Traffic Noise Model Research Articles

Modeling roadway traffic noise in a hot climate using artificial neural networks

Road traffic noise is the most prevalent form of environmental noise pollution, and it can either be measured in the field or predicted through verified mathematical models. While many road traffic noise models are available around the world, these models cannot be simply generalized because local conditions affecting such noise (e.g., vehicle type and weather) vary from one locality to another. In this paper, the Artificial Neural Network (ANN) technique was employed to model road traffic noise in a city with known hot climate, namely Sharjah City in United Arab Emirates. Toward this end, field data were collected from three different road sites which resulted in more than 420 hourly measurements of noise level, traffic volume and classification, average speed, and roadway temperature. Overall, a total of 16 feed-forward back-propagation ANN models with one and two hidden layers were undertaken in this research. The best-performing models were compared with two conventional models: The Basic Statistical Traffic Noise model (BSTN) and the Ontario Ministry of Transport Road Traffic Noise model (ORNAMENT). In general, results showed that ANN models outperformed the conventional models. Furthermore, the performance of both ANN and conventional road noise models was further improved by including roadway temperature. While the reported work emphasizes the importance of adapting models to different local conditions, it also encourages others to consider the roadway temperature when modeling traffic noise, especially in areas of hot climate. Given that ANN models are often designated as “black-box,” a few techniques were utilized to expand the capabilities of ANN models to offer explanatory insights in modeling the inter-relations between the variables considered in this study. Generally, it was found that the ANN model was capable of identifying these relations as expected. In-depth analysis showed that the distance from the edge of road was found to be the most significant contributing factor whereas heavy-vehicle volume was surprisingly found to be the least.

Traffic noise level predictions for buildings with windows opened for natural ventilation in urban environments

The aims of this research are to discuss acoustical building performance for natural ventilation systems and to predict traffic noise level for inhabitants residing in urban settlements. Natural ventilation systems use outdoor air to improve the quality of indoor environments and can reduce energy consumption. However, natural ventilation systems create an acoustical problem. For example, if windows are opened for natural ventilation, outdoor traffic noise can disrupt people working and sleeping in the building. In this study, a new method that combines an algorithm to predict traffic noise levels (calculation of road traffic noise) with the Federal Highway Administration Traffic Noise Model's vehicle noise spectra correctly predicts the properties of noise propagation and attenuation through openings in urban environments, as well as outdoor sound propagation, sound reduction index, and indoor sound transmission. In order to develop this method, various traffic noise prediction methods to calculate sound attenuation during propagation outdoors based on International Organization for Standardization 9613 were analyzed. This study also describes the noise attenuation effect from surrounding elements, such as building envelope, location of openings, opening ratio, and indoor boundary conditions. The results of the simulations indicate that the integrated noise evaluation method can successfully predict the acoustic performance of natural ventilation systems in urban areas.

Adaptive noise risk modelling: fuzzy logic approach

ABSTRACTRecently, noise pollution has taken place as one of the most human health disorders. The main sources of noise are coming from human malpractices on reckless car driving and the use of loud speakers in different festivals at different places. Behavioural study explores that in many places the local usual road traffic noise level exceeds the normal standards. However, few people have adaptive capacity to ignore the effect of ambient noise pollution within considerable limits. Thus, in this article, we have developed an adaptive traffic noise model over the vulnerable society of a specific noise-prone zone. We develop a fuzzy logic to analyse the noise risk, and then it has been compared by the odds ratio of the experimental data. Moreover, we have considered the normality and non-normality in participation for various noise parameters, namely noise level, exposure time and affected age group of the people of a particular place as well. Finally, graphical illustrations are made for global justification of the model.

Assessment of traffic noise levels in urban areas using different soft computing techniques.

Available traffic noise prediction models are usually based on regression analysis of experimental data, and this paper presents the application of soft computing techniques in traffic noise prediction. Two mathematical models are proposed and their predictions are compared to data collected by traffic noise monitoring in urban areas, as well as to predictions of commonly used traffic noise models. The results show that application of evolutionary algorithms and neural networks may improve process of development, as well as accuracy of traffic noise prediction.

