Development of a highway traffic noise prediction model that considers various road surface types

A highway traffic noise prediction model has been developed for environmental assessment in South Korea. The model is based on an outdoor sound propagation method and is fully compliant with ISO 9613 and the sound power level (PWL) estimation for a road segment, as suggested in the ASJ Model-1998 that is based on PWLs. Due to that model’s selection of two pavement types, such as asphalt or concrete pavement, an unacceptable traffic noise prediction is made in cases where the road surface is different from that on which the model is based. In order to address this problem, several road surface types are categorized, and the PWL of each surface type is determined and modeled by measuring the noise levels obtained from newly developed methods. An evaluation of the traffic noise prediction model using field measurements finds good agreement between predicted and measured noise levels.     

A novel traffic-noise prediction method for non-straight roads

In view of the well-recognized need for the availability of simple traffic noise–prediction methods for non-straight roads, such as interchanges and flyovers, an approximate traffic noise–prediction model, suitable for non-straight roads, was developed following the modeling process of the Federal Highway Administration’s (FHWA) traffic-noise model. The approximate model is simpler than the FHWA model when the non-straight road is divided into several road sections, their traffic-noise levels calculated individually, and all the sound levels superimposed to obtain the total traffic-noise level of the entire non-straight road. The rational length of the small road section was investigated. The traffic-noise levels predicted using the approximate and the FHWA models were compared through an example and were shown to differ only very marginally. The approximate model is thus suitable for traffic-noise prediction for non-straight roads.     

A road traffic noise pattern simulation model that includes distributions of vehicle sound power levels

Annoyance, sleep disturbance and other health effects of road traffic noise exposure may be related to both level and number of noise events caused by traffic, not just to energy equivalent measures of exposure. Dynamic traffic noise prediction models that include instantaneous vehicle noise emissions can be used to estimate either of these measures. However, current state-of-the-art vehicle noise emission models typically consider a single emission law for each vehicle category, whereas measurements show that the variation in noise emission between vehicles within the same category can be considerable. It is essential that the influence of vehicles that are producing significantly more (or less) noise than the average vehicle are taken into account in modeling in order to correctly predict the levels and frequency of occurrence of road traffic noise events, and in particular to calculate indicators that characterize these noise events. Here, an approach for predicting instantaneous sound levels caused by road traffic is presented, which takes into account measured distributions of sound power levels produced by individual vehicles. For the setting of a receiver adjacent to a dual-lane road carrying free flow traffic, the effect of this approach on estimated percentile levels and sound event indicators is investigated.     

Statistical classification of road pavements using near field vehicle rolling noise measurements

Low noise surfaces have been increasingly considered as a viable and cost-effective alternative to acoustical barriers. However, road planners and administrators frequently lack information on the correlation between the type of road surface and the resulting noise emission profile. To address this problem, a method to identify and classify different types of road pavements was developed, whereby near field road noise is analyzed using statistical learning methods. The vehicle rolling sound signal near the tires and close to the road surface was acquired by two microphones in a special arrangement which implements the Close-Proximity method. A set of features, characterizing the properties of the road pavement, was extracted from the corresponding sound profiles. A feature selection method was used to automatically select those that are most relevant in predicting the type of pavement, while reducing the computational cost. A set of different types of road pavement segments were tested and the performance of the classifier was evaluated. Results of pavement classification performed during a road journey are presented on a map, together with geographical data. This procedure leads to a considerable improvement in the quality of road pavement noise data, thereby increasing the accuracy of road traffic noise prediction models.     

Traffic noise reduction due to the porous road surface

Porous road surfaces reduce road traffic noise. A new method of noise reduction assessment is proposed. The noise generated by a few vehicles was measured two times: on an old surface with the dense asphalt and on a new surface with the porous asphalt. Subjective assessments of drive-by noise suggest that the sound exposure and the road surface coefficient can be used as the acoustical characteristics of a road surface. Their average values, with the average number of vehicles passing the receiver during a day or night, makes it possible to predict the equivalent continuous A-weighted sound pressure level for the new road surface. This is the main objective of this paper.     

