scholarly journals Integrated road traffic noise mapping in urban Indian context

Noise Mapping ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. 99-113 ◽  
Author(s):  
Dipeshkumar R. Sonaviya ◽  
Bhaven N. Tandel
Keyword(s):  
Prediction Models ◽  
Road Traffic ◽  
Traffic Noise ◽  
Traffic Volume ◽  
Heterogeneous Traffic ◽  
Road Traffic Noise ◽  
Noise Mapping ◽  
Traffic Conditions ◽  
Road Geometry ◽  
Vehicular Speed

AbstractRoad traffic noise has been recognized as a serious issue that affects the urban regions. Due to urbanization and industrialization, transportation in urban areas has increased. Traffic noise characteristics in cities belonging to a developing country like India are highly varied compared to developed nations because of its heterogeneous conditions. The objective of the research study is to assess noise pollution due to heterogeneous traffic conditions and the impact of horn honking due to un-authorized parked vehicles on the main roadside. Noise mapping has been done using the computer simulation model by taking various noise sources and noise propagation to the receiver point. Traffic volume, vehicular speed, noise levels, road geometry, un-authorized parking, and horn honking were measured on tier-II city roads in Surat, India. The study showed not so significant correlation between traffic volume, road geometry, vehicular speed and equivalent noise due to heterogeneous road traffic conditions. Further, analysis of traffic noise showed that horn honking due to un-authorized parked vehicles contributed an additional up to 11 dB (A), which is quite significant. The prediction models such as U.K’s CoRTN, U.S’s TNM, Germany’s RLS-90 and their modified versions have limited applicability for heterogeneity. Hence, the noise prediction models, which can be used for homogeneous road traffic conditions are not successfully applicable in heterogeneous road traffic conditions. In this research, a new horn honking correction factor is introduced with respect to unauthorized parked vehicles. The horn honking correction values can be integrated into noise model RLS-90, while assessing heterogeneous traffic conditions.

scholarly journals 2-D noise maps for tier-2 city urban Indian roads

Noise Mapping ◽  
2019 ◽  
Vol 6 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Dipeshkumar R. Sonaviya ◽  
Bhaven N. Tandel
Keyword(s):  
Road Traffic ◽  
Traffic Noise ◽  
Field Measurements ◽  
Noise Pollution ◽  
Heterogeneous Traffic ◽  
Computer Simulation Model ◽  
Road Traffic Noise ◽  
Noise Mapping ◽  
Traffic Condition ◽  
Traffic Composition

Abstract In today’s era, vehicular noise pollution has been identified as a serious danger that influence the attribute of the urban regions. To identify the influence of noise effects, noise maps are very useful. A noise mapping study has been carried out to study the propagation of urban road traffic noise in the areas along with field measurements. The computer simulation model (Sound- PLAN software) is used to developed noise maps. In developing nations like India, traffic composition is heterogeneous. These traffic compositions contain vehicles, which have different sizes, speeds variations and operating systems. Because of fluctuating speeds, deficiency of lane disciplines, and non-authorized parking on main road lanes, honking events becomes inevitable, which changes and affects the urban soundscape of nations like India. Due to horn events (heterogeneous traffic condition), noise level (LAeq) increase by 0.5–8 dB (A) as compared to homogeneous traffic conditions.

scholarly journals Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction

2021 ◽  
Vol 11 (13) ◽  
pp. 6030
Author(s):  
Daljeet Singh ◽  
Antonella B. Francavilla ◽  
Simona Mancini ◽  
Claudio Guarnaccia
Keyword(s):  
Machine Learning ◽  
Linear Models ◽  
Prediction Models ◽  
Road Traffic ◽  
Traffic Noise ◽  
Machine Learning Techniques ◽  
Coefficient Of Determination ◽  
Road Traffic Noise ◽  
Noise Prediction ◽  
Volume Percentage

A vehicular road traffic noise prediction methodology based on machine learning techniques has been presented. The road traffic parameters that have been considered are traffic volume, percentage of heavy vehicles, honking occurrences and the equivalent continuous sound pressure level. Leq A method to include the honking effect in the traffic noise prediction has been illustrated. The techniques that have been used for the prediction of traffic noise are decision trees, random forests, generalized linear models and artificial neural networks. The results obtained by using these methods have been compared on the basis of mean square error, correlation coefficient, coefficient of determination and accuracy. It has been observed that honking is an important parameter and contributes to the overall traffic noise, especially in congested Indian road traffic conditions. The effects of honking noise on the human health cannot be ignored and it should be included as a parameter in the future traffic noise prediction models.

