Road Noise Research Articles

Studying the factors that affect urban road noise-case study: El-Tahrir Street, Al-Dokki Cairo, Egypt.

Expanding road networks to accommodate various activities has significantly increased urban noise pollution, adversely affecting human health and quality of life. Numerous factors influence the noise level in urban areas, including road characteristics, traffic characteristics, surrounding buildings, and weather conditions. While previous studies have considered many of these factors individually, this study aims to integrate all relevant variables to comprehensively monitor and analyze their combined effects on noise levels. The objective is to determine the most influential factors that could be incorporated into effective noise reduction strategies. This research focuses on Cairo, one of the most densely populated cities in the world, where high noise levels are a persistent issue. A detailed case study of Tahrir Street in Dokki, Cairo, provides the basis for this investigation. One of the most crowded areas is El-Tahrir Street in Al-Dokki, which was selected as a case study in this research. This area experiences high traffic volume, with up to 1700 vehicles passing through within a 15-min interval. This significant traffic volume is the primary driver of the elevated noise levels in the area. Traffic and noise level monitoring was conducted using a field survey using the sound level meter.Consequently, a statistical analysis was conducted to investigate the correlation between different factors and the noise level and determine the most influential factors. The study revealed that traffic volume and congestion are the most significant factors influencing noise levels on Tahrir Street, exhibiting strong positive correlations (R = 0.38). Additionally, the study found an inverse relationship between vehicle speed and noise level due to high traffic volumes and identified that building characteristics and wind direction also play roles, albeit to a lesser extent.

A modified feedforward-feedback time-frequency domain hybrid algorithm for multichannel active road noise control system

Multi-channel Active Road Noise Control (ARNC) systems require numerous adaptive filters to reduce noise at multiple positions inside the vehicle. However, road noise conditions are more complex, and the correlation between the reference signals and the target noise is often poor. When the driving conditions become slightly complex, the correlation between the reference signal and the target noise is often poor. As a result, traditional road noise control algorithms applied to multi-channel ARNC systems often suffer from high computational cost and poor noise reduction performance in non-steady-state and high-speed driving conditions. To address this issue,a reference signal selection method based on the genetic algorithm (GA) is proposed to obtain a reference signals combination with stronger coherence. On this basis, we introduce the time–frequency domain structure into the forward-feedback hybrid algorithm and a multi-channel time–frequency domain hybrid algorithm with a novel variable step size method (VSS-TF-HANC) is proposed in this paper.The feedforward part adopts a time–frequency domain structure and introduces a novel variable step-size strategy to reduce computational complexity and improve noise reduction performance. The feedback part adopts a control system based on the Internal Model Control (IMC) architecture to attenuate the interior noise components that have a strong response but poor correlation with the reference signals. Simulation results demonstrate that the proposed algorithm achieves excellent control results under complex driving conditions, with significantly better noise reduction performance compared to the control algorithms that solely employ the feedforward structure, while also exhibiting low computational cost.

An online decoupling-whitening frequency domain filtered-error least mean square algorithm for active road noise control.

Active road noise control (ARNC) systems have been widely investigated for low-frequency road noise attenuation in vehicle cabins. Multiple reference and error sensors are usually required to ensure noticeable noise reduction. However, this tends to slow down the convergence speed of adaptive algorithms due to the coupling of secondary paths and the cross correlation of reference signals. Furthermore, the high computational burden of normally utilized multichannel control algorithms exacerbates the difficulty of practical implementations. In this paper, an online decoupling-whitening frequency domain filtered-error least mean square (ODW-FDFeLMS) algorithm is proposed to address the aforementioned problems. Secondary path decoupling through inner-outer product decomposition and online reference whitening through spectral factorization effectively accelerate the convergence rate. Additionally, the utilization of the filtered-error algorithm based on frequency domain processing mitigates the computational complexity. Simulations with measured road noise data confirm the superiority of the ODW-FDFeLMS algorithm over existing algorithms in terms of convergence speed and computational complexity. Real-time experiments in a vehicle cabin further confirm the effectiveness of the proposed algorithm.