Statistical tyre/road noise modeling in Hong Kong on friction course

Based on the close proximity (CPX) method specified in ISO/DIS 11819-2, we recorded and analyzed the instantaneous tyre/road sound pressure levels with 9 road sections that are constructed with the same pavement surfacing materials, that is, friction course. A total of 1320 segments were made in urban areas with a pair of SRTT (Standard Road Test Tyre). We tried to relate the tyre/road noise with the instantaneous acceleration, speed, air temperature, road temperature, road gradient, road surface age to develop a multi-variants model. It was subsequently found that a simple tyre/road noise model linking driving speed and acceleration is the best model. The model provides an easy way to estimate the instantaneous tyre/road noise level. As the tyre/road noise is becoming more dominant component of the road traffic noise, our proposed model has the potential to improve the current practice in estimating the road traffic noise.     

An experimental procedure to obtain sound power level of tyre/road noise under Coast-By conditions

The rolling noise from tyre–pavement interaction represents the greatest sound contribution from a vehicle when cruising at a high speed. To evaluate the sound levels from this source, existing standardized methods that establish different measurement procedures in both the immediate tyre surroundings, for example the Close-Proximity method, as well as at greater distances, as the Coast-By method. A fundamental parameter that can quantify the sound generation of a source is its sound power level. The standardized methods establish procedures to measure the sound pressure level but not the power level of a tyre as a noise source. For this reason, this paper presents a novel methodology based on sound pressure measurements to obtain the sound power level that a vehicle emits in Coast-By conditions, where noise is generated at tyre/road interaction. The paper describes the testing procedure used to obtain the sound power level, and it is accompanied by a mathematical simulation that studies the feasibility of the proposal. Finally, the proposed methodology is further validated through a field study.     

Sound quality prediction of vehicle interior noise using deep belief networks

The sound quality of vehicle interior noise strongly influences passengers’ psychological and physiological perceptions. To predict the sound quality of interior noise, a vehicle road test with four compact cars has been conducted. All recorded interior noise signals have been denoised via a discrete wavelet transform (DWT) denoising procedure and subsequently evaluated subjectively through the anchor semantic differential (ASD) test by a jury. In addition, a novel prediction method, namely, regression-based deep belief networks (DBNs), which substitute the support vector regression (SVR) layer for the linear softmax classification layer at the top of the general DBN’s structure, has been proposed to predict the interior sound quality. The parameter selection of the DBN model has been compared and studied using a grid search. In addition, four conventional machine-learning-based methods have been introduced to enable a comparison of the performance with the newly developed DBNs. Furthermore, the feature fusion ability of DBNs has been studied by varying the amount of information that the dataset offers. The results show the following: (1) The accuracy and robustness of the proposed DBN-based sound quality prediction approach are better than those of the 4 other referenced methods. (2) The multiple-feature fusing process can strongly affect the prediction performance. (3) Finally, the unsupervised pre-training process of the DBNs can enhance the information fusing ability. Finally, the newly proposed regression-based DBN approach may be extended to address other vehicle noises in the future.     

基于改进声线跟踪法的室内交通噪声动态模拟

对碰撞点的有效性判断是传统声线跟踪法的一个重要步骤,在计算复杂的室内空间问题时计算量较大,为解决这一问题采用了一种与空间剖分相结合的改进的声线跟踪法。将该方法与微观交通流仿真和车辆噪声排放模型进行结合,实现了道路交通噪声透过窗户在多连通室内空间传播的动态模拟。最后采用该方法对相同道路和交通流条件下不同建筑朝向的4种布局室内噪声进行动态模拟。分析了室内交通噪声的大小和分布与建筑物布局、窗户的朝向、窗户的形式和面积等因数之间的关系。结果表明:窗户正对道路的房间比窗户侧对道路的房间噪声高4～6 d B(A),房间内等效声级与窗户面积的对数成正比关系。     

