Road Noise Research Articles

Application of a novel visual representation method for urban noise source monitoring

Increasing urban mobility has led to a rise in the number of vehicles, resulting in urban noise pollution, which can cause health problems. Conventional methods for noise control can be adopted to reduce noise near the sources. However, this is not cost-effective to reduce overall noise level in the urban area. Therefore, it is preferable to encourage drivers to voluntarily reduce noise by applying methods such as cracking down on road noise sources. To this end, a real-time urban noise source monitoring system is needed, but processing large amounts of data can be difficult. In this study, a novel visual representation method, DoAgram, is applied for urban noise source monitoring. DoAgram can represent the time, frequency, and spatial information of the sound source within a 2D color graph. Therefore, the long-term spatial information of the sound source acquired from the sensor array node can be transmitted as a single image. Also, one can restore spatial information of the source from an image through a decoding process corresponding to the encoding method. One can find that the proposed method can be utilized as a database that can efficiently conduct urban noise source monitoring.

Impact of road noise components (rolling/propulsion) by vehicle categories (light-duty, heavy-duty and two-wheeled vehicles) on population's noise exposure and associated adverse health effects

Exposure to road traffic noise can lead to various adverse health effects, ranging from annoyance and night-time sleep disturbances to ischemic heart diseases. The aim of this paper is to identify the contribution of the road noise components (rolling/propulsion) for each vehicle category (light-duty, heavy-duty and two-wheeled vehicles) on population's noise exposure and the associated adverse health effects. This analysis focuses on the case study of Lyon, France. The input data align with the requirements of the 4th deadline of Directive 2002/49/CE and adhere to the CNOSSOS calculation standard. The results reveal that the rolling noise component of light-duty vehicles is predominant due to vehicle speed and traffic volume. It is the sole noise component (i) significantly exposing a portion of the population to Lden > 68 dBA, noise limit set by the French decree of April 4, 2006, and (ii) where over half of the population is exposed to Lden > 53 dBA, recommended threshold by the World Health Organization (2018). By using transfer functions established between noise components for each vehicle category, and population's noise exposure, noise source-abatement solutions can be quantified in terms of their impact on population's noise exposure, thus determining the reduction in health impacts.

Mid-frequency propagation path analysis on vehicle body using structural intensity

With the penetration of electric vehicles, reduction of structure-borne road noise gets important issue to realize comfortable vehicles for customers.Sound generation mechanism of structure-borne road noise on vehicle body is analyzed basically by modal analysis in low-frequency range. On the other hand, as frequency increases, modal analysis cannot clarify vibration mechanism because vibration wavelength gets shorter, mode density gets high and mode shape gets complicated. Therefore, it is difficult to identify efficient countermeasure parts and inefficient damping materials are added to response panels in mid-frequency range. So alternative analytical methods in mid-frequency range are under research to understand vibration mechanism. In this paper, Structural intensity (SI) analysis is introduced to clarify sound propagation mechanism. This analysis method makes it possible to clarify the main energy propagation path from input point to vibrating panels on vehicle body. The results show that SI analysis unveils the main energy propagation path and gives the opportunity to reduce the panel vibration efficiently by controlling the energy propagation path in body frame structure without damping materials.

Expanding road noise cancellation to higher frequencies

Active vehicle Road Noise Cancellation (RNC) systems effectively reduce persistent vehicle rumble and booming noise which is very difficult to control with traditional NVH measures, particularly at low frequencies. Therefore, more and more vehicles are adapting RNC systems in their production applications. Most current RNC systems cancel low frequency structure-born noise, which is typically below 400 Hz. Although it provides appreciable noise attenuation, remaining high frequency noise contents still requires mitigation for a premium cabin experience. This study targets a next generation RNC system development which extends noise cancellation frequency range up to 1 kHz. High frequency RNC system (HF-RNC) development begins with analyzing limiting factors of the current RNC systems. Theoretical and practical limitations of the current system are discussed. Then, new technology and enablers to overcome those limitations are introduced. New enablers investigated herein in includes feedforward microphones, low latency system, headrest speakers and microphones, and head tracking system. HF-RNC system architecture is defined to comprise extended features and enablers. Low latency software is ported into a development platform which is based on a commercial DSP. The new HF-RNC system demonstrates noise cancellation up to 1 kHz when applied to an electric vehicle.

