Traffic Noise Model Research Articles

3D Kriging interpolation for traffic noise visualization: designing noise observation points and valuation of spatial interpolation accuracy

Identifying the risk of traffic noise is vital in minimizing traffic noise pollution in urban areas. As noise travels in every direction, 3D visualization of traffic noise is essential, which involves visualising traffic noise along the facades of buildings. A standard traffic noise model is necessary to calculate traffic noise levels, as several factors affect traffic noise. Moreover, designing noise observation points in 3D and spatial interpolation play significant roles in 3D noise visualisation. Therefore, this study demonstrates the results by elaborating on the spatial interpolation and designing noise observation points. A noise observation point consists of four parameters in 3D space. Generally, Inverse Distance Weighted (IDW), Triangular Irregular Network (TIN), and Kriging do not support the interpolation of four parameters in 3D. However, 3D Kriging in Empirical Bayesian Kriging provides significant opportunities to interpolate noise levels in 3D. However, the elements of the function of spatial interpolations are vital for accuracy. The 3D Kriging uses different variograms according to semivariance. This variogram directly impacts the weighting factor of 3D Kriging. Therefore, this study develops a comparison to identify the impact of different variograms on the accuracy of 3D Kriging interpolation on traffic noise.

Employing the maximum likelihood fitting method in the principle of probability and statistics to continually apply the original FHWA road traffic noise prediction model

The original federal highway administration (FHWA) model for road traffic noise (RTN) prediction in the United States (US) has been widely applied in many countries. However, the condition was that the intensities of vehicle noise sources at the reference distance must be determined based on the empirical formula related to vehicle speed and road surface obtained by applying the measured results of a large number of different vehicles in the US. Because the dominant vehicle types and road pavement structures in other countries are not the same, the classification of vehicles and the calculation formula for the source intensity will be different. Even in the US, because of the older modeling data, the current vehicles and their changed noise source, and the new traffic noise model (TNM), the original FHWA model may produce errors if applied in other countries. Therefore, by employing the maximum likelihood method in probability and statistics, this study proposes a method that can continue to directly apply the original FHWA model according to the local vehicle traffic flow, together with a simple method of measuring the attenuation of RTN with distance, to eliminate the uncertainty of the source intensity.

A segment-based A-weighted sound propagation model for road traffic noise assessment: A general concept

The propagation model of the road traffic noise established by the Acoustical Society of Japan, the ASJ-RTN Model 2018, directly obtains the A-weighted sound pressure level by assuming specific power spectra of vehicle sources in a single calculation. This is more effective in reducing computational burden than European models, which typically require calculations in eight bands from 63 Hz to 8 kHz. However, the ASJ model has not been generalized enough to deal with an arbitrary terrain profile, contrary to the European models where the handling of a segmented terrain profile is integrated. This will become a major obstacle in realizing noise mapping in Japan. In this paper, a concept of an A-weighted sound propagation model for road traffic noise with integrated handling of a segmented terrain profile is presented. The model combines the segmented modeling of multiple diffractions over a complex profile and the Fresnel weighting of ground characteristics both employed by Harmonoise, the mean plane for ground modeling used in CNOSSOS-EU, and the calculation of the excess attenuation terms following the ASJ model. Preliminary calculation results show that the model agrees reasonably well with finite-difference time-domain simulation results on flat and uniform ground, but still needs investigation for non-flat and nonuniform ground.

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%.

Optimization of Dataset Generation for a Multilinear Regressive Road Traffic Noise Model

