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Year : 2009
 Volume
: 11  Issue : 45  Page
: 189193 

Development of a noise prediction model under interrupted traffic flow conditions: A case study for Jaipur city 

Sheetal Agarwal, Bajrang L Swami, Akhilendra Bhushan Gupta
Department of Civil Engineering, Malaviya National Institute of Technology, Jaipur, India
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Date of Web Publication  2Oct2009 




The objective of this study is to develop an empirical noise prediction model for the evaluation of equivalent noise levels (Leq) under interrupted traffic flow conditions. A new factor tendency to blow horn (A_{ H} ) was introduced in the conventional federal highway administrative noise prediction (FHWA) model and a comparative study was made between FHWA and modified FHWA models to evaluate the best suitability of the model. Monitoring and modeling of Leq were carried out at four selected intersections of Jaipur city. After comparison of the results, it was found that the modified FHWA model could be satisfactorily applied for Indian conditions as it gives acceptable results with a deviation of 3 dB (A). In addition, statistical analysis of the data comprising measured and estimated values shows a good agreement. Hence, the modified FHWA traffic noise prediction model can be applied to the cities having similar traffic conditions as in Jaipur city. Keywords: Ambient noise levels, average difference, capacity ratio, horn, interrupted traffic flow condition, tendency to blow
How to cite this article: Agarwal S, Swami BL, Gupta AB. Development of a noise prediction model under interrupted traffic flow conditions: A case study for Jaipur city. Noise Health 2009;11:18993 
How to cite this URL: Agarwal S, Swami BL, Gupta AB. Development of a noise prediction model under interrupted traffic flow conditions: A case study for Jaipur city. Noise Health [serial online] 2009 [cited 2022 Oct 4];11:18993. Available from: https://www.noiseandhealth.org/text.asp?2009/11/45/189/56211 
Introduction   
Traffic is an important source of noise pollution which directly affects the human health.^{ [1]} It can be seen that the transportation sector is a major contributor of noise pollution.^{ [2]} In India, the number of vehicles is increasing at a rate of more than 7% per annum, which creates a serious threat of noise pollution.^{ [3],[4]}
Jaipur is the capital city of the state of Rajasthan. It has an annual growth rate of 4.9% for population and 12.75% for vehicles.^{ [5]} It clearly indicates that the public transport system is very inefficient and inadequate, resulting in extraordinary growth of personalized vehicles. Besides this, the heterogeneous nature of traffic, continuously plying on roads, develops the interrupted traffic flow conditions and is directly responsible for traffic congestion. It increases the tendency to blow a horn.^{ [6]}
In the present study, a new factortendency to blow horn (A_{ H} ) is introduced in the conventional FHWA model for calculation of equivalent noise levels, (Leq) under interrupted traffic flow conditions. For this, four highly busy intersections were selected for monitoring and modeling and an empirical model was developed .
Research Review   
Federal highway administrative noise prediction model (FHWA)
Traffic noise prediction algorithm is as^{ [7]}
where L_{ 0} is the basic/reference noise level for a stream of vehicles and L_{ i} is adjustment applied.
The hourly Leq value for each category of vehicles is calculated using the following equation:
where Leq (c) = equivalent noise level, calculated by the conventional FHWA model, L_{ 0} = basic/reference noise level, A_{ vs } = volume and speed correction, A_{ D} = distance correction and A_{ S} = ground cover correction.
Calculation of adjustment applied to the FHWA model^{ } FHWA model calculates noise level through a series of adjustments applied which are discussed below.^{ [8]}
(1) Basic/reference noise level: it is the noise emitted by particular class of vehicles at a distance of 10 m from the nearest traffic lane. The reference noise level equation for various categories of vehicle on bituminous pavement is shown in [Table 1].
(2) Volume and speed adjustment: it has been adopted as suggested by the FHWA model. The equation is given as
where Q = traffic flow of each vehicle category (vehicles per hour), v = speed in kmph and D_{ 0} = reference distance (= 10m).
(1) Distance adjustment: It is given in Equation 4:
where α = the value of α is different for different locations. It depends on the ground cover coefficient (ranging from 0 to 0.75). In the present study, it is taken as 0 and 0.25.
Equivalent noise level for each category of vehicle is evaluated using Equation (2) and then combined logarithmically to get the total Leq (c) value. It is given in the Equation (5).
Materials and Methods   
Four heavy to medium busy commercial corridors of Jaipur city were selected for the present study, covering commercial land use only. Details of each site are given in [Table 2].
At each location, measurements were made when there was a reasonable traffic activity (in general from 10 am to 7 pm). Readings of equivalent observed noise levels (Leq [o]) were recorded at 10min intervals. Leq (o) values (in dB [A]) for each hour were obtained by integrating all recorded values.
Various other traffic parameters i.e., classified traffic volume and traffic speed with the road geometrics were monitored at various identified locations of Jaipur city. "Sound Level Meter CYGNET2021" having digital display was used to record the equivalent noise level at different selected locations. The classified traffic speed was recorded at all the selected locations using the "Doppler Radar Speedometer ModelMK11."