Traffic road noise and transportation energy efficiency

Future noise emission limits for passenger car are going to lower levels by 2024 -Social cost of noise for France in 2021, shows clearly that the dominant source of noise pollution is road traffic (81 Bn€ for a total of 146 Bn€) This R51 regulation is meant to lower the noise pollution from road traffic, but when looking closer to the sources and their contributions, in particular the tire/road noise interaction, the environmental efficiency of this regulation is questionable. Tire/Road interaction involves tires characteristics, that are constrained by an array of specification and it has been proven that there is a physical limit to what could be expected from the tire as far as tire/road interaction noise is concerned.Road surface however, shows very wide acoustic characteristics spread : over 15dBA from the quietest to the loudest asphalt, to be compared to 2dBA steps in the regulation and an environmental impact that will depend on the rolling stock renewal rate. This paper will present a new patented method from Renault enabling to assess road acoustic data mapping from a simple drive of a Megane E-Tech, quickly and efficiently, issuing the first ever noise pollution control maps.

Enhanced railway sound event detection using YAMNet and classification criteria

The segmentation-by-classification method has become a popular way to detect sound events. It uses neural networks, trained to detect sound sources on short signals (one tenth of a second to a second), or long-term signals divided into a succession of short segments. In this paper, we focus on detecting train pass-bys from long term signals, where it is assumed that railway noise is higher than ambient noise. Using the neural network YAMNet, we show that a processing stage is necessary to improve the segmentation of such events and reduce the high false positive rate. The applied criteria are related to the nature of a train pass-by, lasting several seconds and with broad frequency band. Around 90% of events are detected with proper boundaries. However, we observe that YAMNet is not designed to distinguish railway vehicles from other vehicles. False positive rate remains high whenever other vehicles travel near the measurement location. Additional AI models are proposed to perform this specific distinction. Their efficiency depends on train type, as recent passenger train noise resembles roadway noise. Otherwise, false positive rate decreases below 10%, and the railway noise contribution estimate aligns with the reference level, within a 0.5 dB(A) margin on average.

Methodology for determining new CNOSSOS-EU vehicle sound power coefficients using type approval data

CNOSSOS-EU is a standardized framework for strategic noise mapping across the European Union which member states are required to conduct every five years. The framework provides an approach to assess noise from different sources, including road traffic noise. Road traffic noise is primarily caused by vehicles. In CNOSSOS-EU, the vehicle exterior noise is modeled using fixed sound power coefficients. These coefficients were derived from measurements conducted up to 2006. Although significant progress has been made in reducing vehicle exterior noise, the existing model does not account for this technical progress. This paper first reviews the evolution of exterior noise legislation and its impact on vehicle noise levels. It then presents a new procedure for integrating the technical progress into vehicle source modeling in CNOSSOS-EU by incorporating type approval data. For demonstration purposes, this procedure is applied to up-to-date type approval data of M1 vehicles, resulting in a more accurate representation of current noise emissions. This research contributes to the refinement of the CNOSSOS-EU model, aligning it with the current state of technology and providing a more precise basis for environmental noise assessment.

Noise monitoring of loud vehicles in four Dutch cities

Excessive noise from loud cars, motorcycles, mopeds, trikes and quads is commonplace in Dutch cities and is a major source of annoyance. In order to assess the sources, causes and potential solutions, TNO has performed a series of noise monitoring campaigns in four major cities including vehicle identification. Sound and vehicle data was acquired at 3 locations in each city for a period of five days. Several steps were applied to separate vehicle noise from other noises, and to attribute the noise to the correct vehicles. The resulting analysis provided insight into the types of vehicles and driving conditions causing most noise. A set of characteristic driving conditions were derived from the measurement data, including high acceleration, engine revving, engine backfire, high engine speed and others. Besides driving behaviour, vehicle modifications such as louder exhausts, removed dB-killers and mechanical or electronic engine modifications are common causes of high noise levels. Also an overview of suggested mitigation options and future outlook is given, based on the findings of the analysis.