Method of control of the noise level of road vehicles in use

The elaboration of the method of control of noise generated by moving road vehicles has been made possible by using previous measurements of external noise produced by each of those particular vehicles. The results have made it possible to lay down the requirements for the design of specialised instrumentation capable of making such measurements. The advantage of this development is that it makes it possible for inexperienced technical staff to obtain the required acoustic measurements. The previously employed methods of noise prediction have required preceisely defined measuring conditions at the measuring site. The measurements, besides being laborious and time-consuming, give little information concerning the technical condition of the vehicle. On the contrary, with the method presented in this paper it is possible to measure not only the sound pressure level of the general external noise of the vehicle but also the band levels in the two frequency bands which are characteristic of the working engine at a definite speed of rotation, thus giving a preliminary estimation of the vehicle's noisiness. Defining the limiting values for noise level and band levels will enable excessively noisy vehicles to be eliminated.The results of measurements obtained with the above technique for various groups of vehicles are presented in this paper.     

Fast prediction of aerodynamic noise induced by the flow around a cylinder based on deep neural network

Accurate and fast prediction of aerodynamic noise has always been a research hotspot in fluid mechanics and aeroacoustics. The conventional prediction methods based on numerical simulation often demand huge computational resources, which are difficult to balance between accuracy and efficiency. Here, we present a data-driven deep neural network (DNN) method to realize fast aerodynamic noise prediction while maintaining accuracy. The proposed deep learning method can predict the spatial distributions of aerodynamic noise information under different working conditions. Based on the large eddy simulation turbulence model and the Ffowcs Williams-Hawkings acoustic analogy theory, a dataset composed of 1216 samples is established. With reference to the deep learning method, a DNN framework is proposed to map the relationship between spatial coordinates, inlet velocity and overall sound pressure level. The root-mean-square-errors of prediction are below 0.82 dB in the test dataset, and the directivity of aerodynamic noise predicted by the DNN framework are basically consistent with the numerical simulation. This work paves a novel way for fast prediction of aerodynamic noise with high accuracy and has application potential in acoustic field prediction.     

Road traffic noise prediction based on speed-flow diagram

The method enables calculation of the annual average sound level of the road traffic noise, when the characteristics of the speed-flow diagram are available: the average speed of freely cruising vehicles, capacity of the traffic flow, traffic speed at the traffic flow capacity, and the slope of the decreasing traffic speed versus traffic flow. Under any conditions, traffic congestion reduces the annual average sound level. The final conclusion is that strong traffic congestion cannot be ignored in noise prediction.     

船尾伴流场-导管-螺旋桨互作用噪声预报研究

研究了导管螺旋桨低频离散谱噪声辐射机理和预报方法。依据线性声学原理,导管螺旋桨噪声场为螺旋桨直接辐射噪声与导管散射噪声之和,并利用速度势面元法分析流场,得到导管螺旋桨非定常力,将其作为FW-H方程的源项,求解得到螺旋桨直接辐射声。导管散射声通过Kirchoff积分方程求解获得。由于导管桨的导管是短导管,其算例分析计算表明,低频情况下导管散射声级远小于螺旋桨直接辐射声级。并将导管螺旋桨离散谱噪声级与测量所得的实桨离散谱噪声级进行了比较,证实导管螺旋桨离散谱噪声理论预报结果能够较合理的反映实桨离散谱噪声的量值。      

Modeling of the roundabout noise impact

A roundabout is a very popular tool used by town planners for carrying smooth and stationary road traffic flow. In this study it is shown that the replacement of a classical road intersection by a roundabout, under certain conditions, may produce a traffic noise decrease. These conditions are expressed in terms of the roundabout speed and the receiver location. The A-weighted sound exposure level is used to describe noise reduction.     