Research on fault diagnosis algorithm of automobile noise based on deep learning

Abstract This study is devoted to improving the accuracy and efficiency of automobile noise fault diagnosis by using deep learning technology. By constructing a convolutional neural network (CNN) model, we deeply analyze and learn the automobile noise data, aiming at realizing the automatic identification of different types of faults. The experimental results show that the deep learning model shows significant improvement in several performance indicators compared with the traditional methods. Different parameter configurations are used to train the deep learning model. The model structure includes a convolution layer, a maximum pooling layer, and a full connection layer. By extracting the hierarchical features of the data, the model can better adapt to the complex characteristics of noise data. Compared with traditional methods, the CNN model has achieved obvious advantages in accuracy, recall, and F1 score. The research results not only verify the effectiveness of deep learning in automobile noise fault diagnosis but also show the robustness and adaptability of the model under more complex parameter configurations. The conclusion of this study provides empirical support for the application of deep learning technology in the field of automobile noise fault diagnosis.

Research on Plunging Resistant Force of Drive-Shaft Systems Based on Fractal Methods

When drive-shaft systems move axially, the friction between rollers and raceways inside tripod joints will generate a plunging resistant force (PRF). Excessive PRF causes significant noise in vehicles. To reveal the generation mechanism, an analytical model of the PRF is proposed by analyzing the axial motion mechanism of a drive-shaft system. Based on the analytical model and fractal methods, a fractal model of the PRF is proposed to reveal influencing factors of the PRF more comprehensively. An effective method for correcting distribution of asperities between rollers and raceways is proposed to describe the contact state accurately. The fractal model shows that influencing factors of the PRF include the fractal dimension and the characteristic scale coefficient related to the roughness of contact surfaces, the material parameters, the working angle, and the correction coefficient for distribution of asperities. A measurement method for the PRF is subsequently proposed, and the fractal dimension and the characteristic scale coefficient are measured and determined. The effectiveness of the fractal model is verified based on the measurement of the PRF and the determined fractal dimension and characteristic scale coefficient. Numerical analysis results show that reducing Poisson’s ratio, the yield strength, and the working angle, increasing the characteristic scale coefficient, the elastic modulus, and the correction coefficient, and selecting a fractal dimension lower than 1.544 or higher than 1.782 can effectively reduce the PRF, thereby reducing the noise caused by the PRF.

Noise barriers as a mitigation measure for highway traffic noise: Empirical evidence from three study cases

Noise barriers are path interventions between noise sources and human receivers used mainly along road corridors to improve the acoustic environment for affected residents. Despite their widespread use, the impact of these interventions on community perception is still insufficiently investigated. This paper presents findings from a longitudinal study evaluating the efficacy of noise barriers in three residential areas alongside highways, compared to a reference case in a relatively quiet area. Noise exposure was objectively quantified via acoustic measurements and noise modelling, while socio-acoustic surveys measured the residents' subjective response in terms of noise annoyance as well as other aspects of quality of life. While noise exposure (Lday) decreased on average by 4.4–11.7 dBA at near-barrier points, direct reductions in pre to post-intervention noise annoyance were observed only in one case. Additionally, only in this particular case were the appraisals of the acoustic environment restored to a condition similar to low-level noise emissions (reference case). Contextual factors potentially downgrading the interventions' effectiveness are discussed, such as the history of complaints and coping, mistrust towards road authorities, high expectations, and the impacts of the COVID-19 pandemic. While noise exposure reductions did not directly lead to noise annoyance decreases, an ordinal regression pooling all cases revealed that larger reductions in noise exposure were associated with a higher likelihood of residents reporting decreased traffic noise annoyance in the post-survey. No evidence was found regarding noise barriers’ impact on the subjective assessment of other aspects of quality of life, such as health complaints, concentration disturbance, and sleep quality.

Tire Texture Monitoring (VGG 19 VS Efficient Net b7)

Tires are crucial components of vehicles, continuously in contact with the road. Monitoring tire conditions is vital for safety and performance, as degradation in tire treads and sidewalls can affect traction, fuel efficiency, longevity, and road noise. This research leverages both VGG19 and Efficient Net B7 algorithms to enhance tire image rendering, addressing limitations of traditional techniques. Using a binary classification algorithm, we classify tire images as healthy or cracked. By fine-tuning VGG19 and EfficientNet B7 on a specialized tire dataset, we achieve high-quality, photorealistic renderings. Our results demonstrate remarkable improvements in texture quality and visual realism compared to traditional methods. The rendered images exhibit finer details and more accurate representations of the tire’s tread patterns and material properties. This research contributes to the field of computer graphics by presenting a novel application of deep learning techniques to a specific industrial need, paving the way for future advancements in high-quality rendering of complex tire textures. Key Words: VGG19,Photorealistic rendering, deep learning.