Weight minimization of train floors with acoustic and structural constraints

For high-speed trains, high noise levels are generated in the bogie from wheel-rail contact and aero-acoustic sources. Manufacturers try to minimize the floor transmission to the interior, e.g. by applying damping layers or by other measures. For vehicle noise control measures, the goal is often to achieve the sound reduction needed at lowest possible mass. Here, an optimization scheme is applied to minimize the mass of an extruded floor panel. Constraints are defined by limiting the maximum local floor deflection from passenger loads according to EN 12663-1 and the A-weighted interior sound level. Recognizing the periodic nature of the floor structure, a Finite Element model of a single periodic cell is set up, using periodic boundary conditions and the floor sound reduction is determined using a Statistical Energy Analysis approach. An analytic beam model is applied to determine the floor deflection from the passenger load. Design solutions meeting the constraints with the smallest possible mass are sought. Starting with a floor structure with 23.3 kg/m2, a final design with 18.0 kg/m2 is reached. In addition, the transmitted sound through the floor is reduced by 8 dBA. Convergence from different starting points is verified.

Residential exposure to transportation noise and risk of incident atrial fibrillation: a pooled study of 11 prospective Nordic cohorts

Transportation noise has been linked with cardiometabolic outcomes, yet whether it is a risk factor for atrial fibrillation (AF) remains inconclusive. We aimed to assess whether transportation noise was associated with AF in a large, pooled Nordic cohort. We pooled data from 11 Nordic cohorts, totaling 161,115 participants. Based on address history from five years before baseline until end of follow-up, road, railway, and aircraft noise was estimated at a residential level. Incident AF was ascertained via linkage to nationwide patient registries. Cox proportional hazards models were utilized to estimate associations between running 5-year time-weighted mean transportation noise (Lden) and AF after adjusting for sociodemographics, lifestyle, and air pollution. We identified 18,939 incident AF cases over a median follow-up of 19.6 years. Road traffic noise was associated with AF, with a hazard ratio (HR) and 95% confidence interval (CI) of 1.02 (1.00-1.04) per 10-dB of 5-year mean time-weighted exposure, which changed to 1.03 (1.01-1.06) when implementing a 53-dB cut-off. In effect modification analyses, the association for road traffic noise and AF appeared strongest in women and overweight and obese participants. Compared to exposures ≤40dB, aircraft noise of 40.1-50 and>50dB were associated with HRs of 1.04 (0.93-1.16) and 1.12 (0.98-1.27), respectively. Railway noise was not associated with AF. We found a HR of 1.19 (1.02-1.40) among people exposed to noise from road (≥45dB), railway (>40dB), and aircraft (>40dB) combined. Road traffic noise, and possibly aircraft noise, may be associated with elevated risk of AF. NordForsk.

Sound Source Identification of Vehicle Noise Based on a Microphone Array With a Modified Multiple Signal Classification Algorithm

In this paper, a modified multiple signal classification (MUSIC) algorithm tailored for sound source identification (SSI) of vehicle noise is introduced and experimentally validated. A uniform planar microphone array (UPMA) is formulated for mathematical modeling, with its SSI-oriented parameters selected based on the primary frequency spectrum of vehicle noise. Simulations are conducted to compare the SSI accuracy of two conventional spatial spectra estimation (SSE) algorithms: the Capon algorithm and the MUSIC algorithm. The results demonstrate that the MUSIC algorithm, which relies on the eigenvalues of a covariance matrix to estimate signal direction, exhibits superior SSI resolution under low signal-to-noise ratio (SNR) conditions. However, it faces challenges in distinguishing between coherent or closely spaced signals. To address this, a modified MUSIC algorithm is proposed by reconstructing the covariance matrix of received signals and the SSE function. Simulation outcomes indicate that the modified MUSIC significantly outperforms the conventional version, owing to its enhanced SSI resolution. The accuracy of the SSI system, incorporating the UPMA and the modified MUSIC, is verified using a low-frequency volume source. Ultimately, the devised SSI system is successfully deployed to identify noise sources in a vehicle at different operational conditions, further validating the efficacy of the modified MUSIC. The UPMA and the modified MUSIC presented in this study have direct applicability in vehicle noise source identification and may be extended to other sound-related engineering fields for SSI purposes.

