Top PDF Modelling Of Road Traffic Noise

Modelling Of Road Traffic Noise

Modelling Of Road Traffic Noise

Road traffic noise is the biggest corridor problem and the most intrusive and annoying form of noise pollution. The growth in vehicle population aggravated the issue. Heavy trucks, buses, auto rickshaws, two wheelers and other automobiles contribute to the vehicular noise in heterogeneous traffic conditions. In developed countries, the only concern is the homogenous traffic which can be tackled with ease. To develop a transport system which is sustainable and environmentally friendly, it is necessary to model different traffic parameters. This helps to identify various traffic problems in advance, so that preventive and mitigation measures can be adopted for improving the transportation system to make it sustainable. It is identified that vehicular noise has a supremacy over all other adverse impacts of traffic, as it affects both the physiological and psychological well-being of peoples exposed to it. Prolonged exposure to traffic noise causes acute and chronic impacts on people. Other than damaging the hearing capacity of people, it may lead to mental illness, behavioral problems and can damage heart, lungs and kidney which can be fatal.
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Performance Analysis of Road Traffic Noise by Using Computer Aided Modelling

Performance Analysis of Road Traffic Noise by Using Computer Aided Modelling

This model was developed for central part of Bangkok by Urban Transport Department in 1997 by Pamanikabud and Tharasawatpipat [17]. The research focuses toward formulating an empirical model of interrupted traffic flow in Bangkok using two analytical approaches. The first being the single model analysis, and the second, the separated model or dual model analysis. In this study, several parameters that are considered to have possible influences on the interrupted traffic flow noises were measured at the study sites. Then, these were tested for their correlation with traffic noise levels. The parameters consisted of vehicle volume which is classified into different vehicle types appearing on the Bangkok roadways, average spot speed of vehicles in the traffic stream, road width, distance from curb to building façade, and distance to the nearest intersections. In the analysis of data, these parameters also were separated into nearside and far side roadway parameters. In this study, the distances to building facade were applied by two parameters. The first was the distance from observer to nearside building facade, and second, the distance from observer to far side building facade. Geometric mean of the roadway cross-section was introduced as one of the parameters in the analysis of the stop-and-go traffic noise model. Tests were conducted to determine the correlation of various parameters with the traffic noise level in Leq as well as the co-linearity among these parameters if they existed. The sets of parameters which correlated highly with Leq were used further as input in multiple regression analysis. Stepwise analysis technique was adopted in the multiple regression analysis processes of the study. Individual noise characteristics at an overall mean vehicle speed was used to identify the proportional weighting scale of the noise levels generated per unit of each vehicle type in relation to an automobile unit. The overall spot speed range obtained from the field survey data of this research together with the value for overall mean vehicle speed (of 33 km/h) were superimposed onto the vehicle noise characteristics. As there are many vehicle types that are in use in Bangkok, these were classified into seven groups based on the similarities in their noise level characteristics within the speed range that was observed in this study. From this analysis, the proportional weighting scale of the noise levels generated by each vehicle class was calculated. With this information, the traffic of traffic used in the model then could be described in terms of the noise generating ratio of each vehicle type in comparison with automobiles in Bangkok’s urban traffic.
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Efficient Road Traffic Noise Model for Generating Noise Levels in Indian Scenario

Efficient Road Traffic Noise Model for Generating Noise Levels in Indian Scenario

(coefficient of determination). All the analysis is conducted on the data been obtained from Dehradun Roorkee highway (NH 58). It comprises of the hourly volume of vehicles, percentage of heavy vehicles and observed value of noise which is obtained by the mathematical modelling of road traffic noise prediction [1]. The observed value of noise is obtained using noise analyzer (B & K 2260 sound level meter. This sound meter measures the value of noise in dB A. dB A roughly corresponds to the inverse of the 40 dB (at 1 kHz) equal- loudness curve for the human ear Thus, we carried out the comparative analysis of the rtm and calixto model on the basis of 𝑅 2 (Coefficient of Determination) and efficiency. Hence found rtm to be much better as compared to the calixto model in accordance with the Indian road conditions.
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Environmental noise exposure, early biological risk and mental health in nine to ten year old children: a cross-sectional field study