Comparison of ARIMA and ANN approaches in time-series predictions of traffic noise

The paper analyzes the long-term noise monitoring data using autoregressive integrated moving averages (ARIMA) modeling technique and artificial neural networks (ANNs) methodology. Box-Jenkins ARIMA and ANN approach have been utilized to simulate daily equivalent LDay (06-22h) and LNight (22-06h) in A and C weightings for a period of 1 year. The forecasting performance is ascertained using the statistical tests. The work draws a comparison of time-series ARIMA and ANN approach for ascertaining their suitability for traffic noise modeling and forecasting. It is observed that the artificial neural network (ANN) models outperform the ARIMA models so developed. The pattern of ARIMAf orecasting models is directional and as such the time-series predictive model utilizing ANN approach has demonstrated superior performance over the ARIMA model.

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.

A traffic noise model for road intersections in the city of Cartagena de Indias, Colombia

Road traffic noise models are fundamental tools for designing and implementing appropriate prevention plans to minimize and control noise levels in urban areas. The objective of this study is to develop a traffic noise model to simulate the average equivalent sound pressure level at road intersections based on traffic flow and site characteristics, in the city of Cartagena de Indias (Cartagena), Colombia. Motorcycles are included as an additional vehicle category since they represent more than 30% of the total traffic flow and a distinctive source of noise that needs to be characterized. Noise measurements are collected using a sound level meter Type II. The data analysis leads to the development of noise maps and a general mathematical model for the city of Cartagena, Colombia, which correlates the sound levels as a function of vehicle flow within road intersections. The highest noise levels were 79.7dB(A) for the road intersection María Auxiliadora during the week (business days) and 77.7dB(A) for the road intersection India Catalina during weekends (non-business days). Although traffic and noise are naturally related, the intersections with higher vehicle flow did not have the highest noise levels. The roadway noise for these intersections in the city of Cartagena exceeds current limit standards. The roadway noise model is able to satisfactorily predict noise emissions for road intersections in the city of Cartagena, Colombia.

Traffic Noise Models for Curved Roads

Abstract The paper presents two theoretical models for traffic noise level distribution on curved horizontal roads. In the case of vehicles moving on a given route, one can consider, in terms of sound field, that the granular traffic is equivalent for short periods with a quasi-continuous noise flow. When computing and modelling the noise level generated by traffic on roads with complex trajectory, it is common to treat the route as a sum of small length road segments, each being assimilated with a linear noise source. This paper started from the assumption that the route can be decomposed into a sequence of linear and arc-shaped road segments, each of which is treated as a linear respectively curved noise source. An arc-shaped road segment is modelled by a tubular vibrating surface, of circular or rectangular section. In the case of rectangular section, the vibrating blade emits complex sounds on its both vertical sides and the generated sound field can be described more clearly, qualitatively and quantitatively, through intensity distribution. The theoretical models presented in the paper have direct application to the traffic noise prediction and noise maps drawing

Study on the relation between urban planning and noise level

Abstract Different streets of a city of Spain were randomly selected and analyzed, extracting 135 different urban variables. The urban variables were compared with measured noise levels, and the possible significance in the relationships among them was analyzed. From the variables with a significant correlation, a multiple regression model for urban traffic noise was developed, which allows explaining 63% of the variability of urban noise. In this regression model, only eight of the initial urban variables were included. The obtained model was validated, and its prediction capacity was analyzed with 30 new randomly selected independent sampling points, showing a global uncertainty lower than 2 dBA, similar to that obtained in noise mapping techniques. The proposed methodology could be extrapolated to other cities, and the obtained models could be an important tool for city planning agents.