The annual average sound level of road traffic noise estimated from the speed-flow diagram

The speed-flow diagram correlates traffic speed and traffic flow on a road. The presented method enables estimation of the annual average sound level of the road traffic noise when the characteristics of the diagram are available: traffic flow capacity, free traffic speed, and traffic capacity speed (Fig. 1). Bunches of vehicles decrease the traffic speed and traffic noise.     

Effects of different noise combinations on sleep, as assessed by a general questionnaire

In the present study, the effects of different noise combinations on sleep were assessed in two contexts, with a single noise source and with combined noise sources. Road traffic noise, and construction or movie noise combined with road traffic noise were used as the single noise source and the combined noise sources, respectively. When the sound pressure level of road traffic noise was kept constant, levels of the construction and movie noise were changed. Twenty participants were followed for approximately 2 weeks, during which their sleep was evaluated using a questionnaire, including questions on sleeping behavior, premature awakening, and subjective responses. The results showed that the combined noise sources including construction noise decreased the number of participants who fell asleep within an hour and increased the number that were awakened prematurely compared to the effects of road traffic noise combined with movie noise. However, similar tendencies were observed while evaluating sleep quality, sleep disturbance, and annoyance.     

Prediction of propeller non-cavitation noise by superimposing shifted sound signal from an isolated blade

The time-cost of the propeller non-cavitation noise prediction can be greatly reduced by the isolated blade method,which is validated via hybrid URANS and acoustic analogy,followed by the acoustic characteristics of propeller in time domain are analyzed.Firstly,we predicted the sound of the E779 A propeller operating in uniform inflow and found a typical periodic characteristic of the sound pressure distribution on propeller blade as well as the sound signal of the receiver,and the result by the superimposing shifted sound signal from an isolated blade(isolated blade method) agreed well with the result by the integration on total blades,which validated the credibility of the isolated blade method in uniform inflow.Finally,we predicted the sound of a propeller running in the wake of submarine by the isolated blade method,and the result also agreed well with the result by the integration on total blades,which further indicated that the isolated blade method was also applicable for the non-cavitation noise prediction of the propeller running in non-uniform inflow.The noise prediction of the counter-rotating propeller,the pump-jet can also benefit from this method.     

Verbal communication and noise in eating establishments

A new simple prediction model has been derived for the average A-weighted noise level due to many people speaking in a room with assumed diffuse sound field. Due to the feed-back influence of noise on the speech level (the Lombard effect), the speech level increases in noisy environments, and the suggested prediction model gives a 6 dB reduction of the noise level by doubling the equivalent absorption area of the room. This is in contrast to the lowering by 3 dB by doubling of the absorption area for a constant power sound source. The prediction model is verified by experimental data found in the literature. In order to achieve acceptable conditions for speech communication within a small group of people, a guide for the recommended minimum absorption area per person in eating establishments is provided.     

A simple formula for evaluating the acoustic effect of balconies in protecting dwellings against road traffic noise

This study explores the noise reducing effect of a balcony and describes the development of a simple theory pertaining to the propagation of traffic noise from a road into a balcony. A new methodology is proposed that is based on the well-known prediction scheme—“Calculation of Road Traffic Noise” (CRTN)—developed in the UK. A geometrical ray theory is developed for the prediction of noise levels inside a balcony due to road traffic. The source level of road traffic noise is obtained as per the standard CRTN methodology. However, road sub-segmentations and new approaches for the prediction of noise levels at illuminated and shadow zones inside a balcony are proposed. Field measurements have been conducted on four different types of balcony to validate the proposed methodology. The insertion loss, defined as the difference in the noise levels with and without the presence of a balcony, has been used to assess the shielding effectiveness of a balcony against road traffic noise. The simple theory is validated by outdoor field measurements. It is also found that a properly designed balcony can provide considerable screening effects in protecting dwellings against road traffic noise.