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.     

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.     

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 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.     

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。     

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.     

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.