Honking noise corrections for traffic noise prediction models in heterogeneous traffic conditions like India

Abstract In developing countries like India, the nature of the composition of traffic is heterogeneous. A heterogeneous traffic flow consists of vehicles that have different sizes, speeds, vehicle spacing and operating characteristics. As a result of the widely varying speeds, vehicular dimensions, lack of lane disciplines, honking becomes inevitable. In addition, it changes the urban soundscape of developing countries. In heterogeneous traffic conditions, horn events increase noise level (Lden) by 0.5–13 dB(A) as compared to homogenous traffic conditions. Therefore, the traffic prediction models that are used for homogenous traffic conditions are not applicable in heterogeneous traffic conditions. To increase the accuracy of noise prediction models, in depth understanding of heterogeneous traffic noise is required. Understanding the real traffic noise characteristics requires quantification of some of the basic traffic flow characteristics such as speed, flow, Level Of Service (LOS) and density. In a given roadway, the noise level changes with density and LOS on the road. In this paper, a new factor for horn correction is introduced with respect of Level Of Service (LOS). The horn correction values can be incorporated in traffic noise models such as CRTN, FHWA, and RLS 90, while evaluating heterogeneous traffic conditions.

Dynamic road traffic density estimation employing noise mapping with the use of grid supercomputing

A noise prediction model of a large city agglomeration was elaborated in order to allow for a dynamic road traffic density estimation in vehicular networks. The implemented application adopts the model fed with traffic noise data based on frequently refreshed L DEN levels. Calculations were made with the use of the numerical model developed for his purpose and then implemented on the PL-Grid supercomputing infrastructure. Data obtained through supercomputing and through the use of a standard noise map computing software were collated with measured levels acquired from the acoustic city monitoring system and then analyzed. The comparison performed afterwards shows a relatively good accuracy of the developed model. The numerical model of traffic noise and its main sources are briefly characterized. A full day dynamic noise map can be browsed as a set of 24 noise maps, one for each hour of the day which in turn allows for vehicular traffic density estimation based exclusively on acoustical data. [Research was subsidized by the Polish National Centre for Research and Development within the Grant No. OT4-4B/AGH-PG-WSTKT.]

Acoustical evaluation of residential laneways and laneway housing

Laneway housing (LWH), a free-standing, small wood-frame house (1 to 1.5 stories high and less than 900sf in floor area), is an increasingly popular product of Vancouver B.C.’s high-density urban growth policy. LWH is subject to multiple noise concerns due to its small architectural form and siting in laneways typically designed for garage access and utilities. (Laneways are approximately 16 ft wide and 12 to 18 ft high.) Case study investigations examined residential laneways as potential urban canyons and the acoustical performance of LWH envelope. Methods employed include ASTM field measurements, software models (Odeon and AFMG Soundflow), and the National Research Council of Canada traffic noise model (comparable to the FHWA model). Metrics evaluated include rate of attenuation over propagation distance through a laneway, façade transmission loss, and room absorption. Results show that certain laneways function as urban canyons, measuring nearly 20 dBA higher in SPL at the LWH façade than in laneways without urban canyon characteristics. Indoor SPL due to traffic noise transmitting through the building envelope were above 45 dBA. At least half of the case studies investigated did not meet noise criteria for residential health. These investigations emphasize the need for acoustical specifications in design and construction guidelines for LWH.

Assessment of methods for simplified traffic noise mapping of small cities: Casework of the city of Valdivia, Chile

In many countries such as Chile, there is scarce official information for generating accurate noise maps. Therefore, specific simplification methods are becoming a real need for the acoustic community in developing countries. Thus, the main purpose of this work was to evaluate and apply simplified methods to generate a cost-effective traffic noise map of a small city of Chile. The experimental design involved the simplification of the cartographic information on buildings by clustering the households within a block, and the classification of the vehicular traffic flows into categories to generate an inexpensive noise map. The streets have been classified according to the official road classification of the country. Segregation of vehicles from light, heavy and motorbikes is made to account for traffic flow. In addition, a number of road traffic noise models were compared with noise measurements and consequently the road traffic model RLS-90 was chosen to generate the noise map of the city using the Computer Aided Noise Abatement (CadnaA) software. It was observed a direct dependence between noise levels and traffic flow versus each category of street used. The methodology developed in this study appears to be convenient in developing countries to obtain accurate approximations to develop inexpensive traffic noise maps.

