTS Corridors with Different Median Vegetation: A Case Study of Surat City (Western India)

Analisis Tingkat Kebisingan Akibat Lalu Lintas Pada Kawasan Perkantoran dan Pendidikan di Kecamatan Kota Utara Kota Gorontalo

Increased traffic volume can result in environmental issues such as noise or noise pollution. The purpose of this study is to analyze the noise levels in the office and educational areas in Kota Utara District, Gorontalo City, using a traffic noise prediction calculation model and to analyze the comparative value of noise levels based on the prediction calculation model and quality standards according to KEP-48/MENLH/11/1996. The location of this study is spread over several points, namely in Kampus STIKES Bina Mandiri Gorontalo, in front of Kantor Kejaksaan Tinggi Gorontalo, and in front of Kantor Imigrasi TPI Kelas I Gorontalo. The method used in this study is an empirical method with a prediction model for traffic noise prediction.The results showed that the highest noise levels on Monday and Thursday occurred in the Kampus STIKES Bina Mandiri Gorontalo area with a noise intensity of 65.71 dB (A) and 64.71 dB (A). The highest noise level on Saturday occurred in front of Kantor Kejaksaan Tinggi Gorontalo with a noise intensity of 65.12 dB (A). According to a comparison of the results of the analysis and the quality standard according to KEP-48/MENLH/11/1996, the average noise level in the Kampus STIKES area has exceeded the quality standard value for the educational area, which is 55 dB (A). The noise level in front of Kantor Kejaksaan Tinggi Gorontalo on Saturday at 07.00–08.00 WITA shows that the noise intensity has exceeded the quality standard for office areas, which is 65 dB (A). Meanwhile, the noise level in front of Kantor Imigrasi on average still meets the quality standards required for office areas.

Acoustically soft ground as a highway traffic noise reduction strategy

Acoustically soft ground adjacent to transportation systems can help reduce noise in nearby communities. The effectiveness of soft ground surfaces to reduce highway traffic noise is dependent on the soft ground placement, ground type/material, number/placement of traffic lanes, and vehicle mix. Each specific site geometry and each vehicle noise sub-source location combines to define the region of influence in adjacent communities. The effect of a soft ground strip in the right-of-way (ROW) of a highway is examined on both a broadband and 1/3-octave band basis and as a function of distance using the U.S. Federal Highway Administration Traffic Noise Model (TNM). Examinations as a single noise reduction strategy and combined with quieter pavement are presented for a variety of highway/ROW configurations. Results show a noise reduction of several decibels due to a gravel strip and more combined with quieter pavement.

Traffic Noise Modeling of Short Safety Barriers

Solid safety barriers are commonly constructed along highways. These barriers improve highway safety by preventing collisions and providing separation from slopes or adjacent hazards such as deep water. For elevated and at-grade roadways, these shorter barriers have also been shown to noticeably reduce traffic noise. In this study, modeling methods were developed and evaluated for their ability to accurately calculate the performance of short barriers in reducing traffic noise at the wayside. Five real-world highway scenarios were selected for model validation, including four sites located behind short safety barriers and a site located behind a short berm. Theoretical modeling was conducted to systematically evaluate the effects of modeling parameters on the prediction of noise reduction provided by short barriers.

Recent advancements in the challenges and strategies of globally used traffic noise prediction models.

It is the need of an era to develop efficient traffic noise prediction models with optimum accuracy. In this context, the present work tries to comprehend the performance-related potential parameters based on earlier published articles worldwide that are responsible for deviation in noise values for different traffic noise prediction models and find out critical gaps. This study reviewed the process involved in source modeling and sound propagation algorithms, applicability, limitations, and recent modification in 9 principal traffic noise prediction models adapted by different countries all around the globe. The result of this review shows that many researchers had carried out comparative analysis among various traffic noise prediction models, but no emphasis was made on the recent modifications, limitations associated with those models, and strategies involved without ignoring the propagation and attenuation mechanism in the developing phase of these models. The findings of this study revealed that the major challenge for any traffic noise prediction model to be efficient enough is the inclusion of all the factors responsible for the generation and deviation of traffic noise before reaching the receiver. These responsible factors include a factor for source emission, sound propagation and attenuation, road characteristics, and other miscellaneous factors such as absorption characteristics of building facades, honking, and dynamic behavior of traffic. This study adds to the broader domain of research and will be used as reference material for future traffic noise modeling strategies.