A spatial approach to uncovering the socio-environmental injustice of noise pollution

ABSTRACT There is growing evidence of socio-environmental injustice related to road noise pollution among marginalised populations. Patterns of inequality are not constant across space, and understanding the local and regional scales of environmental injustice is critical for appropriate policy and public health interventions. This study used road noise exposure data from the 2018 National Transportation Noise Map, socio-demographic variables from the U.S. Census Bureau, and advanced spatial modeling to examine the socio-environmental injustice of road noise pollution in Texas census tracts (N = 5265). Local spatial regressions predicted higher coefficients of noise pollution in census tracts with higher minority populations, higher poverty, and higher social deprivation. Strong associations were specifically identified in the rural southern and eastern border areas of Texas. Findings from this study may guide policymakers and public health agencies in identifying geographic locations where interventions can be pinpointed to target socio-spatial noise pollution disparities in Texas.

Enforcement against vehicle noise from aftermarket parts in the United States: a qualitative analysis of stakeholders' opinions

Background: Disruptive environmental sounds, estimated to impact at least 145 million Americans, correlate with higher risk for hypertension, adverse cardiovascular events, and hearing loss. Traffic noise, especially from vehicles with aftermarket parts, is a major contributor to environmental noise. Restrictions on vehicle noise vary by state and are rarely enforced. Aim: To understand perspectives of individuals, government officials, and special interests regarding vehicle noise enforcement. Methods: Two focus groups and 27 one-on-one interviews were conducted, primarily on Zoom. The stakeholders interviewed were: 13 concerned residents, two auto shop owners, 8 auto enthusiasts, two police departments, one city councilor, and one special interest group. Call guides were developed based on background research and 238 written comments. Interviews were transcribed, summarized, and analyzed using qualitative coding. Results: While residents and enthusiasts agree drivers must be considerate, only enthusiasts prefer neighbor dialogue to government enforcement. Government officials favor in-person police enforcement but are considering acoustic cameras and other targeted approaches. All agree that vehicle noise should not be prioritized over more serious public safety matters. Conclusion: An effective compromise is possible. Governments should continue in-person noise enforcement while altering the built environment and beginning publicity campaigns to discourage noisy and reckless driving.

Traffic Projections in Noise Analysis

Traffic volumes are an integral part of any highway traffic noise analysis. The defining procedure for federal regulation for highway transportation noise analysis is 23 CFR 772. Section 23 CFR 772.9 Traffic Noise Prediction (d) states "In predicting noise levels and assessing noise impacts, traffic characteristics that would yield the worst traffic noise impact for the design year shall be used." States have the discretion to determine how to interpret what traffic metrics they use in their federally approved noise analyses. For example, some states use Level of Service (LOS) traffic volumes, while others use peak hour or design hour volume. Some states use an in-depth method to determine loudest hour which incorporates traffic volumes for a twenty four hour period and their corresponding operating speed per hour. This paper will discuss the time and effort associated with each procedure. Case studies will be presented to determine which of these traffic metrics best matches the current traffic counts.

Electromechanical Modeling and Analysis of Motor Whine in Electric Propulsion Systems

Electric motor whine is a major source of tonal noise and vibration for electric propulsion systems. The lack of engine masking noise in electric vehicles (EVs) further pronounces the pure tones, which are annoying to the occupants. The associated vibration can cause durability issues due to resonance and lead to failure of the sub-assembly or the supporting structure. A source-level NVH assessment of electric propulsion systems in modern EVs aims to achieve first-time quality designs at the advanced design stage. Accurate modeling of the motor stator and the electromagnetic (EM) forces at the air gap between the stator and rotor are critical to the NVH assessment. This work investigates the interaction between EM force application and the stator design in all four quadrants in which the electric motor operates, including both driving and regenerative braking in both clockwise and counterclockwise directions. A finite element (FE) model of the stator is developed, and the EM forces are mapped on the stator teeth, including different clocking and application directions. The stator model is integrated in the electric drive unit (DU) model, which enables the evaluation of the NVH performance of the propulsion system due to the reversal the EM force directions. The predicted analysis results are compared with the measured mount vibration

