Road Traffic Noise Prediction Model Research Articles

Consideration on accuracy on road traffic noise prediction model "ASJ RTN-Model 2018" in power level and equivalent noise level

This paper introduces the study on verification of prediction accuracy about road traffic noise prediction model "ASJ RTN-Model 2018" used as standard for prediction of road traffic noise in Japan. The effects of vehicle type classification, traffic volume and regional characteristics on prediction accuracy were examined for the power level LWA representing the sound source characteristics of the traveling vehicle and the equivalent noise level LAeq of the evaluation value used for noise control standards. As a result, it was confirmed that the prediction accuracy of this model was generally good regardless of the traffic volume, regional characteristics and the vehicle type classification.

Comparison of sound emission in ASJ RTN-Model 2018 and several European prediction models

The road traffic noise prediction model ASJ RTN-Model 2018 is used widely as a support tool in the environmental impact assessment of development projects in Southeast Asia as well as in Japan. In European countries, various prediction models such as Nord and CNOSSOS-EU have been published. Since 2018, the specific noise emission, which can be incorporated into CNOSSOS-EU, was proposed in several countries: CNOSSOS-NL, -FR and sonROAD. Also, the coefficients of the emission model in CNOSSOS-EU were replaced in 2021. In this study, the values of noise emission and its frequency characteristics were compared between these prediction models using immission values calculated considering the effects of the ground surface and a barrier. The results revealed that the emission values for three vehicle types in ASJ RTN-Model 2018 are almost 1 dB lower on average than those in European models, and the immission values on the reflective ground are independent of differences in the frequency characteristic between the emission models.

Sound power level determination of roads in Tokyo using aerial photographs, machine learning, and ASJ RTN-Model 2018

The traffic noise map is a visual representation of the sound level distribution in and around a road based on the sound power levels of vehicles on the road and the diffraction and reflection by the surrounding buildings. The purpose of creating the traffic noise map is to facilitate the government, researchers, industry, and the public in their efforts to track trends in transportation-related noise. To create noise maps, sound power level determination is the most important. The road traffic noise prediction model "ASJ RTN-Model 2018" has been widely used for road traffic noise prediction in Japan. Provided a method of calculating sound power level by vehicle classification, traveling speed, and driving mode. In the study, to obtain the vehicles' classification and coordinates, YOLO (You Only Look Once) was used to process the aerial photos. This study constructed sound power level distribution on the road network in Tokyo. Additionally, the estimated data were compared with the measured data to confirm the estimated data's reliability.

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.

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%.

Urban road traffic noise on human exposure assessment using geospatial technology

The sounds produced by humans, industries, transport and animals in the atmosphere that pose a threat to the health of humans or animals can be characterized as noise pollution. Adverse effects due to noise exposure can involve speech communication interference and declining learning skills of children. Highway traffic noise contributes to 80% of all noise. It has grown to a massive scale because of growth in population along the roads leading to a rapid change in land use and has evolved into a common reality in various Indian cities. The main objective of this work is to develop a road traffic noise prediction model using ArcGIS 10.3 for the busy corridors of Chennai. The collected data includes traffic volume, speed, and noise level in lateral and vertical directions. Noise levels were measured in 9 locations using a noise level meter. It is observed that the noise levels vary from 50 dB to 96 dB. It is found that the noise problem is severe in 18% of the area, and 6.3% of people are exposed to the traffic noise problem. The results obtained in this study show that the city is affected by severe noise pollution due to road traffic.

Road traffic noise mapping based on aerial photographs - sound power level determination of road vehicles

As the first step to obtain a city urban area noise map of road traffic noise, sound power levels of vehicles on the roads should be accurately estimated over a wide area. In Japan, ASJ RTN-Model 2018 was proposed as the representative road traffic noise prediction model, and by using the model sound power level of a vehicle can be determined if the vehicle type, traveling speed, and driving mode are known. As such data on urban road network, the Ministry of Land, Infrastructure and Transport of Japan publishes the road traffic census including road traffic volume and travel speed of major roads in Japan. The data, however, is limited to major roads and there is no data on minor roads. In this study, to estimate noise condition and situation on arbitrary road, a method for estimating the traveling speed and the traffic volume of vehicles on the road from aerial photographs was examined. Road traffic noise levels along several roads in Tokyo were analyzed by the proposed method and the validity of the calculation results were verified by comparing with short-time measurement results obtained along the target roads.