Improving upon standard approaches for mapping road traffic noise

The Federal Highway Administration’s Traffic Noise Model is the standard model used to predict traffic noise in the United States. The Department of Transportation’s National Transportation Noise Map (NTNM), based on average annual daily traffic counts, uses the traffic noise model to map out an average A-weighted equivalent sound level based on road traffic. Because the NTNM does not account for temporal variation, measured acoustic levels often differ from the average predicted levels. A recent internally developed method uses state highway agencies’ reported traffic counts to create a model to predict hourly traffic across the United States based on geospatial features. This predictive approach can be combined with acoustical propagation algorithms in the traffic noise model to create temporally variable traffic noise maps. Because predicted traffic counts match reported traffic counts more closely than averaged counts, predicted sound levels are more characteristic of measured sound levels than the NTNM. The expected sound level errors using the predictive approach are shown to be significantly smaller than the expected NTNM errors. [Work supported by U.S. Army SBIR.]

Statistical modelling for urban roads traffic noise levels

Traffic noise is one of the most significant types of vehicular emissions that result in physical and psychological health effects on humans and is caused by an increase in vehicular ownership and urbanisation. The central business district (CBD) of Ondo has been exposed to persistent road traffic and commercial activities due to development and expansion of the economy, and this has led to an increase in traffic noise levels. Due to the increase in traffic volume, urbanisation, and population deterioration of road pavements in the CBD, it was hypothesised that noise related to traffic has increased and is above the permissible limit of the World Health Organisation in the study area. Monitoring noise levels by administrative agencies will help mitigate traffic noise intensity and aid in urban planning. This study examined the traffic noise levels and developed models for the CBD of Ondo, Nigeria. Adopting the empirical methods of the Calculation of Road Traffic noise (CoRTN) model and statistical Multiple Linear Regression (MLR) modelling approach, traffic noise models for the assessment of equivalent noise levels (Leq) at the CBD of Ondo were developed. Over 90% of the roadsides surveyed were above the world health organization's 70 dB(A) threshold. Correlation between CoRTN and MLR models demonstrated reliable efficiency relative to observed noise levels with an acceptable coefficient of determination (R2) values of 0.943 and 0.963, respectively. The deviation between the noise levels measured with the expected noise levels (MLR and CoRTN) varied, between 0.44 dB(A) and 2.09 dB(A) with an average mean difference of 0.37 dB(A) to 1.9 dB(A). These values are adjudged satisfactory since it is within the +/- 3.0 (dB)A allowed by the Federal Highway Administration (FHWA). The models are therefore robust and accurate in estimating the level of noise from road traffic for the study area.

Understanding and planning shared micro-mobility

The prediction and evaluation of expressway traffic noise is essential work of environmental management. A Monte Carlo simulation method for instantaneous sound levels is introduced according to the characteristics of free-flow traffic on expressways. The sound power levels of light and heavy vehicles are measured by field experiments, and a probability model of vehicle noise emission is established. Noise under different traffic volumes and distances is simulated by the Monte Carlo method. A probability distribution model of traffic noise with traffic volume and distance as parameters is given. A comparison of the calculated results and measured data shows that the proposed model can effectively predict the probability distribution of traffic noise and can be used to predict common traffic noise indicators. The prediction errors are less than 3 dB(A) for Leq, L10, L50, and L90; less than 1.3 dB(A) for σ; and less than 13 dB(A) for TNI.

A probability distribution prediction method for expressway traffic noise

The prediction and evaluation of expressway traffic noise is essential work of environmental management. A Monte Carlo simulation method for instantaneous sound levels is introduced according to the characteristics of free-flow traffic on expressways. The sound power levels of light and heavy vehicles are measured by field experiments, and a probability model of vehicle noise emission is established. Noise under different traffic volumes and distances is simulated by the Monte Carlo method. A probability distribution model of traffic noise with traffic volume and distance as parameters is given. A comparison of the calculated results and measured data shows that the proposed model can effectively predict the probability distribution of traffic noise and can be used to predict common traffic noise indicators. The prediction errors are less than 3 dB(A) for Leq, L10, L50, and L90; less than 1.3 dB(A) for σ; and less than 13 dB(A) for TNI.