Numerical modeling of axial fan noise in fuel-cell electric truck: from component to full-vehicle analysis

This paper presents a comprehensive approach to predict the noise generated by an axial fan in electric vehicles, which is more noticeable due to the absence of combustion engines. Such cooling fans require stronger cooling performance due to increased demand for heat dissipation, resulting in even higher noise levels. A numerical model for fan noise in a fuel-cell electric vehicle (FCEV) is proposed with two steps: component-level and full-vehicle level models. At component level, Lattice Boltzmann Method (LBM) is employed for aeroacoustics simulation, determining the component sound power level (SWL). At full vehicle level, statistical energy analysis (SEA) is utilized to estimate the sound pressure level (SPL) inside the vehicle cabin, particularly at the driver's ear locations. The noise sources in the full vehicle model, were characterized from the component level SWL, as monopole sources. The model is validated by comparing the numerical predictions with the experimental measurements of the SWL in a hemi-anechoic chamber and the SPL in the cabin. Good agreements were obtained between numerical results and measurement results, demonstrating the effectiveness of the model. This allows for faster and more accurate virtual NVH design validations on large and complex systems where prototype measurements are difficult to conduct.

Rail noise comparison of FTA methodology with CadnaA modeling at Bronx development site

For environmental studies, identifying road and rail noise is important in projecting potential impacts from increased operations and also in identifying the window/wall attenuation needed to reduce outdoor to indoor noise levels. The Federal Transit Administration's (FTA) transit noise and vibration impact assessment procedures provide spreadsheets for estimating noise levels at specific receptor points. However, computer models such as SoundPLAN and CadnaA are also available. Because these computer models have complex input requirements and account for local terrain, they are rarely used for large corridor studies, but they are well suited to smaller sites. This study compares the FTA procedures with CadnaA modeling at a development site in the Bronx, NY, that is bounded by multiple rail lines and is proximate to a major highway. Discussion of results presents the advantages and disadvantages of each approach and identifies the conditions under which each approach is most appropriate.

Assessing wayside traffic noise levels using long-term noise monitoring and available Department of Transportation traffic data - worst-case noise traffic conditions, effects of repaving, and methods for comparing traffic noise data sets

The availability of automated continuous traffic data from State Departments of Transportation (DOTs), such as the California Department of Transportation (Caltrans) Performance Measurement System (PeMS), provides increased opportunities for the analysis of highway noise. Traffic data that is free to access and downloadable via the internet can be matched with unattended long-term noise measurement data to provide large datasets for analysis, without requiring field staff to be continuously on site. A 2018 ICF/Caltrans paper documented how this approach was used on an operating freeway to determine the volume of traffic that results in the highest one-hour energy average traffic noise level. At the time of those measurements, the subject freeway had Portland cement concrete pavement. The freeway has since been repaved with an open-graded rubberized asphalt concrete pavement overlay. The current study uses new (2023) traffic noise measurements and PeMS data to investigate how worst-noise conditions have changed and the effect of the repaving on wayside noise levels. Changes in traffic patterns since 2018 required the authors to normalize the measurement data for comparison. Opportunities were identified to take advantage of the large measurement data sets to match before-and-after data, and those methods are also described in this paper.

Applying reflections in the FHWA Traffic Noise Model

The latest version of the Federal Highway Administration (FHWA) Traffic Noise Model (TNM), version 3.2, was released early 2024 and is encouraged for use to conduct highway noise impact and noise abatement analyses. TNM 3.2 has the advantage of allowing single order reflections in addition to a parallel barrier/wall analysis. This paper presents a process to apply single order reflections considering both their sound contribution and their efficiency in modeling and calculations. The process was applied to a real highway project to evaluate sound level increases due to single reflections and to compare predicted sound levels with and without reflections to measured sound levels. Reflecting surfaces evaluated include noise walls on one side of a highway, building facades facing the highway, and sides of buildings.