Environmental noise exposure, early biological risk and mental health in nine to ten year old children: a cross-sectional field study

Noise was a continuous measure calculated for each school in dB (A) (A-weighted decibels, where A- weighted means that the sound pressure levels in var- ious frequency bands across the audible range have been weighted in accordance with differences in human hear- ing sensitivity at different frequencies) where aircraft noise was based on 16-hour outdoor LAeq contours (continuous equivalent sound level of aircraft noise within an area from 07:00 to 23:00 within a specified period) and road traffic noise was derived from mod- elled data for the Netherlands and from a combination of modelling the proximity to motorways, major roads, and minor roads; traffic flow data; and noise measure- ments taken at the façade of the school building for the United Kingdom and Spain [15]. In order to reduce the impact of other environmental noise sources, schools which were exposed to dominant sources of noise other than aircraft or road traffic noise (i.e. rail noise) were excluded from the study.
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The use of artificial intelligence to
predict road traffic noise

The use of artificial intelligence to predict road traffic noise

Strategy for modelling complex situations The development of a grid-based neural network noise prediction model for a site-specific 2dimensional situation is quite straightforward, only [r]

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Modelling the Trend of Road Traffic Accidents in Accra

Modelling the Trend of Road Traffic Accidents in Accra

In order to forecast the trend of road accidents in Accra, several models such as the AR, ARMA, ARIMA, Exponential and Polynomial methods were used. However, the sixth order polynomial analysis, ultimately proved to be the most appropriate fitting model that best predicts RTAs as can be seen evidently in the ensuing diagram; Figure 1. However, due to the existence of multicollinearity among the 2 , 4 , 5 under the sixth-order polynomial model as seen in Table 6, they were conveniently removed from the analysis. Essentially, the independent variable had a relatively high r-square value of 0.878 after removing the collinearity. The result further indicates, that this figure was statistically insignificant, which is ultimately, explained on the basis of the fact that the p-value was greater than 0.05. Nevertheless, using the cubic polynomial model, the r-square value obtained subsequently, was 0.957 which is significantly indicative of the fact that, the cubic model is 95.7% accurate in predicting RTAs in the Accra Metropolis. The cubic polynomial model as can be seen in Tables 7, 8 and 9 is thus statistically significant since the p-values of the coefficients were less than 0.05.
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Evaluation of Road Traffic Noise Pollution in Quetta (Pakistan)

Evaluation of Road Traffic Noise Pollution in Quetta (Pakistan)

Noise pollution is the consequence of urbanization and industrialization and is considered as major problem of urban areas. The most important factors raising noise pollution in urban areas include vehicular traffic, neighborhood electrical appliances, TV and music systems, public address systems, and railway and air traffic. Increase in noise level, across the world, has motivated the researchers of the world to this study the problem and its impact on the environment. Also, researchers have reported that the road traffic noise is the leading source of noise in urban areas [1]. Noise pollution is not only the growing problem of developing countries but also of the developed countries. According to researchers, over 130 million people in Europe suffer from exposure to noise levels above 65 dB(A) [2].
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Street-level noise in an urban setting: assessment and contribution to personal exposure

Street-level noise in an urban setting: assessment and contribution to personal exposure

Perhaps the most important contributor to the urban soundscape is street-level sound. Given the ubiquitous exposure to street-level sounds among urban dwellers, and the potential for annoyance and health effects from this noise, strict urban noise control measures for street- level sources of noise are increasingly being imple- mented. However, even in cities such as New York City (NYC), which has rigorous regulations on, and enforce- ment of, nuisance noise sources (e.g., loud radios and car alarms) and construction–related noise, there is little focus on street-level noise (e.g., noise from roadway traf- fic, commercial activities, etc.,), though some studies have evaluated noise from mass transit in NYC [19,20]. The extent and magnitude of levels of street noise and air pollutants in NYC has recently been assessed [21], but the methodology used did not measure personal ex- posures, but rather levels at 10 ft above street level, well above the elevation of the heads of pedestrians. Variation in street-level noise with regards to vehicle traffic and road proximity has been explored, [18] but most studies on traffic noise have relied on modeling of noise levels from a network of roads, land use regression, or extrapo- lation of models based on a small number of noise sam- ples [22-24]. While these are cost-effective approaches to estimate traffic noise levels, they can neglect factors in the urban built and natural environment that may mitigate or exacerbate exposure to street noise levels, in- cluding temporal changes in noise, built environment factors, and vulnerable areas and individuals. These other factors have particular relevance for understanding exposure to street-level noise in the urban environment.
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Evaluation and Analysis of Road Traffic Noise on a National Highway