Road traffic noise disease burden estimates for a model study of varying urban morphology cases

2021 ◽  
Vol 263 (3) ◽  
pp. 3282-3293
Author(s):  
Jens Forssén ◽  
Andreas Gustafson ◽  
Meta Berghauser Pont ◽  
Marie Haeger-Eugensson ◽  
Christine Achberger ◽  
...  
Keyword(s):  
Disease Burden ◽  
Noise Exposure ◽  
Road Traffic ◽  
Traffic Noise ◽  
Urban Morphology ◽  
Urban Setting ◽  
Road Traffic Noise ◽  
Noise Mapping ◽  
Direct Exposure ◽  
Life Years

For a model set of 31 different building morphologies in an urban setting, road traffic noise exposure has been calculated and analysed. For five of the building morphologies also vegetation surfaces on facades and roofs were studied. Facade exposures were analysed for both smaller (single-sided) flats and larger (floor-through) flats, considering the direct exposure from the roads as well as the non-direct exposure at noise-shielded positions like inner yards, applying a noise mapping software in combination with a prediction model for the non-direct exposure. Using noise indicators Lden and Lnight, the disease burden, in terms of DALY (Disability-Adjusted Life Years) per person, was estimated and analysed, via predictions of annoyance and sleep disturbance. The resulting effects of varying the building morphology and adding vegetation are shown and discussed, including effects of a bonus model for flats having additional facade elements with lower noise exposure.

Road traffic noise mapping in the Federal University of Santa Maria, Brazil

2015 ◽  
Vol 138 (3) ◽  
pp. 1732-1732 ◽  
Author(s):  
Olmiro C. de Souza Neto ◽  
Stephan Paul
Keyword(s):  
Road Traffic ◽  
Traffic Noise ◽  
Road Traffic Noise ◽  
Noise Mapping ◽  
Santa Maria

scholarly journals Road traffic noise prediction models: a review

2008 ◽  
Vol 3 (3) ◽  
pp. 257-271 ◽  
Author(s):  
H.N. Rajakumara ◽  
R.M. Mahalinge Gowda
Keyword(s):  
Prediction Models ◽  
Road Traffic ◽  
Traffic Noise ◽  
Road Traffic Noise ◽  
Noise Prediction

scholarly journals Characterization of a WASN-Based Urban Acoustic Dataset for the Dynamic Mapping of Road Traffic Noise

Proceedings ◽  
2020 ◽  
Vol 42 (1) ◽  
pp. 60 ◽  
Author(s):  
Francesc Alías ◽  
Joan Claudi Socoró ◽  
Ferran Orga ◽  
Rosa Ma Alsina-Pagès
Keyword(s):  
Sensor Networks ◽  
Road Traffic ◽  
Traffic Noise ◽  
Road Traffic Noise ◽  
Acoustic Sensor ◽  
Noise Mapping ◽  
Acoustic Environment ◽  
Operation Conditions ◽  
Dynamic Mapping ◽  
Acoustic Sensor Networks

Road Traffic Noise (RTN) is one of the main pollutants in urban and suburban areas, negatively affecting the quality of life of their inhabitants. In the context of the European LIFE DYNAMAP project, two Wireless Acoustic Sensor Networks (WASN) have been deployed to monitor RTN: one in District 9 of Milan, and another along the A90 motorway of Rome. Since the dynamic mapping system should be able to identify and remove those Anomalous Noise Events (ANEs) unrelated to regular road traffic (e.g., sirens, horns, speech, and doors), an Anomalous Noise Event Detector (ANED) has been included in the dynamic noise mapping pipeline to avoid biasing the computation of the equivalent RTN levels. After deploying the 24 low-cost acoustic sensor networks in both pilot areas, WASN-based acoustic datasets were built to adapt the previous version of the ANED algorithm to run in real-operation conditions. In this work, we describe the preliminary results of the analysis of the 154 h WASN-based urban acoustic dataset obtained from the Milan city in terms of the main characteristics of ANEs. The results confirm the unbalanced nature of the problem (83.7% of the data corresponds to RTN), showing the urban WASN-based dataset a larger number of ANEs with higher local predominance than what was observed in the previous expert-based recording campaign, which underlines the importance of the accurate modeling of the urban acoustic environment to train the ANED properly.

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