According to the European Environmental Agency, road traffic noise is one of the worst and most prevalent kinds of environmental pollutants, which causes health problems to a constantly increasing number of people in urban areas throughout Europe. It has been proved that prolonged exposure to sound levels exceeding 55 dBA is harmful and causes severe problems like sleep disturbances, tiredness, lack of concentration, high blood pressure and, in the worst case, sudden death. A precise and constant evaluation of sound level in inhabited areas is therefore desired (and in some cases compelled by laws), but collection of actual noise data is not easy and sometimes not possible. For this reason, Road Traffic Noise (RTN) models are very handy: one can (more or less precisely) estimate the noise emitted in a certain area having certain road traffic characteristics. The application of RTN models, anyway, also has problems. First of all, an RTN model has to be built and calibrated by using real collected noise data. Moreover, when trying to apply an RTN model on road traffic situations that are far away from the site of collection, the models generally fail. To overcome such problems, in this contribution, a road traffic dataset has been computed by randomly generating values of traffic variables like the number of vehicles per unit of time, their velocities, and their distance from the receiver. Then, by applying a multiregressive function on the dataset, the obtained coefficients have been used to calibrate and validate the presented model. The three steps (generation of the dataset, calibration of the model, and validation on a real dataset) are detailly investigated.

Heterogeneous road traffic noise modeling at mid-block sections of mid-sized city in India.

This study attempted to develop a computer-based software for monitoring the traffic noise under heterogeneous traffic condition at the morning peak (MP), off peak (OP), and evening peak (EP) periods of mid-block sections of mid-sized city in India. Traffic noise dataset of 776 (LAeq, 1hr) were collected from 23 locations of Gorakhpur mid-sized city in the state of Uttar Pradesh in India. K-nearest neighbor (K-NN) algorithm was adopted for traffic noise prediction modeling. Moreover, principal component analysis (PCA) technique was used for the dimensionality reduction and to overcome the problem of multi-collinearity. The developed model exhibits R2 value of 0.81, 0.78, and 0.77 in the MP, OP, and EP, respectively, forLeq, and a value of 0.86, 0.80, and 0.84 for L10. The proposed model can predict more than 94% observations within an accuracy of ±3%. Ultimately, a user-friendly noise level calculator named "Traffic Noise Prediction Calculator for Heterogeneous Traffic (TNPC-H)" was developed for the benefit of field engineers and policy planners.

A Microscopic Prediction Model for Traffic Noise in Adjacent Regions to Arterial Roads

A Microscopic Prediction Model for Traffic Noise in Adjacent Regions to Arterial Roads

Roadway construction noise model progression and integration with federal highway administration's traffic noise model

In 2006, the Federal Highway Administration (FHWA) published the Roadway Construction Noise Model (RCNM 1.0) to predict noise levels from construction activities. RCNM 1.0 included basic acoustical data for a variety of construction equipment. In 2018, RCNM 2.0 was developed using a similar user interface to FHWA's Traffic Noise Model (TNM) version 3.0. RCNM 2.0 improved prediction of construction noise propagation and mitigation. RCNM 2.0 also expanded the source database and allowed users to analyze the project site conditions using mapping. TNM 3.2 includes integration of the TNM 3.1 software structure with RCNM 2.0. Equipment source data in the RCNM database were calibrated to account for the excess attenuation from each new source height. The RCNM database consists of moving and stationary equipment sources, each with spectra for two-four cardinal directions relative to the equipment, which may not always operate constantly throughout the day. Modeling these novel source features in TNM required new logic to handle stationary point sources, partially inactive sources, and source directionality. The goal of TNM 3.2 was for analysts to combine roadway and construction noise analyses within one file. Doing so required changes to the TNM object intersection rules, as well as all aspects of the user interface including map displays, input commands, and output data tables.

An app for nationwide dynamic traffic noise prediction

Despite being so pervasive, road traffic noise can be difficult to model and predict on a national scale. Detailed road traffic noise predictions can be made on small geographic scales using the US Federal Highway Administration's Traffic Noise Model (TNM), but TNM becomes infeasible for the typical user on a nationwide scale because of the complexity and computational cost. Incorporating temporal and spectral variability also greatly increases complexity. To address this challenge, physics-based models are made using reported hourly traffic counts at locations across the country together with published traffic trends. Using these models together with TNM equations for spectral source emissions, a streamlined app has been created to efficiently predict traffic noise at roads across the nation with temporal and spectral variability. This app, which presently requires less than 700 MB of stored geospatial data and models, incorporates user inputs such as location, time period, and frequency, and gives predicted spectral levels within seconds.