Evaluation and Analysis of Road Traffic Noise on a National Highway

Noise is a common environmental pollutant in nearly all urban communities. Noise can be emitted from various sources such as aircraft, road traffic, railways, construction, factories, etc. Among these, road traffic is a major source of noise in the urban areas contributing to 55% of the total noise. Social surveys conducted in various cities throughout the world indicated that traffic noise is the major source of nuisance and annoyance [1,2].Social survey conducted in various cities throughout the world indicated that traffic noise is the main source of nuisance and annoyance [3,4].The effect of noise depends on various factors such as time duration, noise sound level, distance from the source, etc. The health impact associate with the noise pollution on human well-being is done by various researchers [5, 6, 7, 8]. The CBCB of India in its notification for noise has laid down the ambient noise standards [9]. FHWA model was found to be suitable for the prediction of noise in India within a fair degree of accuracy.
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Soundscapes of Urban Parks in and around Bhubaneswar and Puri, Odisha, India: A Comparative Study

Soundscapes of Urban Parks in and around Bhubaneswar and Puri, Odisha, India: A Comparative Study

ABSTRACT: Anthropogenic noise is debatably one of the most common threats to national parks' resources. Park visitors and workers generally suffer from adverse effects of noise from on- and off-road vehicles. The parks, studied here, are located in strictly urban areas, surrounded by streets with intense vehicle traffic. This study assesses the soundscape of urban parks in two cities of Odisha State, on the basis of acoustic field measurements and interviews. Noise descriptors in and around three different parks in Bhubaneswar and Puri cities have been measured and analyzed. A field experiment has been conducted with 330 participants in three parks, representing urban natural environment. The questionnaire comprised identification of the interviewee, characteristics of the user's profile in terms of his/her use of the park, and aspects of individual‘s perception of the soundscape and environmental quality of the park. Positive correlation has been established among the noise levels of these three parks. The present study reveals that the acoustic sound levels of all the investigated parks are more than 50 dB (A) [permissible limit, established by Central Pollution Control Board (CPCB) for green parks]. Considering the urban elements and acoustical characteristics, it can be concluded that all the parks are affected by several factors such as urban planning, land use, main traffic routes, type of public transportation, and its internal sounds.
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A Bayesian approach to modelling manual traffic counts

A Bayesian approach to modelling manual traffic counts

In this research, we have looked into modelling of the frequency of traffic count passing through a particular road (Tombia - Amassoma Road) to the Niger Delta University, Bayelsa State of Nigeria using a Bayesian approach. The manual method of vehicle counting was used to record the time intervals between vehicles in seconds. We have used a Bayesian approach to modelling the occurrence of timed events following a Poisson process. We have evaluated the posterior probability density function and distribution function using R codes. We have also discussed the probability of observing number (say ) vehicles in the next few (say ) seconds.
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A case study of integrated modelling of traffic, vehicular emissions, and air pollutant concentrations for Huron Church Road, Windsor

A case study of integrated modelling of traffic, vehicular emissions, and air pollutant concentrations for Huron Church Road, Windsor

