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| Year : 2014 | Volume
: 16
| Issue : 68 | Page : 63--67 |
Development of a traffic noise prediction model for an urban environment
Asheesh Sharma1, GL Bodhe2, G Schimak3,
1 Environmental Systems Design Modeling Division, CSIR-National Environmental Engineering Research Institute, Nagpur, Maharashtra, India
2 Analytical Instrumentation Division, CSIR-National Environmental Engineering Research Institute, Nagpur, Maharashtra, India
3 Department Information Management, Austrian Institute of Technology, GmbH, Donau-City-Strasse 1, A1220 Vienna, Austria
Correspondence Address:
Asheesh Sharma
Environmental Systems Design Modeling Division, CSIR-National Environmental Engineering Research Institute, Nehru Marg, Nagpur - 440 020, Maharashtra
India
Abstract
The objective of this study is to develop a traffic noise model under diverse traffic conditions in metropolitan cities. The model has been developed to calculate equivalent traffic noise based on four input variables i.e. equivalent traffic flow (Q e ), equivalent vehicle speed (S e ) and distance (d) and honking (h). The traffic data is collected and statistically analyzed in three different cases for 15-min during morning and evening rush hours. Case I represents congested traffic where equivalent vehicle speed is <30 km/h while case II represents free-flowing traffic where equivalent vehicle speed is >30 km/h and case III represents calm traffic where no honking is recorded. The noise model showed better results than earlier developed noise model for Indian traffic conditions. A comparative assessment between present and earlier developed noise model has also been presented in the study. The model is validated with measured noise levels and the correlation coefficients between measured and predicted noise levels were found to be 0.75, 0.83 and 0.86 for case I, II and III respectively. The noise model performs reasonably well under different traffic conditions and could be implemented for traffic noise prediction at other region as well.
How to cite this article:
Sharma A, Bodhe G L, Schimak G. Development of a traffic noise prediction model for an urban environment.Noise Health 2014;16:63-67
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How to cite this URL:
Sharma A, Bodhe G L, Schimak G. Development of a traffic noise prediction model for an urban environment. Noise Health [serial online] 2014 [cited 2023 Dec 4 ];16:63-67
Available from: https://www.noiseandhealth.org/text.asp?2014/16/68/63/127858 |
Full Text
Introduction
Traffic noise has become an urban problem and it is a common reality in metropolitan cities world-wide. [1] It significantly affects human health, especially for people living in the vicinity to highways. [2] Research shows that over 40% of the population is bothered by noise exposure levels. [3] Traffic noise assessment is an important aspect of environment, although its management is a challenging task for environmental managers and urban planners. In the past few decades, Federal Highway Administration (FHWA), Calculation of Road Traffic Noise (CRTN), Richtlinien für den Lärmschutz an Straben ( RLS-90) noise models have been developed for traffic noise assessment and prediction. [4] These statistical models estimate equivalent sound level as a function of traffic variables under homogeneous traffic conditions. [5],[6] Recently, these models have been adapted to predict sound level under diverse traffic conditions, too. For example, FHWA model has adjusted to predict noise levels in interrupted traffic flow while CRTN model has improved to calculate equivalent noise level. [7],[8] Still the improved FHWA and CRTN model have some constraints. For example, honking has not been considered for traffic noise level prediction, which is a major characteristic of diverse traffic. [9],[10] Limited studies have been carried out reporting honking as one-of the important traffic variable for traffic noise assessment. [11],[12] In the present study, the impact of honking on traffic noise level are analyzed and presented. Three different cases of sound level prediction are generated based on a statistical analysis of traffic variables and equivalent sound level. The traffic is comprised of equivalent traffic flow (Q e ), equivalent vehicle speed (S e ) and distance (d) and honking (h). The mathematical formula between traffic variables and sound level are going to be discussed and presented in this study for traffic noise assessment and prediction.