The sound level meter (SLM) was mounted on a stand at a height of 1.2 m above the ground level and was located at 7.5 m distance from the center line on the road way during interrupted traffic flow conditions. The classified two directional traffic volumes were recorded manually. Traffic composition in the study area included, two wheelers, three wheelers, cars, jeeps, vans, buses, and mini buses, light commercial vehicles (LCV's) and trucks.
Results and Discussion   
[Table 3] represents the Leq (o) and Leq (c) for all the four locations. It was found that there was an unexplained difference of 515 dB (A) between Leq (o) and Leq (c) values. For calculation of this unexplained noise difference, a new correction factor i.e., tendency to blow horn (A_{ H} ) was introduced in the conventional FHWA model. For calculation of this new factor, a logarithmic graph between capacity ratio (ratio between calculated capacity to standard capacity in 1 hour) and average difference (difference between Leq (o) and Leq (c)) was plotted using Microsoft excel and the equation for the bestfit curve (Y = A + B* log X) was developed for all selected locations. A combined graph was developed for all investigated locations. The shape of the curve so obtained is given as shown in [Figure 1].
The above mentioned curve gave the following equation with the value of R^{ 2 =} 0.5524.
where X = capacity ratio.
It was found that the value of R^{ 2} =0.5524 (as presented in [Figure 1]),was not very good. But, if the same study was applied to the individual sites, it would give better R^{ 2} value. Details are given in [Table 4] and obtained curve is given in [Figure 2].
Since, the main aim of this study was to develop a generalized form of the modified FHWA model, which can be applied for the whole city, hence a big dataet was taken by clubbing together the observations at four different sites in order to develop a common equation. As, there were huge differences among road geometrical dimensions, road conditions, traffic characteristics and population density at all investigated locations, big dataset gave lesser R^{ 2} value (R^{ 2} = 0.5524) as in Equation (6).
Therefore, in Indian conditions the modified FHWA model may be taken as:
where Leq (m) = hourly equivalent noise level, calculated by the modified FHWA model, L_{ 0} = basic/reference noise level, A_{ vs } = volume and speed correction, A_{ D} = distance correction, A_{ S} = round cover correction and A_{ H} = tendency to blow horn.
The abovementioned equation was applied at all four selected locations of Jaipur city and Leq (m) values for all the identified locations are given in [Table 5]. It can be observed that the Leq (m) values were closer to the Leq (o) values. Hence, the modified FHWA model is significantly more applicable under the interrupted traffic flow conditions than the conventional FHWA model.
Model interpretation
To determine the applicability of the modified FHWA model, a XY scatter plot was drawn between Leq (o) vs Leq (c) and Leq (o) vs Leq (m) as shown in [Figure 3] and [Figure 4], and a bestfit line was also generated which gave an R^{ 2 } value of 0.4321 for the conventional FHWA model and 0.8337 for the modified FHWA model, respectively. Based on the findings, it can be observed that the modified FHWA model gives a strong correlation (R^{ 2} =0.8337) than the conventional FHWA model. Testing of the goodness of fit to the models was also done by the paired the ttest technique. The paired ttest results of both FHWA model and modified FHWA models at 5% level significance are shown in [Table 6].
It was found that for a degree of freedom 78 at 5% level of significance, modified FHWA model gave tstatistical values less than the tabulated values of tcritical. Hence, it is concluded that this model is significantly fit for field data.
Conclusions   
Road traffic noise directly depends on the interrupted traffic flow conditions which causes congested traffic movement and increases the tendency to blow horn.
In the present study, a new factor i.e., tendency to blow horn (A_{ H} ) was introduced in the conventional FHWA model and a comparison was done among Leq (o), Leq (c), and Leq (m). The regression equation was also developed to produce better performance of the model. The statistical data analysis between predicted and observed Leq values was carried out to determine the suitability of the model. It can be concluded that the modified FHWA model gives significantly higher correlation coefficient values and can be applied for the calculation of road traffic noise under interrupted traffic flow conditions in urban areas of Indian cities.
References   
1.  Rao KV, Padmaja P. Ambient noise level monitoring in Gwalior at various zones. J Environ Pollution 1999;2:2114. 
2.  Murugesan R. Noise nuisance by road traffic assessment and control. Indian Highways 1997;25:2531. 
3.  Sharma N. Vehicular pollution modelling in India. J Inst Engg 2005;85:4653. 
4.  Rajakumar HN, Gowda Mahalinga RM. Road traffic noise prediction model under interrupted traffic flow condition. Environ Monit Assess 2008;13:1518. 
5.  Srinivas DS. Energy, environment linkages of urban transport sector in India. Indian Highways 1996;24:2737. 
6.  Choudhary R, Patanayak SK, Gupta AB, Vyas AK, Swami BL. Application And modification of FHWA model for noise prediction at congested commercial location of Jaipur city. Indian J Environ Protect 2003;23:90712. 
7.  Bhattacharya CC, Jain SS, Singh SP, Parida M, Mittal N. Development of comprehensive highway noise model for Indian condition. J Indian Roads Congr 2001;62:45387. 
8.  Nirjar RS, Jain SS, Singh SP, Parida M, Katiyar VS, Mittal N. A study of transport related noise pollution in Delhi. J Inst Eng 2003;84:615. 
Correspondence Address: Bajrang L Swami Civil Engineering Department, Malaviya National Institute of Technology, Jaipur 302 017, Rajasthan India
Source of Support: None, Conflict of Interest: None  Check 
DOI: 10.4103/14631741.56211
[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6] 

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