The VISSIM traffic simulation model (PTV AG 2008) was used to capture instantaneous speed and acceleration of vehicles and to estimate average speed of vehicles at each link. In VISSIM, different vehicle lengths and acceleration/deceleration are used for cars and trucks separately. The model simulates individual vehicle movements based on cars following techniques and pre-specified vehicle operational characteristics. Entry volumes at each intersection were set to car and truck counts upstream of that road section. The volumes in each approach were estimated based on the proportions of left- turn, through and right-turn movements from Huron Church Road as observed in the intersection vehicle counts (Section3.2). Desired speed distribution of vehicles was set to the range of 55-65 km/h with an 85th percentile of 60 km/h which is the speed limit on the road. Since most trucks on the road were truck trailers with the approximate length of 22 m, their length was entered as 22 m. For cars, six default lengths–one for each of six classes – and their default proportions of total car traffic as in VISSIM (PTV AG 2008) were used.
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Assessment of the masking effects of birdsong on the road traffic noise environment

Assessment of the masking effects of birdsong on the road traffic noise environment

This study aims to explore how the soundscape quality of traffic noise environments can be improved by the masking effects of birdsong in terms of four soundscape characteristics, i.e., Perceived Loudness, Naturalness, Annoyance and Pleasantness. Four factors that may influence the masking effects of birdsong (i.e., distance of the receiver from a sound source, loudness of masker, occurrence frequencies of masker, and visibility of sound sources) were examined by listening tests. The results show that the masking effects are more significant in the road traffic noise environments with lower sound levels (e.g. <52.5 dBA), or of distance from traffic (e.g. >19 m). Adding birdsong can indeed increase the Naturalness and Pleasantness of the traffic noise environment at different distances of the receiver from a road. Naturalness, Annoyance and Pleasantness, but not Perceived Loudness, can be altered by increasing the birdsong loudness (i.e., from 37.5 to 52.5 dBA in this study). The Pleasantness of traffic noise environments increases significantly from 2.7 to 6.7, when the occurrence of birdsong over a period of 30 s is increased from 2 to 6 times. The visibility of the sound source also influences the masking effects, but its effect is not as significant as the effects of the three other factors.
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Transport and the Environment: Research and Policy Issues

Transport and the Environment: Research and Policy Issues

Recent years has seen a growing awareness of and concern about the environmental impacts of transport systems. The transport sector is a major cause of environmental impact arising from the construction of infrastructure, the movement of traffic and the manufacture and disposal of vehicles. Various reports have attempted to list the nature of the impacts (eg Nash et al, 1991, TEST, 1991). In the case of new infrastructure the principal direct effects are landtake, the destruction of property, severance, and visual intrusion. Concern about the loss and damage to nature conservation sites from new road building has been reported and catalogued in various documents (Harwood and Hillborne, 1992, Bowers, Hopkinson and Palmer, 1992). The movement of traffic gives rise to numerous environmental externalities including noise, air pollution, visual intrusion and severance. A recent report by has attempted to define separately such impacts and ways by which they can be appraised (Institute for Transport Studies, 1992). These impacts vary in their nature. Air pollution for example includes both local air quality affected by emissions of CO, particulates and lead etc, and transnational problems such as acid rain and global warming. These latter effects are generally unaffected by the time and place in which the emissions occur. In other words it is the total pollution loads that are important whereas in the case of impact such as noise and severance, these vary by time and place. Such a distinction is important when we come to consider mechanisms for reducing the environmental effects of transport.
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A model for Traffic noise prediction in heterogeneous traffic conditions

A model for Traffic noise prediction in heterogeneous traffic conditions

This paper describes a traffic noise prediction methodology for heterogeneous traffic conditions. Traffic noise characteristics in cities of developing country like India are slightly varied by virtue of the fact that the composition of the traffic is heterogeneous associated with variance of road geometry and varying density of the buildings on the either side of the road. Traffic noise prediction models developed on the basis of homogeneous traffic are not apt to predict the actual noise levels for Indian countries. A new model has been developed in this study considering different parameters such as traffic volume, composition of traffic, speed, horn using effect, number of lanes, road width, road gradient and local metrological conditions.
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The comparison of road traffic noise levels from prediction models and actual noise measurement

The comparison of road traffic noise levels from prediction models and actual noise measurement

4.3.5 The comparison of the accuracy provided by each prediction model over the study sites With regard to the above results, when considermg the absolute noise levels, T-Noise and STAMS[r]