Tracing the Evolution of Sound in Six Generations of the Chevrolet Corvette

This study presents the noise characteristics of six generations of the Chevrolet Corvette, an iconic American sports car that has undergone technological leaps and design innovations over the years, redefining performance and handling. The research conducted as part of an undergraduate noise control project is meant to study the auditory experiences of drivers, occupants, and pedestrians, considering the implications of improved cabin noise reduction in newer models while examining the changes surrounding powertrain and exhaust expectations in various generations of the model. Focusing on the evolution of sound pressure levels, the study analyzes both interior and exterior sound pressure level data to extract insights into the acoustic metamorphosis of the Corvette. This exploration enhances the understanding of the difference between sound and the Corvette driving experience. The research's significance extends to the community of Corvette enthusiasts, contributing to the historical narrative of this sports car and impacting automotive culture. Anticipation regarding the exterior experience for pedestrians aligns with sentiments about the powertrain and exhaust personality. The study concludes that stock Corvettes, even older models, do not significantly endanger hearing, mainly due to road noise. The interior sound at various speeds and accelerations can reach close to 90dBA periodically. Also, aftermarket exhaust systems hint at the possibility of extremely high sound pressure levels.

Detailed CNEL/Ldn roadway noise calculations vs. estimation methods

Roadway traffic noise calculations of the community noise equivalent level (CNEL) and the day-night average sound level (Ldn) metrics are sometimes required for various federal, state, and local government permitting requirements. To complete calculations of these metrics, detailed hourly traffic data is needed and obtaining this data can sometimes be a challenge. This paper explores various estimation strategies that are sometimes employed for situations where this data is not available and compares the estimates against calculations using detailed hourly traffic data to assess their validity. Project examples are provided of various permitting efforts such as those in support of California Environmental Quality Act (CEQA) documents, roadway projects at airports for Federal Aviation Administration (FAA) National Environmental Policy Act (NEPA) documents, and other local permitting requirements.

An annoyance model for urban air mobility vehicle noise in the presence of a masker

Proposed Urban Air Mobility (UAM) operations offer an alternative to road and rail traffic for local and regional movement of people and goods. To allow for large-scale adoption of UAM vertical takeoff and landing (VTOL) aircraft, it is critical to predict human annoyance response to the noise generated by these vehicles. This paper proposes a model that predicts an individual's perceived level of annoyance when presented with UAM VTOL aircraft noise in addition to a masking noise. The model predicts changes in annoyance based on subjective evaluation of detection, noticeability, and annoyance of noise in the presence of a masker. The application of this annoyance model will influence future work for a psychoacoustic annoyance model that incorporates UAM sound quality.

Evaluation of steering wheel vibration in electric vehicles and application to Dynamic Motor Drive

Eliminating steering wheel vibration is a key in refining vehicle Noise, Vibration, and Harshness (NVH). The absence of masking engine noise and vibration makes this challenge harder in a pure electric vehicle. This paper provides three key insights with regards to the evaluation of steering wheel vibration in EVs using on-road test data. First, we explore the effects of steering wheel grip variation in the measured accelerometer response. Second, we identify the limitations of evaluating powertrain induced vibration in a noisy environment and propose a post-processing technique to reject irregular road-induced perturbations from the measured data. Third, we evaluate a tuned mass absorber as a localized mitigation tool for steering wheel vibration. Finally, we describe how the above techniques were brought together to deliver a refined NVH experience while integrating Dynamic Motor Drive (DMD) technology as a software-only solution which has been shown to improve EV range by 0.4% on IPM and 3% on EESM based vehicles.

Delayed feedback active suspension control for chaos in quarter car model with jumping nonlinearity

Vehicles that operate on off-road terrain, such as rough and unpaved roads, sometimes suffer from severe vibrations. This vibration causes vehicle tires to lose contact with the supporting ground and subsequently collide with it. This jumping leads to nonlinear impact dynamics similar to those of a bouncing ball. In this study, a quarter car model with jumping nonlinearity is newly developed to represent the impact dynamics of an off-road vehicle under harsh terrain conditions. Subsequently, complicated vibrations, such as quasi-periodic and chaotic vibrations in the developed model, are identified using a bifurcation diagram and Lyapunov exponents. Additionally, delayed feedback (DF) control of the active suspension is designed for the developed model. The numerical results show that DF control is effective at eliminating chaos and reducing vibration levels. The noise robustness of the DF control is validated for sinus excitation and small random road noise. The results indicate that DF is effective even when road noise exists. Thus, this study demonstrates the feasibility of DF active suspension control for future suspension control design of the off-road vehicle.