Optimized road noise asphalt pavements - The types of noise-reducing road surfaces

The noise has long been recognized as a stress factor for humans. A high level of noise has many negative influences on health such as: difficulty to concentrate, stress, health issues for the ears, and also has a damaging effect on the cardiovascular and endocrine systems. Road traffic is main source of total noise, especially in conurbations. In order to effectively reduce noise emissions, surface courses can be installed as road surfaces that generate particularly little noise in interaction with the vehicle tire. This paper delas with different types of noise-reducing road surfaces.

Applying Michelson-interferometer based optical fiber sensors for active control of road noise in a car

Active noise control (ANC) technology is a promising solution for reducing road noise in cars, where the quality of reference signals is critical for improving the performance. In this paper, an optical fiber sensor based on Michelson interferometer is proposed to obtain the vibration reference signal for active control of road noise in a car. First, the principle of this optical fiber sensor is introduced, then the feasibility of using optical fiber sensor for active control of sound radiation from a panel of an enclosure is verified, where the spectrum, coherence and impulse response of the optical fiber sensor are reported and compared with that of an accelerometer. Finally, the optical fiber sensor is applied in a car for active control of road noise and the simulation results based on the measured data are presented. The research demonstrates the potential of using the proposed optical fiber sensor for active control of road noise in a car by discussing its advantages and weakness.

The Relationship between PM2.5, Greenness, and Road Noise Exposures and Children’s Cognitive Performance in England: The Millennium Cohort Study

The Relationship between PM2.5, Greenness, and Road Noise Exposures and Children’s Cognitive Performance in England: The Millennium Cohort Study

Helmholtz resonator adjustment computational studies, where in order to reduce the vehicle noise level, the resonator waveguide and throat are inside the resonator volume

Helmholtz resonator adjustment computational studies, where in order to reduce the vehicle noise level, the resonator waveguide and throat are inside the resonator volume

Introducing the rubber plate impact method: a versatile alternative to close proximity test for tire-pavement noise characterization on asphalt highways

Despite its widespread use, the close proximity (CPX) test for tire-pavement noise is limited to existing pavements, posing challenges for timely and effective corrections of noisy pavements. Additionally, the CPX test entails significant procurement and operation costs. Addressing these issues, this study introduces the rubber plate impact (RPI) method as a simple experimental technique for assessing tire-pavement noise in both laboratory and field settings. The RPI test utilizes the folding motion of the treaded rubber plate on the road surface to simulate air pumping and tread impact noise as two main contributors to tire-pavement noise. Indoor RPI tests on asphalt concrete samples of dense graded asphalt and porous asphalt mixes revealed dominant effects of air void content and associated surface texture on the level of noise. The RPI testing procedure was verified at 17 highway test sections, where both CPX and RPI tests were conducted for comparison. Multiple linear regressions between RPI and CPX data were analyzed to propose CPX prediction models at different speeds (80, 100, 120 km/h), and the performance of these prediction models was validated with independent noise datasets collected from four different highway routes. The findings suggest promising prospects for the RPI test as a cost-effective and versatile alternative to CPX for tire-pavement noise characterization on asphalt-surfaced roads, with implications for road materials design and noise mitigation strategies.

Research on the design and noise reduction performance of periodic noise barriers based on nested structure

The rapid development of highway transportation has led to a gradual deterioration of the sound environment along highways. The theory of periodic structures provides a new approach for the design of traffic noise barriers. However, the lack of practical numerical models and on-site experimental validation has hindered the widespread application of periodic structure sound barriers. This study investigates the noise reduction performance of nested structure periodic sound barriers. Results from semi-anechoic chamber tests demonstrate excellent wave attenuation at the air gap of the barrier, achieving a peak noise reduction of 16 dB without the addition of any sound-absorbing materials or treatment measures, particularly at local peaks in the highway noise spectrum. To enhance the noise reduction performance of nested structure periodic sound barriers, three optimization measures were explored: filling sound-absorbing materials inside the scattering elements, adding micro-perforated panels, and their combined effects. The results indicate that both porous sound-absorbing materials and micro-perforated panels can improve noise reduction effectiveness, especially outside the air gap. In the optimization design of nested structure periodic sound barriers, adding porous sound-absorbing materials inside the micro-perforated panels effectively enhances noise reduction within the air gap. Considering the abundance of abandoned w-beam guardrails in highway projects, which also meet the requirements of scattering elements, this study proposes the use of abandoned w-beam guardrails as scattering elements. This approach aligns with principles of cost-effectiveness and environmental sustainability, and it provides design guidance to promote the application of nested structure periodic sound barriers in traffic noise control.