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Road traffic noise and registry based use of sleep medication

Road traffic noise and registry based use of sleep medication

examine health effects from environmental exposures. In- formation bias was prevented by using modeled noise ex- posure levels and registry data on sleep medications obtained independently both of each other, and other questionnaire data. The noise exposure was thoroughly assessed, using a detailed noise model. Furthermore, the study population included participants from both urban and suburban areas of the City of Oslo. This resulted in a broad range of noise exposure levels, which strengthened the possibility to detect associa- tions. A common approach in studies on noise and health is to use a cut off level for the noise exposure in order to account for background noise. In the present study, we found it appropriate to use the full range of exposure levels in the analyses. Firstly, be- cause the only noise source included in the noise model is road traffic noise. Thus, by increasing the lowest modeled noise levels to a level of background noise, misclassification of exposure would likely occur, the mean exposure level in the study population would increase, and the association between road traffic noise and sleep medication use could poten- tially be overestimated. Furthermore, a cut off would also mean that some of the variance in the exposure is lost and the accuracy of the analytic model will consequently be reduced.
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Quantification of the exposure and effects of road traffic noise in a dense Asian city: a comparison with western cities

Quantification of the exposure and effects of road traffic noise in a dense Asian city: a comparison with western cities

There is a global shift in the centre of gravity of urbanization from the developed to the developing world. In the latter, about half of the population already live in cities and this proportion will be two-thirds by 2050 [27]. By 2025, more than half of the twenty-five megacities in the world will be in Asia, and located in the tropics or sub-tropics [28]. Hong Kong (Figure 1) has one of the world’s highest population densities with most of the population living in high-rise buildings, in- cluding what Yuen and Yeh [29] call super-tall buildings of 50 storeys or more, surrounded by high intensities of road traffic (251 vehicles/road kilometre [30]). Most of the dwellings in Hong Kong are apartments in these high-rise building, typically with two to three bedrooms and mostly in line of sight with nearby or distant road- ways. While the city form of any individual city will depend on topography, planning controls and land eco- nomics, the growing number of large and mega-cities of
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Road traffic noise and children’s inattention

Road traffic noise and children’s inattention

mothers categorized as unexposed to rail traffic noise had residential address outside a radius of 700 m from a rail- way line and 300 m for trams and metros, since outside these radii, the rail traffic noise is either nonexistent or is so low that it is masked by other noise sources. The noise exposure assessment was based on input data for the years 2011 and 2006 and included data on topography, building polygons, traffic counts (but estimations for smaller roads without counts), estimated values for 24 h traffic distribu- tion (75% day, 15% evening and 10% night for highways, and 65%, 20% and 15% for municipal roads), signed speed, information on noise barriers, and ground surface (hard or soft). The search radius of 1000 m was used for high- ways, and 500 m for municipal roads. Residential exposure to rail traffic noise was modeled separately and in a similar way as road traffic noise. For rail traffic, rail time tables were used to obtain information on traffic volume and diurnal distribution of traffic.
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Analysis Of Road Traffic Noise At Express Highway

Analysis Of Road Traffic Noise At Express Highway

Abstract- Noise, defined as unwanted or excessive sound, is an undesirable by-product of our modern way of life. In world health organization (WHO) statements, “large city noise is considered to be the third most hazardous pollution”. Amravati is the seventh most populous metropolitan city in Maharashtra state. Apart from Amravati district itself- Akola, Yavatmal, Buldhana, Washim, these district also comes under Amravati Division. The city is located on the national highway NH-6 leading to Mumbai in the west and Kolkata in the east. Several trips were required to identify enough sites that would meet the criteria and be appropriate for the measurement methods. In front of PRMIT&R, crossing at kondeswar square, crossing at MIDC, these three spots are taken into account for study. Noise barriers and building insulation are probable implications. In the study, the minimum and maximum noises level observed were 52.9 dB and maximum 104.5 dB. Basing on the findings in the study, it can be inferred that there is an urgent need to set up noise standards in the country to minimize the noise pollution.
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