Noise modeling of short safety barriers in FHWA's updated Traffic Noise Model (TNM) version 3.1

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, shorter barriers have also been shown to noticeably reduce traffic noise. In prior research, modeling methods were developed and evaluated for their ability to accurately calculate the performance of short barriers in reducing traffic noise at the wayside. The modeling results were compared to available field data and to the results of FHWA's Traffic Noise Model version 2.5 (TNM). This paper extends the prior research to compare the results to the newest FHWA TNM version, 3.1.

What is Average Pavement? A Critical Commentary

The Traffic Noise Model (TNM) is used to forecast traffic noise levels. When applied to federally funded projects, a user of the software is expected to apply "average" noise emissions for pavements. This "average" is an intermediate value that in some manner falls in between the higher noise emissions of Portland Cement Concrete (PCC) and lower emissions of Dense Grade Asphalt Concrete (DGAC). The purpose of this paper is to illustrate some curious characteristics of "average" pavement as it is employed in the TNM. Among these characteristics are what appear to be different methods for averaging the noise emissions for various vehicle types, such as automobiles, medium trucks, and heavy trucks. The inquiry concludes with an attempt to clarify exactly what constitutes an "average" when it comes to pavement noise emissions..

Requirements and best practices for traffic noise model validation

The exercise of validating a predictive model typically consists of modeling an existing real-world condition and predicting some resulting quantity of interest, and then comparing the predicted results to actual measured values for the same condition within a specified margin of error. In the case of road traffic noise models this would include modeling the characteristics of the existing roadway condition (geometry of the roadway network; number, vehicle type and speed of vehicles using the roadways; pavement types; intervening terrain; etc.) and comparing the resulting predicted noise level to actual noise measurements for the same conditions. In this paper the authors will provide a concise background of the validation purpose and process as well as a review of agency requirements and best practices for traffic noise model validation procedures.

Statistical modeling of traffic noise at intersections in a mid-sized city, India

Abstract The modeling of traffic noise is more debated around intersections due to traffic flow and road geometry complexity. The available intersection-specific traffic noise models cannot be transferred to predict the traffic noise at intersections in the mid-sized Indian cities due to traffic heterogeneity, variety in driving conditions, and vehicle compositions. This article aims to develop an intersection-specific traffic noise model by collecting data at 19 intersections in Kanpur, India. The data include a wide range of traffic, road, and weather-related variables. Furthermore, significant input variables are determined and used in the statistical regression model to develop an intersection-specific traffic noise model for the mid-sized Indian cities. This study develops a separate entrance and exit arm model based on the corresponding influencing variables. The coefficient of determination (R 2) value is 0.74 and 0.69 for the developed model at the entrance and exit arms, respectively, whereas these models achieve R 2 values of 0.73 and 0.67 in the validation step. Also, the performance of developed models is evaluated on the standard and mean absolute errors as performance metrics. This study finds that traffic volume and receiver distance are relatively the most important variables in the entrance and exit arm noise models.

Big data to streamlined app: Nationwide traffic noise prediction

A vast amount of data can be processed using high-performance computing (HPC), but sometimes, the HPC hardware requirements are cost prohibitive. However, by using HPC to create reduced-order models that require much less processing power and data storage, the benefits of HPC can be realized for a wider range of users. For example, detailed traffic noise predictions can be made on small geographic scales using the US Federal Highway Administration’s Traffic Noise Model (TNM), but when considering a nationwide scale, TNM becomes infeasible for the typical user because of computational cost. Additionally, though calculating an annually averaged overall level may be within a user’s computational capacity, incorporating temporal and spectral variability increases complexity. Using HPC on hourly traffic counts at locations across the country together with published traffic trends and TNM equations, a streamlined app has been made to efficiently predict traffic noise at roads across the nation with temporal and spectral variability. This app, which presently requires less than 700 MB of stored geospatial data and models, incorporates user inputs such as location, time period, and frequency, and gives predicted spectral levels within seconds.

