Road Traffic Noise Analysis at the U-Turn in Makassar City

AbstractThis paper presents a study conducted at major rotaries for quantifying the traffic noise levels by considering the vehicle volume and their respective honking as governing parameters for heterogeneous traffic. Traffic volume and traffic noise data was collected using a digital video camera and a class 1 sound level meter, respectively. The traffic noise data was analysed using noise tools for identifying the noise level variation. The data collected was subjected to statistical analysis for light, medium and heavy vehicles, and their contribution towards noise levels is proven to be effective with the forthright fact that, heavy vehicles and their corresponding honking were majorly affecting the equivalent noise level compared to other vehicular proportion. An equivalent noise level [LAeq (dB)] rise of 2 to 6 dB (A) is solely caused by heavy vehicles, which is an important observation to be considered for traffic noise analysis at the rotaries. Based on the obtained results from one of the rotaries, noise prediction model is developed for estimating the LAeq (dB), which is able to predict the noise levels with good precision when validated with the data collected at second rotary intersection for different vehicle volumes.

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Simulation and Analysis of Road Traffic Noise among Urban Buildings Using Spatial Subdivision-Based Beam Tracing Method

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16142491 ◽

2019 ◽

Vol 16 (14) ◽

pp. 2491 ◽

Cited By ~ 3

Author(s):

Haibo Wang ◽

Ming Cai ◽

Hongjun Cui

Keyword(s):

Road Traffic ◽

Noise Analysis ◽

Traffic Noise ◽

Point Sources ◽

Road Traffic Noise ◽

Emission Model ◽

Noise Abatement ◽

Urban Buildings ◽

Spatial Subdivision ◽

Tracing Method

In order to realize the simulation and evaluation of road traffic noise among urban buildings, a spatial subdivision-based beam-tracing method is proposed in this study. First, the road traffic source is divided into sets of point sources and described with the help of vehicle emission model. Next, for each pair of source and receiver, spatial subdivision-based beam-tracing method is used in noise paths generation. At last, noise distribution can be got by noise calculation of all receivers considering the complex transmission among urban buildings. A measurement experiment with a point source is carried out to validate the accuracy of the method; the 0.8 m height and 2.5-m height average errors are about 0.9 dB and 1.2 dB, respectively. Moreover, traffic noise analysis under different building layouts and heights are presented by case applications and conclusions can be reached: (1) Different patterns result in different noise distributions and patterns designed as self-protective can lead to an obvious noise abatement for rear buildings. Noise differences between the front and rear buildings are about 7–12 dB with different patterns. (2) Noise value might not show a linear variation along with the height as shielding of different layers is various in reality.

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Determination of passenger car noise equivalent for mid-sized cities in India

INTER-NOISE and NOISE-CON Congress and Conference Proceedings ◽

10.3397/in-2021-1505 ◽

2021 ◽

Vol 263 (6) ◽

pp. 526-539

Author(s):

Adarsh Yadav ◽

Manoranjan Parida ◽

Brind Kumar

Keyword(s):

Road Traffic ◽

Noise Analysis ◽

Traffic Noise ◽

Concrete Pavement ◽

Road Traffic Noise ◽

Stream Data ◽

Noise Emission ◽

Passenger Car ◽

Reference Vehicle ◽

Traffic Stream

The heterogeneity in traffic flow composition increases the complexity of road traffic noise analysis for mid-sized in India. This study aims to determine a passenger car noise equivalent (PCNE) with respect to the average traffic stream speed that represents the number of a particular vehicle category with reference to an identified vehicle based on their noise emission characteristics. In the present study, vehicles are classified as bus, truck, light commercial vehicles (minibus, minitruck), three-wheelers (vikram-rickshaw), two-wheelers (bike/scooter), car, e-rickshaw and auto-rickshaw, and tractor-trailer. Car is taken as a reference vehicle for estimation of PCNE in our study due to its high percentage in traffic stream. Data has been collected on both bituminous and concrete pavement in Kanpur city, India, to analyze the differential effect of pavement on the noise level. As per this study, tractors-trailers, trucks, three-wheelers, and buses had a higher PCNE value, while two-wheelers and cars had almost similar PCNE value. A comparative analysis of PCNE value at concrete pavement is also conducted by considering car running on the bituminous pavement as reference vehicle. The study suggests to employ PCNE value in traffic noise analysis as it converts the divergent traffic volume in terms of the car.

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