Geographical information system for traffic noise analysis and forecasting with the appearance of barriers (original) (raw)
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Areawide Road Traffic Noise Contour Maps
In this study a framework for developing an object-oriented tool -DRONE (areawide Dynamic ROad traffic NoisE simulator) to generate areawide noise contour maps for a road network is demonstrated. This provides faster access to information for abatement of noise policies. The approach for integrating the dynamic output from traffic simulator to noise model, which predicts traffic noise based on geographical data set for the study area, are described. Noise level at different points of study area is calculated based on integration of noise prediction model, ASJ Model-1998, with traffic simulation model, SOUND. The integration with traffic simulation model provides a dynamic access to traffic-flow characteristics and hence automated and detailed prediction of road traffic noise. Data from the integration of traffic and noise simulation models are used to generating areawide noise contours using GIS. The application of DRONE on a real world situation is also presented.
2001
Traffic noise is a significant effect of transport in urban areas and a priority issue in the EU. Noise as an environmental factor needs to be considered more closely when transport policies and relevant measures are examined and planned to tackle transport related problems. There is a need for including impacts on noise both at the macro and the micro level. Existing official traffic noise calculation methods mainly focus on the former. By using suitable methods for both these levels in combination with transport simulation models and GIS technology, it is possible to approach the whole issue in a more comprehensive way. Visualisation of problem areas and spots with extreme effects as well as other traffic parameters is easily achieved with the use of GIS functionality.
Study of the Noise Generated by a Major Road in a City
ANNALS OF THE ORADEA UNIVERSITY. Fascicle of Management and Technological Engineering., 2015
The paper presents a case study regarding a the noise generated by the road traffic on a main road that traverse a city. Since no input data was available, all data was produced using free available sources for the base map and data collected on site for the traffic volumes and speed. The methods and tools used to process the outputs are also presented. Beside the noise mapping software, are used GPS, GIS and CAD techniques.
Review on the Road Traffic Noise Assessment
Journal of Engineering Studies and Research, 2016
This paper presents a synthesis of current state of the assessment of road traffic noise in urban areas considering economic, social and legal aspects. Therefore, there were described several prediction methods of the urban traffic noise. These methods are useful in calculating the exposure of the population at noise levels which exceed the permissible limits. Mapping is one of the most common methods used for the assessment of noise. Whether it is industrial, airport, rail or road traffic noise, noise mapping provides accurate data needed later in developing action plans against noise. The road traffic noise assessments are performed periodically, and a representative picture of the noise in the analysed areas is obtained. Then, the action plans can be developed in order to reduce road traffic noise, where it is necessary.
An Evolution of Road Traffic Noise Modeling Technologies in Hong Kong
Proceedings of ICSV 15, 2008
Different kinds of road traffic noise models are employed commonly in identifying the road traffic noise problems of an area, planning of new road or new development, or predicting changes in the acoustical environment due to proposed new strategic policies, plans or programs. Generally, there are four main steps, they are data preparation such as data collection and input, selection of calculation methodology, validation, and lastly presentation of results. Decades before, the basic input data could only be obtained from field survey and traffic count, the road traffic noise predictions could only be conducted by hand calculation or using a programmable calculator. Complicated road alignments or development plans, and project of a larger scale could not be easily handled. At a later stage, with the help of faster computers, spread sheets, computer programs can be used to handle digitized data, and the results can be presented in tables and figures. Nowadays, the input data can be accurately transferred from digitized data sets or through the data stored in the Geographic Information System (GIS), complicated three-dimensional (3D) data can now be taken into consideration, 3D and graphical results can be generated in map or model formats. The output of the noise model is self-explanatory and easily understood by readers. However, from the modeling technology point of view, together with the accuracy, it is still facing a demand in producing more humanized system and results. Hence, more enhancement in the system interfaces, stronger power in calculating complicated structures, and more elements in aural or virtual reality in the coming models are expected. This paper describes the Hong Kong’s experience in the development of road traffic noise modeling techniques. It also proposes a benchmark and common ground for the current technologies in road traffic noise models.
Journal of Environmental Health Science and Engineering, 2018
Background Road traffic noise influencing directly public health in the modern cities is a growing problem in both developing and developed countries. The objective of this study was to model traffic-induced noise in Antalya province, validate the model with noise emission data, and to run the model for the noise preventive scenarios. Methods In this study, modeling of traffic-induced noise was performed using SoundPLAN® software at Gazi Boulevard in the city of Antalya. Calculations were made according to NMPB-Routes 96, which have been accepted by environmental noise legislation of the European Union and Turkey. Fundamental data sets such as geographical, topographical and meteorological data, building information and population, traffic network, traffic volume and vehicle speed, and composition of types of vehicle were utilized for the development of noise prediction model. Eight preventive scenarios to reduce traffic-induced noise levels were simulated using the validated model considering traffic flow measures such as types of vehicles, vehicle speeds, types of road surface, redirecting portion of heavy vehicles to alternative routes and noise barrier usage. Results Results showed that increase in heavy vehicle speeds in smooth road surface conditions caused more increase in exposures than that of light vehicle speed. It was highlighted that it would be appropriate to use porous road surface to reduce exposures on population on high-speed roads. Furthermore, the number of people that are exposed to noise is significantly reduced by precautions such as alternative routes for heavy vehicles and speed restriction. These precautions reduced noise exposures by 25.5-63.8%. The results showed that the usage of noise barrier at the alternative routes in case of porous asphalt road reduced population, dwellings, and area exposed to traffic noise which is greater than 75 dB(A) as 63.8, 40.5, and 60.0%, respectively. Conclusion It could be concluded that the outcomes of the noise prediction models based on the generated scenarios could be used for the purpose of decision support system and could be helpful for decision-makers on the noise legislations.