Development of Road Traffic Noise Prediction Model for Major Urban Roads of Kota City

Development of Road Traffic Noise Prediction Model for Major Urban Roads of Kota City

Sound power level of road vehicles running on dense asphalt pavement at various sites in Japan

Road traffic noise is one of the major environmental issues in urban areas. In Japan, the road traffic noise prediction model ``ASJ RTN-Model 2013,'' which has been developed by the Research Committee in the Acoustical Society of Japan (ASJ), is used widely in environmental impact assessment. Several calculation formulas for the A-weighted sound power level, LWA, of each type of road vehicle are specified in this model. Among them, the formulas for LWA of road vehicles on dense asphalt pavement have been derived using the measurement data acquired in 1991–1998. In this study, to improve the accuracy of LWA for dense asphalt pavement, pass-by noise measurements were performed on 40 actual roads during the last decade. The results revealed that LWA of passenger cars, including hybrid vehicles and mini-sized vehicles, was 1.1 dB lower than that adopted in ASJ RTN-Model 2013. Regarding the other vehicle types such as large-sized vehicles, the differences between LWA obtained from field measurements and those in the model were 0.5 dB or less, and no marked changes in LWA were found.

Road traffic noise prediction model ``ASJ RTN-Model 2018'': Report of the Research Committee on Road Traffic Noise

Road traffic noise prediction model ``ASJ RTN-Model 2018'': Report of the Research Committee on Road Traffic Noise

Development of a road shoulder's equivalent sound source traffic noise prediction model

The accuracy of traffic noise prediction models (TNMs) is impaired by affecting factors and system errors. In addition, the traditional traffic noise source cannot be tested as the sound source is generated in the centreline of the road. To address these limitations, a TNM based on the equivalent sound source at the road shoulder was developed. First, the equivalent traffic flow based on the acoustic-equivalent conversion coefficient was calculated and the traffic noise source intensity model was deduced. The shoulder's sound pressure level (SPL) data were then used to formulate a single-vehicle equivalent SPL model and correct the traffic noise source intensity model. The propagation model was fitted according to the attenuation law that traffic noise strength attenuates gradually from the shoulder to the road outside and fluctuates periodically. The results of a case study showed that the absolute percentage error of the proposed model's prediction was 2·3% compared with the measured value, which was better than the performance of the current model suggested in the Chinese specification. The proposed prediction model provides a friendly and less time-consuming approach for city planners and traffic engineers to conduct freeway traffic noise prediction and assessment.

Theoretical Comparison of the Effects of Different Traffic Conditions on Urban Road Traffic Noise

Road traffic noise is one of the most relevant sources in the environmental noise pollution of the urban areas where dynamics of the traffic flow are much more complicated than uninterrupted traffic flows. It is evident that different traffic conditions would play the role in the urban traffic flow considering the dynamic nature of the traffic flow on one hand and presence of traffic lights, roundabouts, etc. on the other hand. The main aim of the current paper is to investigate the effect of different traffic conditions on urban road traffic noise. To do so, different traffic conditions have been theoretically generated by the Monte Carlo Simulation technique following the distribution of traffic speed in the urban roads. The “ASJ RTN-Model” has been considered as a base road traffic noise prediction model which would deal with different traffic conditions including steady and nonsteady traffic flow that would cover the urban traffic flow conditions properly. Having generated the vehicles speeds in different traffic conditions, the emitted noise (LWA) and subsequently the noise level at receiver (LA) were estimated by “ASJ RTN-Model.” Having estimated LWA and LA for each and every vehicle in each traffic condition and taking the concept of transient noise into account, the single event sound exposure levels (SEL) in different traffic conditions are calculated and compared to each other. The results showed that decelerated traffic flow had the lowest contribution, compared to congestion, accelerated flow, free flow, oversaturated congestion, and undersaturated flow by 16%, 14%, 12%, 12%, and 10%, respectively. Moreover, the distribution of emitted noise and noise level at receiver were compared in different traffic conditions. The results showed that traffic congestion had considerably the maximum peak compared to other traffic conditions which would highlight the importance of the range of generated noise in different traffic conditions.

A six-category heavy vehicle noise emission model in free-flowing condition

Annoyance and sleep disturbance caused by transportation noise are frequently associated with heavy vehicles. The ability to accurately predict heavy vehicle noise impact using conventional road traffic noise prediction methods has reduced over the years as the variety of heavy vehicles have increased progressively and the predominant long haul freight vehicle is trending towards larger trucks with a greater number of axles. In this paper, a six-category heavy vehicle source emission model in free-flowing condition has been developed based on the state-wide road setting in New South Wales, Australia. The six-category model allows traffic noise across the road network, carrying a diverse fleet of heavy vehicles, to be predicted with notably higher accuracy and precision in comparison to conventional models that aggregate heavy vehicles into one, or at most, two distinct categories. A comparative analysis is carried out to examine the source emission from various traffic mix scenarios in urban areas and along major freight routes. Current findings also highlight the importance of distinguishing regional characteristics in a harmonised road traffic noise prediction model.