RESEARCH ON THE NOISE POLLUTION FROM DIFFERENT VEHICLE CATEGORIES IN THE URBAN AREA

The noise pollution inside urban areas is one of the common problems for the inhabitants. The different levels of a noise are generated from the large amount of sources, including traffic flow on a road of urban areas. Therefore, it is essential to measure and evaluate the road traffic noise in urban areas and its population exposure in order to obtain models of a traffic noise as well as the noise mapping. The current research includes establishing a traffic noise from the different types of vehicles caused by the speed in urban areas and different road pavements (dry, wet and covered with a snow) with generalising the obtained data for more accurate using in future traffic noise models and the noise mapping. The study region is Vilnius (Lithuania), the speed range for different categories of the vehicles in the collected data is 40…130 km/h, with wide ranges of a noise level 20…180 dB. The approach presented in this research of experimental measurements is based on Statistical Pass-By (SPB) method with data proceeding upon implementation of Pearson correlation coefficient. In course of the analysis of the obtained results, it was found that the level of the curves of noise depends on the vehicles’ speed what corresponds to the best-measured values and can be used in the traffic noise models and the noise mapping.

Air pollution and traffic noise interact to affect cognitive health in older Mexican Americans

BackgroundBoth air pollution and noise exposures have separately been shown to affect cognitive impairment. Here, we examine how air pollution and noise exposures interact to influence the development of incident dementia or cognitive impairment without dementia (CIND). MethodsWe used 1,612 Mexican American participants from the Sacramento Area Latino Study on Aging conducted from 1998 to 2007. Air pollution (nitrogen dioxides, particulate matter, ozone) and noise exposure levels were modeled with a land-use regression and via the SoundPLAN software package implemented with the Traffic Noise Model applied to the greater Sacramento area, respectively. Using Cox proportional hazard models, we estimated the hazard of incident dementia or CIND from air pollution exposure at the residence up to 5-years prior to diagnosis for the members of each risk set at event time. Further, we investigated whether noise exposure modified the association between air pollution exposure and dementia or CIND. ResultsIn total, 104 incident dementia and 159 incident dementia/CIND cases were identified during the 10 years of follow-up. For each ∼2 µg/m3 increase in time-varying 1- and 5-year average PM2.5 exposure, the hazard of dementia increased 33% (HR = 1.33, 95%CI: 1.00, 1.76). The hazard ratios for NO2-related dementia/CIND and PM2.5-related dementia were stronger in high-noise (≥65 dB) exposed than low-noise (<65 dB) exposed participants. ConclusionOur study indicates that PM2.5 and NO2 air pollution adversely affect cognition in elderly Mexican Americans. Our findings also suggest that air pollutants may interact with traffic-related noise exposure to affect cognitive function in vulnerable populations.

Computing a psychological health risk assessment model for road traffic noise

IntroductionPhysical air pollutants comprising noise, temperature, and humidity are among health risk factors that influence human well-being. No study has been carried out on how the three physical pollutants affect the psychological health. Therefore, the objectives of this research are to assess road traffic noise, temperature, and relative humidity in association with psychological health. Final objective is to propose a formula capable of computing psychological health index using the three above-mentioned risk factors. MethodsAt the chosen sample points throughout Baharan Town situated in the city of Sanandaj in Iran, data of four independent variables encompassing age of the residents, traffic noise, ambient temperature, and ambient relative humidity were collected from January to November 2021. Simultaneously, the 12-item General Health Questionnaires (GHQ-12) were distributed between residents of Baharan Town to figure out their psychological health index. Age and gender was already added to GHQ-12 as two questions. Finally, a model was formulated for general health by means of multiple linear regression analysis in the SPSS software. ResultsThe results reveal that age of the residents does not have a significant relationship with psychological health index. Hence, the final multiple regression model for psychological health index depends upon three predictor factors including traffic noise, ambient temperature, and ambient relative humidity with 0.521 as its coefficient of determination (R Square). After verifying the final model, it was witnessed that computed psychological health index data were in agreement with measured data. In the end, a conceptual model is proposed that implies if no observed noise levels are available, then a road traffic noise model and its output i.e. estimated LAeq values are applied as traffic noise (input) in the aforementioned health risk assessment model. ConclusionsThe three above-mentioned predictors explain 52.1% of variance of the dependent variable i.e. psychological health index. Namely, the model's error equals 0.479. Given the amount of error that this formula generates, it is recommended new predictors such as financial conditions of the residents, ambient particulate matter (PM2.5 and PM10), ambient chemical air pollutants (SO2, CO, NO2, etc.), and other sources of environmental noise pollution (industrial noise, community noise, aircraft noise, railway noise, and harbor noise) to be used to explain more amount of variance of the dependent variable i.e. psychological health index, and therefore less error to be produced.

