Towards a model for electric vehicle noise emission in the European prediction method CNOSSOS-EU

The CNOSSOS-EU method is recommended in Europe for environmental noise prediction. In regards to road traffic, it includes vehicle noise emission models implicitly referring to internal combustion vehicles. The development of electrically driven vehicles calls for the future consideration of these vehicles in prediction models. On the basis of experimental data, the study reported in this paper proposes a noise emission model for extending CNOSSOS-EU to light electric vehicles. Correction terms to be applied to the propulsion noise component are determined. Investigations on a sample of tyres with good rolling resistance performance, which is a main tyre selection criterion on these vehicles, indicated that no correction is required for the rolling noise component. Differences between the noise emission from conventional vehicles and electric vehicles are discussed for several road surfaces. Owing to the limited vehicle sample as well as transitional statements, this new model for electric vehicles running at constant speed over 20 km/h should be considered as a first step towards the definition of this vehicle technology in CNOSSOS-EU.     

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

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 (L_den) 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.     

Blind separation to improve classification of traffic noise

In order to apply noise mapping to traffic noise prediction, a knowledge of several information about traffic characteristics is required to predict the noise levels emitted by the roads involved. In the European case, the CNOSSOS-EU calculation method for traffic-noise level prediction is now under discussion, to be agreed in response to the European Directive relating to the Assessment and Management of Environmental Noise (2002/49/EC). In this application context, standard ISO 1996-2:2007 Determination of Environmental Noise Levels, in its Section 6.2, specifically mentions that during L_eq measurements of road traffic noise the number of vehicle pass-bys shall be counted during the measurement time interval. This information is often not available in many roads, so it is typically registered by means of casual counts, often through manual procedures. Besides, if the measurement result is converted to other traffic conditions, a categorization of the vehicles involved is also required. Some additional information, such as the traffic density and the average speed, should be registered if a calculation method is used to build a noise map.In this paper a new automatic classification system of traffic noise covering these requirements is presented. The portable system processes a two channel audio recording to provide information of the average speed and the number of vehicles, which are classified in six categories during the measurement period. After several evaluations of the possibilities to get a good classification of the noise emission of a road from audio recordings, it is shown that increasing the within-class separation, as well as introducing a novel BSS–PCA-based classifier, the precision achieved in the final results is substantially improved.     

Influence of the automotive Start/Stop system on noise emission: Experimental study

One of the most common environmental impacts of road transportation is the traffic noise. Linked to this, Start/Stop is a technology which has demonstrated to save fuel by powering off the engine when the vehicle is stopped, such as in front of a traffic light, and restarting the engine instantly when the driver pushes back the pedal brake to proceed. The technology helps also to reduce the CO₂ emission, playing a key role in a way to accomplish stringent emission norms for vehicle manufacturer. However, we are not sure whether it reduces the noise emission and how much? Thus, the main aim of this work is to assess the engine noise emissions of a vehicle incorporating a Start/Stop system in urban traffic, and compare it with those radiated by the mean traffic stream. Experimental results demonstrate that there are no contributions of the Start/Stop system to reduce meaningfully the engine noise in urban traffic.     

Estimating traffic noise for inclined roads with freely flowing traffic

Traffic noise measurements on the kerbs of 19 independent inclined trunk roads with freely flowing traffic within the residential areas of Hong Kong are carried out in the present investigation. The performance of the existing noise prediction models in predicting traffic noise from inclined roads is evaluated. By regression analysis and simple physical consideration of the traffic noise production mechanisms, formulae for the prediction of the L_A10, L_A50, L_A90 and L_Aeq are developed or re-calibrated. Results suggest tyre noise has the major contribution to the overall noise environment when the source is an inclined trunk road. Also, the road gradient is found to have a higher contribution to the traffic noise than assumed in the existing models, but becomes unimportant when the background noise level L_A90 is concerned.     

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.     

Predicted effects of a speed bump on light vehicle noise

A speed bump reduces traffic noise levels during the deceleration phase and increases them during the acceleration phase. The net effect of a speed bump on noise from a light vehicle is assessed by means of the concept of noise energy density, S. This is a function of the instantaneous distance between the vehicle and the bump, S(x). To determine the function S(x) explicitly, five measurements of the sound exposure level, for each vehicle, are needed. It is assumed that the noise from each vehicle is generated by a single non-directional point source and propagates without vertical-surface reflections. An example prediction is presented based on measurements of sound exposure levels due to passenger cars.     

The impact of urban noise on primary schools. Perceptive evaluation and objective assessment

This paper aims to assess the impact of environmental noise in the vicinity of primary schools and to analyze its influence in the workplace and in student performance through perceptions and objective evaluation. The subjective evaluation consisted of the application of questionnaires to students and teachers, and the objective assessment consisted of measuring in situ noise levels. The survey covered nine classes located in three primary schools. Statistical Package for Social Sciences was used for data processing and to draw conclusions. Additionally, the relationship of the difference between environmental and background noise levels of each classroom and students with difficulties in hearing the teacher’s voice was examined. Noise levels in front of the school, the schoolyard, and the most noise-exposed classrooms (occupied and unoccupied) were measured. Indoor noise levels were much higher than World Health Organization (WHO) recommended values: L_Aeq,30min averaged 70.5 dB(A) in occupied classrooms, and 38.6 dB(A) in unoccupied ones. Measurements of indoor and outdoor noise suggest that noise from the outside (road, schoolyard) affects the background noise level in classrooms but in varying degrees. It was concluded that the façades most exposed to road traffic noise are subjected to values higher than 55.0 dB(A), and noise levels inside the classrooms are mainly due to the schoolyard, students, and the road traffic. The difference between background (L_A95,30min) and the equivalent noise levels (L_Aeq,30min) in occupied classrooms was 19.2 dB(A), which shows that students’ activities are a significant source of classroom noise.     

Assessing methodologies for calculating road traffic noise levels in Ireland - Converting CRTN indicators to the EU indicators (Lden, Lnight)

To comply with the EU Noise Directive 2002/49/EC, Member States are required to produce strategic noise maps for designated areas, including mapping road traffic noise from major roads. These maps must be presented using the EU indicators L_den and L_night. However, the most common noise indicator used in Ireland at present is the L_A10,18h indicator arising from the use of the Calculation of Road Traffic Noise (CRTN) prediction method. Therefore, a relationship needs to be established between L_A10,18h and L_den and L_night, separately. In addition to noise mapping these indicators are used for noise abatement purposes, so the proposed relationship must be accurate and robust. In 2002, the UK’s Transport Research Laboratory (TRL) published a paper describing mathematical procedures that could be used to convert values of L_A10 to L_den and L_night. These procedures were then adopted for use in Ireland. This paper examines the suitability of the TRL conversion methods 1 and 3 for use under Irish road conditions. Method 2 was not considered in this study, as it was a methodology not applicable in an Irish scenario. Studies concluded that where hourly traffic data are available, the conversion methodology outlined in TRL Method 1 is robust and reproducible. However, in the absence of hourly traffic data where daily traffic counts are used, the relevant conversion procedures produce variable results for both L_den and L_night when applied to Irish road conditions. To reduce the variability, new conversion procedures were developed, specifically for Irish road conditions.