Methods
The study area lies between 21°4′0″ to 21°12′0″ N and 79°2′0″ to 79°10′0″ E in Nagpur city, Maharashtra, India. Eight sampling locations have been selected for field data collection and sound level prediction, i.e. After airport, Before airport, Rx-station, NEERI gate no 1, Burdi, LIC chowk, Indora and Ring road [Figure 1]. The traffic data is collected for 15-min with 1 min intervals during morning (10:00-11:00) and evening (18:00-19:00) rush hours. Field data is comprises of traffic volume, type, speed, honking, distance and traffic noise. Four categories of vehicles are considered in the study, i.e. light vehicles (two wheelers), medium vehicle (four wheelers), auto (three wheeler) and heavy vehicles (vehicles more than four wheels).{Figure 1}
Equivalent traffic flow (Q e )
A composite relationship is calculated based on a factor of acoustic equivalence between the different vehicle classes as proposed by Rajakumara and Gowda. The total equivalent traffic flow Q e is calculated as follows:
[INLINE:1]
where, Q HV , Q auto , Q LV and Q MV are the total volume of heavy vehicle, auto, light and medium vehicles per minute respectively, while E HV , E auto and E LV are the acoustic equivalence of heavy vehicles, auto and light vehicles respectively. The values of E HV , E auto and E LV are taken as 9.63, 5.60 and 1.48 respectively.
Equivalent vehicle speed (S e )
Similarly, the equivalent vehicle speed S e is calculated as follows:
[INLINE:2]
where S HV , S Au , S LV , and S MV are average speed of heavy auto, light and medium vehicles respectively. [13]
Honking
The frequent usage of honks is subtly ingrained in the driving culture of India. Some of the recent study shows that honking has a significant impact on traffic noise level. [9],[10] The total numbers of horns blown in 1 min duration by different category of vehicles are recorded for traffic noise prediction in our study.
Distance
The noise level decreases exponentially when increasing the distance of the receiver from the noise source. [14] In this study we present that road traffic flow is considered as a noise source while sound pressure level meter is considered as a noise receiver. The distance of the road centerline and the sound pressure level is measured at each sampling point for traffic noise prediction.
The equivalent traffic flow (Q e ) and equivalent vehicle speed (S e ) is calculated with measured traffic volume and the average speed. The calculated values of Q e , S e , distance (d) and honking (h) at the eight sampling locations presented in [Table 1], [Table 2] and [Table 3]. Field data is statistically analyzed in three different cases for noise model development [Figure 2]. The locations where the equivalent speed is <30 km/h, represented by case I while the locations where it is above 30 km/h, represented by case II. Case III is derived from case I and II where traffic is calm and no honking is recorded. Three different mathematical expressions are derived for case I, II and III respectively given below:{Table 1}{Table 2}{Table 3}{Figure 2}
Case-I: If S e < 30 km/h and honking >0 then equivalent sound level is estimated as:
[INLINE:3]
Case-II: If S e > 30 km/h and honking > 0 then equivalent sound level is estimated as:
[INLINE:4]
Case-III: If S e >0 and honking = 0 then equivalent sound level is estimated as:
[INLINE:5]
in which L eq is equivalent sound level in dB (A), Q e is the equivalent traffic flow, S e is the equivalent vehicle speed (km/h), d is the distance (road centreline and sound pressure level meter) in meters, h is the total honking/min.
Results
To demonstrate the application, the model performance is compared and calibrated by previous traffic noise regression model [13] and measured sound levels in the study area. The model performance is tested by paired t-test results for case I, II and III at 5% level significance with measured noise level are shown in [Table 4]. The model results gives t-statistical values less than the t-critical values, which infers the predicted values of the model significantly fit with the field data from all the cases. The XY scatter plots drawn between measured and predicted values of noise level suggests the model performs statistically well for case II and III while it needs improvement on to case I [Figure 3], [Figure 4] and [Figure 5]. It is important to notice that case I represents congested traffic condition with average equivalent traffic flow (2491.31), average honking (57) and equivalent vehicle speed (27.14 km/h). Frequent gear's change, driving pattern, horn intensity and duration produces a huge variation in sound level L eq in congested and stagnant traffic flow and thus it is difficult to correlated traffic sound level with traffic variables in congested traffic. [15] These features are not consider in the present study and needed to be incorporate in further studies to improve model performance in congested traffic conditions.{Table 4}{Figure 3}{Figure 4}{Figure 5}
Discussion
Today, road traffic noise is a major problem for communities living in the vicinity of road networks in urban areas. The study clearly shows that honking has a significant impact on traffic noise assessment. The discussed model performs well for traffic flow above 30 km/h while it needs further improvement where traffic is flowing at a creeping speed of less than 30 km/h. The study also reveals that traffic parameters such as frequent gear changes, driving pattern, horn intensity and duration affect sound level and need further investigation for better traffic noise prediction and noise pollution management.
Acknowledgment
The authors are grateful to the Director, CSIR-National Environment Engineering Research Institute for encouraging and providing necessary infrastructure and permission to publish the paper. Author is also thankful to the Department of Science and Technology, New Delhi for sponsoring the study.
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