Assessment of Traffic Noise and Development of Noise Models for National Highway-06, Passing through Chhattisgarh, India

Background/Objectives: Traffic noise pollution is a major concern of the societies residing near the busy National highways. The recognition of this noise as the main source of environmental pollution has compelled the construction engineers & traffic planners to bring forth the models that enable us to predict noise from basic variables. These noise prediction models are very much needed as helping tools in the design of roads& traffic and in assessing frequent changes in traffic noise scenario. In India, vehicular traffic is increasing tremendously, which has given birth to overcrowded roads and simultaneously to noise pollution. Therefore, it is expected to develop noise prediction models suitable for national highways pertaining to Indian context Methods: This paper is the zest of a research conducted on National highway-06 with the sole purpose of setting up traffic noise models based on existing traffic conditions. Traffic noise levels have been measured by noise meter SL4023 SD by adopting standard procedure. Findings: In the process of observation & measurement of noise data, noise levels were found alarmingly& consistently high. After the analysis coefficient of correlation (R2) for the developed model for Supela (Location 1) and Power house (Location 2) are found 0.78 and0.70 respectively, which is lying in acceptable range. Applications: It can be concluded that traffic noise prediction models so developed is theoretically acceptable and conveniently applicable for real traffic conditions of National highway-06passing through Chhattisgarh, India.

Evaluation and Modelling of Traffic Noise on the Asian Highway in Golestan National Park, Iran

The increasing number of vehicles on Iran’s highways and major roads has led to an increase in noise levels. As a result, traffic is now considered a main source of noise pollution. This paper reports on the modelling of traffic noise levels in Golestan National Park, Golestan using vehicle data and other environmental features. For the evaluation of noise and the recording of independent environmental variables, Sampling stations were selected using a systematic-random method at 76 points at various distances and between 0-250 meters from the road. At each sampling point, traffic flow (number and speed of vehicles, number of horn beeps) was measured for 15 minutes from 8 am to 8 pm. Simultaneously other environmental variables were assessed, including the geometry of the road surface and location conditions .The best multivariable regression based on the correlation coefficient (R) and the coefficient of determination (R2) was achieved. The R-square (73%) and the adjusted R-square (68%) of the regression equation were 73% and 68% respectively. The results of modelling show that the most important variables affecting noise pollution are distance from the road, roughness coefficient, speed of medium-weight vehicles, relative humidity, and height and number of light vehicles. There is a negative correlation with distance from the road and noise pollution.The accuracy of the model was found to be about ±5 dB. Therefore, the model is suggested for the prediction of traffic noise on the Asian Highway in Golestan National Park.

GIS based spatial noise impact analysis (SNIA) of the broadening of national highway in Sikkim Himalayas: a case study

Mountainous areas create a complex and challenging environment to conduct noise impact analysis of development projects. This paper presents a noise impact analysis methodology using Geographic Information Systems (GIS) and Traffic Noise Model (FHWA TNM 2.5) to portray spatial distribution of noise due to the broadening of the national highway in the mountainous terrain of East Sikkim. Two noise level indices viz., Hourly Equivalent Sound Level (Leq(H)) and Day and Night Average Sound Level (Ldn) were calculated for the year 2004 as pre-project scenario, 2014 as project implementation scenario and 2039 as post-project scenario. The overall trend shows that the proportion of area under adverse noise level decreases from pre-project scenario to project implementation scenario. Over the time the adverse noise impact in the post-project scenario reaches very close to pre-project scenario in case of both the noise indices. Overlay analysis of noise based landuse maps over actual landuse map show that non-compliance of noise based landuse will show similar trend. This trend is mainly attributed to traffic composition and highway broadening induced-traffic volume. The study shows that TNM and spatial interpolation of noise data using Empirical Bayesian Kriging (EBK) are reliable tools to perform noise impact analysis in mountainous areas. Multiple regression analysis show that, radial distance and elevation difference of noise receivers from the nearest point in the highway are significant predictors of Leq(H) and Ldn at lower percentage of heavy trucks in traffic composition.