Experimental and Simulation Study on Flow-Induced Vibration of Underwater Vehicle

At high speeds, flow-induced vibration noise is the main component of underwater vehicle noise. The turbulent fluctuating pressure is the main excitation source of this noise. It can cause vibration of the underwater vehicle’s shell and eventually radiate noise outward. Therefore, by reducing the turbulent pressure fluctuation or controlling the vibration of the underwater vehicle’s shell, the radiation noise of the underwater vehicle can be effectively reduced. This study designs a cone–column–sphere composite structure. Firstly, the effect of fluid–structure coupling on pulsating pressure is studied. Next, a machine learning method is used to predict the turbulent pressure fluctuations and the fluid-induced vibration response of the structure at different speeds. The results were compared with experimental and numerical simulation results. The results show that the deformation of the structure will affect the flow field distribution and pulsating pressure of the cylindrical section. The machine learning method based on the BP (back propagation) neural network model can quickly predict the pulsating pressure and vibration response of the cone–cylinder–sphere composite structure under different Reynolds numbers. Compared with the experimental results, the error of the machine learning prediction results is less than 7%. The research method proposed in this paper provides a new solution for the rapid prediction and control of hydrodynamic vibration noise of underwater vehicles.

Interior sound quality evaluation and forecasting of passenger vehicles based on hybrid optimization neural networks

The interior noise of vehicles directly affects the comfort of the occupants, necessitating precise evaluation and control. Existing research has focused on constructing mappings between objective parameters and subjective perceptions of noise, where back propagation neural networks (BPNNs) are widely used due to their strong nonlinear mapping capabilities. However, the selection of initial weights and thresholds can affect the predictive accuracy of BPNN. This study developed a BPNN model optimized by an intelligent algorithm for predicting the level of subjective annoyance of passengers during the movement. Initially, objective parameters of interior noise were obtained through acoustic signal processing techniques, and five parameters were selected for studying subjective annoyance through correlation analysis and two-tailed tests. Meanwhile, the actual subjective ratings of passengers on interior noise were captured for subsequent training of the model and testing of the results. Finally, the established sparrow search algorithm (SSA) and genetic algorithm (GA) optimized BPNN were used to predict subjective evaluations. The predictive accuracy and efficiency of the model were significantly improved upon validation, providing a viable alternative to traditional passenger vehicle noise assessment experiments and valuable references for future noise control and optimization efforts. The experimental results are consistent with the view that the neural network model optimized with a mixture of intelligent algorithms is closer to the passenger’s subjective annoyance level having higher accuracy and efficiency.

Effects of acoustic environment on sleep and mental health in residential regions near railways

Noise is an important environmental risk factor for physical and mental health. Furthermore, long-term noise exposure is burdensome for the mind and body and has become a serious problem. Rail transportation is one of the main methods used to transport goods in China; however, the noise and vibrations generated by freight trains have serious impacts on residents in nearby regions. To further investigate the relationship between railway noise and road noise exposure and changes in sleep duration and mental health scores, a field study in Harbin, China, was conducted and a health risk prediction model was constructed. The results showed that for every 1 dB increase in the Equivalent continuous A-weighted sound pressure level (LAeq), the percentage of deep sleep among residents living near the railway decreased by 0.2 %. Although residents near the railway reported similar sleep evaluations compared to those living farther away, they exhibited poorer mental health. Cox risk modeling indicates that the risk of mental health problems is approximately three times higher for those living near the railway. These findings potentially provide benefits in developing strategies to reduce the risk of mental illness for people residing near railways.