Wave-based numerical investigation on diffraction correction for a low-height barrier in energy-based sound propagation model for road traffic noise

In energy-based noise propagation prediction methods, such as CNOSSOS-EU in Europe and ASJ RTN-Model in Japan, sound propagation is calculated by using the equation of sound attenuation by the geometrical divergence of the sound power from a sound source and adding various attenuation corrections such as diffraction effect and ground effect. In the ASJ RTN-Model, the insertion loss due to a semi-infinite barrier to a free field is generally used for the diffraction correction for a single barrier, the top of which is regarded as a knife wedge, whereas the diffraction correction for a low-height barrier is different from that for a general single barrier. It is given as the insertion loss due to a low-height barrier to a semi-infinite barrier, the top height of which is at the ground surface level, in a free field: the diffraction correction is zero when the low-height barrier is 0 m high. However, its range of application is not clear. In this paper, the diffraction correction for a low-height barrier is discussed by comparing the results of three-dimensional wave-based numerical analysis and propagation calculations using the ASJ RTN-Model.

Study on regional road network planning based on time and noise integrated resistance function

To rationally reduce traffic noise emission from road network from the perspective of planning, a regional traffic flow distribution method combining time and noise integrated resistance function is proposed, and the method is applied in a typical region to verify its reasonability and practicability. Firstly, according to Greenshields model, the relationship among three traffic parameters (velocity, density and flow) is described, and combined with a line-source emission model of traffic noise, a univariate noise prediction model based on flow is established. Then, with the establishing of the traffic noise resistance function, an integrated resistance model considering traffic noise impact and travel time impact is proposed, which is used for traffic flow distribution while in the road network planning process. Finally, the network planning is realized utilizing the proposed method considering traffic noise. A network planning case in a typical region shows that the method can effectively reduce the noise emission of the regional network, which has practical value in traffic environment control.

Development of a Reference Energy Mean Emission Level Traffic Noise Models for Bituminous Pavement for Mid-Sized Cities in India.

Present study is towards the development of the Reference Energy Mean Emission Level (REMEL) of vehicle types involved in the mixed traffic flow on the bituminous pavements on Indian roads for a mid-sized city. Five locations were identified in Kanpur urban area under free flow traffic and good pavement conditions. A total 10700 data sets for 11 vehicle categories were collected from these locations under a sponsored research project being sponsored by Ministry of Education (MoE) and Ministry of Housing &amp; Urban Affairs (MoUD), Govt. of India, under the flagship programme of IMPRINT India. The REMEL of every vehicle category was measured using Type-1 Sound Level Meter (SLM) and was placed at a distance of 7.5 meters from the centre of the nearby carriageway while measuring the single vehicle passby event for each category. Noise level and spot speed data for bituminous pavement surface were collected for 11 vehicles categories as bus, truck, tractor-trailer, light commercial vehicle (LCV), 3-wheeler (auto, vikram), motorcycle, e-rickshaw, bicycle, tricycle and horse driven vehicle. Regression analysis was performed to obtain the REMEL equations for prediction of Leq.The study provides an interesting insight to noise emission characteristics of vehicle types for mixed traffic under Indian conditions.