Highway traffic noise modeling and estimation based on vehicles volume and speed

Traffic noise estimation models are useful in evaluation of the noise pollution in current circumstances. They are helpful tools for design and planning new roads and highways. Measurement of average traffic noise level is possible when traffic speed and volume are known. The objective of this study was to devise a model for prediction of highway traffic noise levels based on current traffic variables in Iran. The design of this model was to take the impact of traffic congestion into consideration and to be field tested. This study is a library research augmented by field study conducted on Saeedi Highway located south west of Tehran. The period for the field study lasted 5 days from 7-12 February, 2013. This study examined liner and non-liner methods in formulation of its model. Liner method without a fixed coefficient was the best fit for the intended model. The proposed model can serve as a decision making tool to estimate the impact of key influential factors on sound pressure levels in urban areas in Iran.

Spatial Techniques to Visualize Acoustic Comfort along Cultural and Heritage Routes for a World Heritage City

This paper proposes to visualize acoustic comfort along tourist routes. Route-based tourism is crucial to the sustainability of tourism development in historic areas. Applying the concept of route-based tourism to guide tourists rambling along cultural and heritage routes can relieve overcrowded condition at hot scenic spots and increase the overall carrying capacity of the city. However, acoustic comfort along tourist routes is rarely addressed in academic studies and decision-making. Taking Macao as an example, this paper has studied pedestrian exposure to traffic noise along the cultural and heritage routes. The study is based on a GIS-based traffic noise model system with a high spatial resolution down to individual buildings along both sides of the street. Results show that tourists suffer from excessive traffic noise at certain sites, which may have negative impact on the promotion of route-based tourism in the long run. In addition, it is found that urban growth affects urban form and street layout, which in turn affect traffic flow and acoustic comfort in urban area. The present study demonstrates spatial techniques to visualize acoustic comfort along tourist routes, and the techniques are foreseen to be used more frequently to support effective tourism planning in the future.

Comparison of ANN and Analytical Models in Traffic Noise Modeling and Predictions

This paper demonstrates the applications of artificial neural networks to predict the equivalent continuous sound level $$(L_\mathrm{Aeq})$$ and 10 Percentile exceeded sound level $$(L_\mathrm{10})$$ generated due to traffic noise for various locations in Delhi. A Model based on back-propagation neural network was trained, validated, and tested using the measured data. The work shows that the model is able to produce accurate predictions of hourly traffic noise levels. A comparative study shows that neural networks out-perform the multiple linear regression models developed in terms of total traffic flow and equivalent traffic flow. The prediction model proposed in the study may serve as a vital tool for traffic noise forecasting and noise abatement measures to be undertaken for Delhi city.

Evaluation and validation of the Federal Highway Administration: Traffic noise model in a sanitized environment

The development of powerful physics based simulation models that are dependent on stochastic parameters does not always guarantee reliability. The Federal Highway Traffic Noise Model (TNM) is such a complex model that relies on Reference Energy Mean Emission Level (RMEL) from highway vehicle-type average speed and volume to predict noise propagation in far-field environment. Although the TNM accounts for static parameters such as ground type, it does not account for changing noise source or noise propagation medium. Currently, when validating TNM, the neglected dynamic parameters in the model are usually accounted for by calibrating the model to agree with measured data. However, a model is not necessarily validated when its predicted output is compared to measured data and updated to produce desired values. This paper will review current validation procedures and assess TNM’s reliability to predict noise propagation from a highway line source within a sanitized environment. Sanitized environment is an open field within which noise propagation is independent of dynamic variables. By validating TNM within a sanitized environment TNM’s residual prediction error due only to variation in the traffic parameters (vehicle type, speed, and flow rate) could be identified and the reliability of REML used in TNM could be confirmed.