Road traffic noise prediction model for heterogeneous traffic based on ASJ-RTN Model 2008 with consideration of horn

This research aimed to predict the noise produced by the traffic in the road network in Makassar City using ASJ-RTN Model 2008 by calculating the horn sound. Observations were taken at 37 survey points on road side. The observations were conducted at 06.00 - 18.00 and 06.00 - 21.00 which research objects were motorcycle (MC), light vehicle (LV) and heavy vehicle (HV). The observed data were traffic volume, vehicle speed, number of horn and traffic noise using Sound Level Meter Tenmars TM-103. The research result indicates that prediction noise model by calculating the horn sound produces the average noise level value of 78.5 dB having the Pearson’s correlation and RMSE of 0.95 and 0.87. Therefore, ASJ-RTN Model 2008 prediction model by calculating the horn sound is said to be sufficiently good for predicting noise level.

An Empirical Model of Road Traffic Noise on Heterogeneous Traffic Situation

The motorcycle domination on heterogeneous traffic situation in many cities in developing countries including Indonesia leads to the decreasing of environment qualities such noise pollution. Regarding the road traffic noise (RTN) pollution, this paper attempts to develop an empirical model for a RTN prediction model. The model based on a motorcycle unit as reference unit to consider flow rate of the road traffic which dominated by motorcycles. The study collected the RTN data such volume of each vehicle types, i.e., motorcycle; light vehicle; and high vehicle, and the noise level on the forty arterial roads in Makassar, Indonesia. The survey methods based on the traffic count method and the measurement noise level using a video camera and a sound level meter, respectively. We collected data during ten minutes of each one-hour period of each road. The empirical relationship models between the noise level and the traffic volume based on the motorcycle unit were developed using various types of regression models. The results showed that the polynomial model is more significant than the other models. We expected that the model provides a basic RTN prediction model in order to simulate some measures of the traffic management system in reducing the RTN level in Makassar City.

Addendum to: Comparison of Road Traffic Noise Prediction Models: CoRTN, TNM, NMPB, ASJ RTN

Addendum to: Comparison of Road Traffic Noise Prediction Models: CoRTN, TNM, NMPB, ASJ RTN

A field survey on sound power level and spectrum of Japanese road vehicles

For precise prediction and appropriate countermeasure of road traffic noise, to grasp the sound power level and power spectrum of running vehicles is important. In Japan, there exists a road traffic noise prediction model “ASJ RTN-Model 2013,” which provides a simple sound source modeling and simple calculation methods of the noise propagation. Recently, vehicles with good fuel-efficiency such as hybrid vehicles are increasing in the category of passenger car. Such vehicles may have less noise emission than previous gasoline engine vehicles. The authors conducted field surveys to grasp the noise of the current road vehicles especially in the category of passenger cars. As a result of that, in terms of A-weighted sound power levels, which can be expressed as a function of running speed, the difference between gasoline engine vehicles is quantitatively discussed. As for the power spectrum, there is no appreciable difference between gasoline engine vehicles and hybrid vehicles. However, both types of vehicle...

Comparison of Road Traffic Noise Prediction Models: CoRTN, TNM, NMPB, ASJ RTN

This research investigates the prediction accuracy of Calculation of Road Traffic Noise (CoRTN), Traffic Noise Model version 2.5 (TNM), Acoustical Society of Japan-Road Traffic Noise (ASJ RTN) and Noise Propagation Computation Method Including Meteorological Effects (NMPB). A test case model was constructed to investigate the combination of ground effect and shielding in each methodology. The predictions for CoRTN are implausible, because predicted noise levels for soft ground are equal to hard ground in some locations. This anomaly was not present in predicted noise levels from TNM, ASJ RTN and NMPB for the same test case. Noise levels for a rural freeway and an urban freeway were predicted for each methodology and compared with noise monitoring results. CoRTN resulted in high over-prediction. TNM and NMPB produced similar results, with TNM predicting slightly higher on average and NMPB having the lowest standard deviation in the prediction error. ASJ RTN resulted in high under-prediction.

International scale implementation of the CNOSSOS-EU road traffic noise prediction model for epidemiological studies

The EU-FP7-funded BioSHaRE project is using individual-level data pooled from several national cohort studies in Europe to investigate the relationship of road traffic noise and health. The detailed input data (land cover and traffic characteristics) required for noise exposure modelling are not always available over whole countries while data that are comparable in spatial resolution between different countries is needed for harmonised exposure assessment. Here, we assess the feasibility using the CNOSSOS-EU road traffic noise prediction model with coarser input data in terms of model performance. Starting with a model using the highest resolution datasets, we progressively introduced lower resolution data over five further model runs and compared noise level estimates to measurements. We conclude that a low resolution noise model should provide adequate performance for exposure ranking (Spearman's rank = 0.75; p < 0.001), but with relatively large errors in predicted noise levels (RMSE = 4.46 dB(A)).

Road traffic noise prediction model ``ASJ RTN-Model 2013'': Report of the Research Committee on Road Traffic Noise

Road traffic noise prediction model ``ASJ RTN-Model 2013'': Report of the Research Committee on Road Traffic Noise