Quiet Route Planning for Pedestrians in Traffic Noise Polluted Environments

Noise pollution is one of the main stressors in urban environments, having negative impacts on people’s quality of life and health. For some groups of citizens, such as school children, patients, and elders, there is a need to support them in finding pedestrian routes in noise polluted areas of cities. In this paper, we focus on the estimation of traffic noise, and present an approach to provide quiet routing services, taking into account the estimated noise levels of roads. By combining Volunteered Geographic Information, official socio-economic data, and open-access GPS trajectory data, we develop a set of traffic related variables, and apply machine learning methods to perform traffic volume estimations. Given the estimated traffic information, an existing traffic noise model is then employed to derive the noise polluted areas. For generation of quiet routes, a new routing algorithm is proposed based on the Dijkstra algorithm. It minimizes the exposure of pedestrians to traffic noise pollution while taking into account the route distance constraint. We apply our quiet routing approach to the city of Heidelberg (Germany). The application results demonstrate the efficacy of our algorithms in the generation of quiet routes customized to pedestrian preferences.

Modelling of Traffic Noise Along Urban Corridor: A Case Study of Amman

Abstract Noise became one of the main environmental indicators of the quality of urban life. The aim of this study was to develop a traffic noise model for an arterial road in Amman, the capital of Jordan, which was being subjected to a persistent increase in traffic and its related issues. The characteristics of the traffic and its relevant noise were analysed to develop a noise prediction model using an adaptive neuro-fuzzy inference system (ANFIS). A geographic information system (GIS) was then implemented to model the current noise along the arterial road. The results of ANFIS model showed that traffic delays and the percentage of heavy vehicles were the main causes of traffic noise generation within the urban area. However, the developed ANFIS model can simulate traffic noise with a relatively low root mean square error. Furthermore, this study will help to improve the perception about traffic noise and its development along arterial roads.

Determining the Impact of Vehicle Traffic on Selected Noise Level Indicators in the Vicinity of the Unsignalized Intersection - A Case Study

There is a growing concern about the negative impact of road traffic noise on mental and physical health in developing countries. Road traffic noise models are important for preparing prevention and mitigation plans in order to minimize noise levels. The majority of the researches have focused to model traffic noise on-road section for uninterrupted flow conditions. Research on traffic noise modeling for interrupting flow situations has been mainly focused on the intersection controlled by traffic light and roundabouts. A little attention has been given to model traffic noise at unsignalized intersections. The objective of this study has been to investigate the contribution of the traffic parameters and performances on the noise level at unsignalized intersections based on simple regression and multiple linear regression analyses. The noise level indicators have included Leq, L10, L50 and L90. The results have indicated that the traffic performance variables such as the degree of saturation and the average delay had a positive correlation with the traffic noise level. However, traffic volume had a higher relationship with the noise level compared to the traffic performance variables. Since the traffic composition has been dominated by motorcycles, it has been found out that the volume of motorcycles was the best predictor of the variation in traffic noise level.

Traffic noise models and noise guidelines: A review

Traffic noise is a major cause of noise pollution, and impacts of noise pollution on humans have been studied extensively. Previous studies have helped in the development of traffic noise prediction analysis using robust analytical and computational models, parameters connected with traffic, road characteristics, and environment. This study reviewed 11 traffic noise modeling strategies and parameters used by various agencies in different parts of the world. Seven out of the 11 models reviewed in this paper were based on outdoor measurements while the remaining four were based on outdoor and indoor measurements. Considering the cost and time involved in developing these models, there is need to understand existing traffic noise models, differences, and assumptions before adopting or recalibrating them for local demands. This study contributes to the larger body of knowledge and intends to serve as a reference material for future researchers in the area of traffic noise modeling and techniques.

The Impact of Thin Asphalt Layers as a Road Traffic Noise Intervention in an Urban Environment

Low-noise thin asphalt layers (TALs) are a feasible solution to mitigate road traffic noise in urban environments. Nevertheless, the impacts of this type of noise intervention are reported mostly regarding noise levels, while non-acoustic aspects influencing the population perception are still little-known. This study investigates the implementation of TALs in two streets of Antwerp, Belgium. The effectiveness of the intervention was measured via noise modelling and acoustic measurements of road traffic noise. A reduction of 2.8 dB in noise exposure was observed in Lden and Lnight, while SPB measurements showed decreases up to 5.2 dB on the roadside. The subjective impacts of the TALs were evaluated via self-administered surveys and compared to results from control streets. The annoyance indicators were positively impacted by the TALs implementation, resulting in annoyance levels similar or lower than in the control streets. The TALs did not impact the reported physical complaints, sleep quality, and comfort level to perform activities.