Comparative assessment of road traffic noise through 2D noise mapping: A case study of an urban area
Frontiers in sustainability, 2022
Noise pollution has risen to a worrying degree in all zones of the city (residential, commercial, industrial, and silent) because of the rapid rise in urbanization, industrialization, and other connectivity of transport systems in all zones of the city. It has always been challenging to identify noise hotspots where immediate remedial measures are required. In addition to providing the propagation of noise in the X and Z direction, D noise mapping is an essential method for identifying regions where noise levels may reach a hazardous level. Thus, the comparative examination of all residential areas inside a city provides a clear picture of noise exposure. The main focus of this study is to carry out comparative analysis of noise exposure level of selected locations such as residential commercial, industrial and silent area of Delhi city using D noise maps. For noise monitoring, Sound Level Meter (SLM) Larson & Davis in compliance with the standard procedure of CPCB is used for monitoring peak tra c hours of working day and night. After that, using sound PLAN (acoustic) and MapInfo Pro,-D (Desktop GIS) noise maps were developed, visualized, and analyzed. According to the findings, residential areas have been recorded as having the highest levels of noise, followed by commercial and silent zones, and industrial areas have been seen to have the lowest levels of noise. The high noise level in residential area may be due to heavy tra c volume and road surrounded by high rise buildings. Whereas minimum noise has been observed in industrial area because the industries along the major roads have been surrounded with parapets and trees. As a result, only a small amount of industrial noise entered major highways/roads. Additionally, D noise map revealed that the surrounding structures of road (high rise buildings, brick walls, grilled boundaries, trees etc.) significantly influence propagation of noise in all directions. These maps may also be utilized by decision makers in the process of formulating noise control strategies or implementing corrective measures. KEYWORDS noise monitoring, sound level meter (SLM), urban area, road tra c noise, D noise mapping Frontiers in Sustainability frontiersin.org Alam et al.. /frsus. .
Prediction and evaluation of noise pollution caused by a roads network
2005
According to OECD, at least 20% of the European Union population (around 80 million of people) is exposed in daytime to traffic noise, whose level exceeds the limit of 65 dB(A); traffic represents today the main source of noise in most European countries. Italian laws are very concerned about noise pollution; in particular law no. 447/95 states the obligation for companies which run public transport services and for those which run road infrastructures to carry out noise evaluation and reduction plans. In this context, the local government of the zone of Perugia and the Department of Industrial Engineering of the University of Perugia have started a cooperation aimed at predicting, evaluating and reducing traffic noise in the whole network of the zone (about 2,800 km). The large extension of the network made necessary the use of a simulation code; a commercial code was used to study the acoustical climate produced by the road infrastructures in a conventional “impact corridor”. The ...
Evaluation of the Traffic Noise Prediction Procedure
1973
Report No. 375 {''Vehicle Noise Survey in Kentucky;" September 1973) presented analyses of more !ban 10 ,000 isolated vehicle noise measurements. The report now submitted presents corrections which have been found to improve the accuracy of the "Procedure for Predicting Traffic Noise Levels" {Design Memorandum No. 1-72). These corrections are based on analyses of 270 noise-level measurements at 39 sites. It is recommended that the noise prediction procedure now in use be revised to include these corrections. They are applicable directly to "clear11 or "free field" situations and, so may be carried through the prediction procedure. This is our third report concerning noise; the first was a brief, literature review (No. 322, February 1972). We have reviewed draft reports of NCHRP Project 3-7, Phase III-one volume of which is expected to be published as Report 144 and which will contain corrections for the effects of barriers and obstructions in that portion of the prediction procedure. The reports reviewed allude to the possible need for corrections to the 11free field" model but provide none. The reports also mentioned in a reminding way that the original model (NCHRP Report No. 117) was developed from and is applicable to so-called "freeways;" and, so, we infer from this that our corrections should be most significant where traffic volumes are low and when the distance from the roadway is short. The noise generated by and(or) reflected by different types of pavements or surfaces offers some possibilities for further corrections and also the possibility of qualifying projects to meet noise-level restrictions by selection of pavement surface types. The adjustments allowed in the existing procedure appear to be too general and too subj' ective. In order to further define the effects of pavement surfaces in this way, measurements have been made at several road sites (50-ft distance, same automobile, scheduled speeds) surfaced with sand-asphalts, with open-graded plant-mix seal, with Class I, and with portland cement concrete. It appears that porous bituminous surfaces produce less noise. The analyses and derived factors will follow in a subsequent report.
Urban road traffic noise on human exposure assessment using geospatial technology
Environmental Engineering Research
The sounds produced by humans, industries, transport and animals in the atmosphere that pose a threat to the health of humans or animals can be characterized as noise pollution. Adverse effects due to noise exposure can involve speech communication interference and declining learning skills of children. Highway traffic noise contributes to 80% of all noise. It has grown to a massive scale because of growth in population along the roads leading to a rapid change in land use and has evolved into a common reality in various Indian cities. The main objective of this work is to develop a road traffic noise prediction model using ArcGIS 10.3 for the busy corridors of Chennai. The collected data includes traffic volume, speed, and noise level in lateral and vertical directions. Noise levels were measured in 9 locations using a noise level meter. It is observed that the noise levels vary from 50 dB to 96 dB. It is found that the noise problem is severe in 18% of the area, and 6.3% of people are exposed to the traffic noise problem. The results obtained in this study show that the city is affected by severe noise pollution due to road traffic.