Development of a traffic noise prediction model for an urban environment

Sharma, Asheesh; Bodhe, G. L.; Schimak, Gerald

doi:10.4103/1463-1741.127858

Cited by 23 publications

(14 citation statements)

References 6 publications

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“…But commonly accessible professional calculation programmes do not have such a function. Another problem seems to be the statistics base applied for validation of calculation programmes, which is also highlighted by the authors of [5,21]. Probably it does not fully cover the real randomness of traffic.…”

Section: Discussionmentioning

confidence: 99%

Verifying Traffic Noise Analysis Calculation Models

Zagubień¹,

Wolniewicz²

2015

Pol. J. Environ. Stud.

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The safe exploitation of technical structures such as a public road is connected not only with safety of traffic but also with minimization of its negative impact on the environment. The quality of the natural environment influences the comfort of life -especially the comfort of relaxation of inhabitants of both big cities and small villages [1]. Research connected with minimizing the bad impact of transport on the environment has been conducted all over the world for years. While doing acoustic analyses we should take into account the fact that there are many factors influencing the level of traffic noise. The most essential ones are: type, condition, and quality of road surface; the number of vehicles passing in a certain period of time, and their technical state; the intensity of traffic depending on the time of day; the number of road lanes; the distance from buildings protected acoustically; the changeability of traffic connected with traffic organization; and weather conditions. According to many World Health Organization (WHO) recommendations [2] and to a European Union directive [3], noise is treated as any other contamination of the environment. These documents include useful instructions on how to fight traffic noise, such as obligation of monitoring, preparing acoustic maps, or assessing impact on the environment. WHO recommends that the A-equivalent sound pressure level outside a building should not exceed 55 dB during the day and 45 dB at night. At such levels outside a building it is still possible to keep the right acoustic conditions inside, with windows set ajar or temporarily opened.The inspiration for this paper was publications [4, 5] indicating the differences in acoustic simulation results while using commercial calculation programmes. In the publication [5] we can find the acoustic analysis for one section of a road in the open area at changeable traffic intensity conducted in some calculation methods used in the world. In this case the results show the difference even up to 4 dB. It is obvious that when applying programmes built on different algorithms we should get results more or less similar. An engineer using a certain calculation programme expects that the simulation results will allow him Pol. J. Environ. Stud. Vol. 24, No. 6 (2015) AbstractThis paper concerns choosing the best software for road noise simulation. Three independent parts of roads were analysed using three calculation programmes based on three mathematical models. The results of conducted measurements and analyses proved how important the selection of the right calculation programme for each numerical traffic noise simulation is. One should also be careful when choosing a calculation programme for rare traffic situations, such as bus terminals or transport bases. Despite striving to create an ideal calculation model for traffic noise considering the large number of changeable acoustic and non-acoustic parameters, engineers should also be informed about the criteria of usefulness of a programme (model) for a certain r...

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Section: Discussionmentioning

confidence: 99%

Verifying Traffic Noise Analysis Calculation Models

Zagubień¹,

Wolniewicz²

2015

Pol. J. Environ. Stud.

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“…Traffic noise was recorded manually using sound level meter with accuracy of ±0.1% [19]. Vehicle volume, average speed and honking are measured for the total duration of 15-minute with one minute intervals during morning (10:00 to 11:00) and evening (18:00 to 19:00) peak traffic hours respectively at each selected location (Table 1) [5]. The vehicle volume has been classified into four categories, i.e.…”

Section: Data Acquistionmentioning

confidence: 99%

“…In the model 3 "gbellmf" membership functions were selected for each input. The numerical ranges were used for Q E (45.35-373.9), for S E (23.91-49.28), for honking (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14) respectively. Membership functions of inputs are displayed in Fig.…”

Section: Development Of Anfis Modelmentioning

confidence: 99%

“…The metropolitan cities in India, exhibit varying characteristics like interrupted traffic flow, heterogeneous traffic conditions, lack of lane discipline and frequent honking, making formulation of a theoretical noise prediction model difficult and complex. The empirical models like USA FHWA and UK CRTN are some of the popular noise prediction models used in India [4][5][6]. The other noise models are ASJ, RLS-90, and Mithra are also available for noise level prediction but limited uses of these models have been so far reported [7].…”

Section: Introductionmentioning

confidence: 99%

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Adoptive Neuro-Fuzzy Inference System for Traffic Noise Prediction

Sharma¹,

Vijay²,

Bodhe³

et al. 2014

IJCA

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An adaptive neuro-fuzzy inference system (ANFIS) is implemented to evaluate traffic noise under heterogeneous traffic conditions of Nagpur city, India. The major factors which affect the traffic noise are traffic flow, vehicle speed and honking. These factors are considered as input parameters to ANFIS model for traffic noise estimation. The proposed ANFIS model has implemented for traffic noise estimation at eight locations. The results have been compared and analyzed with observed noise levels and the coefficient of co-relation between observed and predicted noise level was found to be in range of 0.70 to 0.95. The model performance has also been compared with Federal Highway Administration (FHWA), Calculation of road traffic noise (CRTN) and regression noise models and it is observed that the model performs better than conventional statistical noise model. The proposed noise model is completely generalized and problem independent so it can be easily modified to prediction traffic noise under various traffic criteria and serve as first hand tool for traffic noise assessment. General Terms Back propagation algorithm KeywordsHeterogeneous traffic, adaptive neural network based fuzzy inference system, noise prediction, honking.

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“…There has been no standard traffic noise model followed in Indian conditions, although many studies have been reported in recent years in various parts of the country [10][11][12][13][14][15] . The Central Pollution Control Board (CPCB), Delhi has taken many initiatives and carried out numerous studies for monitoring the ambient noise levels at hot spots in Delhi and implementation of suitable measures to be adopted for noise mitigation.…”

Section: Introductionmentioning

confidence: 99%

Applications of AutoRegressive Integrated Moving Average (ARIMA) approach in time-series prediction of traffic noise pollution

Garg¹,

Soni²,

Saxena³

et al. 2015

noise cont engng j

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The paper analyzes the long-term noise monitoring data using the AutoRegressive Integrated Moving Average (ARIMA) modeling technique. Box-Jenkins ARIMA approach has been adapted to simulate the daily mean L Day (06-22 h) and L Night (22-06 h) in A-and C-weightings in conjunction with single-noise metrics, daynight average sound level (DNL) for a period of 6 months. The autocorrelation function (ACF) and partial autocorrelation function (PACF) have been obtained to find suitable orders of autoregressive (p) and moving average (q) parameters for ARIMA (p, d, q) models so developed for all the single-noise metrics. The ARIMA models, namely, ARIMA(0,0,14), ARIMA(0,1,1), ARIMA(7,0,0), ARIMA(1,0,0) and ARIMA(0,1,14), have been developed as the most suitable for simulating and forecasting the daily mean L Day dBA, L Night dBA, L Day dBC, L Night dBC, and day-night average sound level (DNL) respectively. The performance of the model so developed is ascertained using the statistical tests. The work reveals that the ARIMA approach is reliable for time-series modeling of traffic noise levels. © 2015 Institute of Noise Control Engineering.

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Development of a traffic noise prediction model for an urban environment

Cited by 23 publications

References 6 publications

Verifying Traffic Noise Analysis Calculation Models

Verifying Traffic Noise Analysis Calculation Models

Adoptive Neuro-Fuzzy Inference System for Traffic Noise Prediction

Applications of AutoRegressive Integrated Moving Average (ARIMA) approach in time-series prediction of traffic noise pollution

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