Prediction of L10 and Leq Noise Levels Due to Vehicular Traffic in Urban Area Using ANN and Adaptive Neuro-Fuzzy Interface System (ANFIS) Approach

2022 ◽  
pp. 597-611
Author(s):  
Vilas K Patil ◽  
P.P. Nagarale
Keyword(s):  
Noise Level ◽  
Urban Areas ◽  
Road Traffic ◽  
Traffic Noise ◽  
Vehicular Traffic ◽  
Noise Levels ◽  
Anfis Model ◽  
Neuro Fuzzy ◽  
Interface System ◽  
Artificial Neural Network Ann

Recently in urban areas, road traffic noise is one of the primary sources of noise pollution. Variation in noise level is impacted by the synthesis of traffic and the percentage of heavy vehicles. Presentation to high noise levels may cause serious impact on the health of an individual or community residing near the roadside. Thus, predicting the vehicular traffic noise level is important. The present study aims at the formulation of regression, an artificial neural network (ANN) and an adaptive neuro-fuzzy interface system (ANFIS) model using the data of observed noise levels, traffic volume, and average speed of vehicles for the prediction of L10 and Leq. Measured noise levels are compared to the noise levels predicted by the experimental model. It is observed that the ANFIS approach is more superior when compared to output given by regression and an ANN model. Also, there exists a positive correlation between measured and predicted noise levels. The proposed ANFIS model can be utilized as a tool for traffic direction and planning of new roads in zones of similar land use pattern.

Prediction of L10 and Leq Noise Levels Due to Vehicular Traffic in Urban Area Using ANN and Adaptive Neuro-Fuzzy Interface System (ANFIS) Approach

2019 ◽  
Vol 15 (2) ◽  
pp. 92-105
Author(s):  
Vilas K Patil ◽  
P.P. Nagarale
Keyword(s):  
Noise Level ◽  
Urban Areas ◽  
Road Traffic ◽  
Traffic Noise ◽  
Road Traffic Noise ◽  
Vehicular Traffic ◽  
Noise Levels ◽  
Anfis Model ◽  
Neuro Fuzzy ◽  
Interface System

Recently in urban areas, road traffic noise is one of the primary sources of noise pollution. Variation in noise level is impacted by the synthesis of traffic and the percentage of heavy vehicles. Presentation to high noise levels may cause serious impact on the health of an individual or community residing near the roadside. Thus, predicting the vehicular traffic noise level is important. The present study aims at the formulation of regression, an artificial neural network (ANN) and an adaptive neuro-fuzzy interface system (ANFIS) model using the data of observed noise levels, traffic volume, and average speed of vehicles for the prediction of L10 and Leq. Measured noise levels are compared to the noise levels predicted by the experimental model. It is observed that the ANFIS approach is more superior when compared to output given by regression and an ANN model. Also, there exists a positive correlation between measured and predicted noise levels. The proposed ANFIS model can be utilized as a tool for traffic direction and planning of new roads in zones of similar land use pattern.

scholarly journals Vehicular Traffic Noise Modelling of Urban Area- A Contouring & Artificial Neural Network-Based Approach

2021 ◽  
Author(s):  
Abhijit Debnath ◽  
Prasoon Kumar Singh ◽  
Sushmita Banerjee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Traffic Flow ◽  
Noise Level ◽  
Urban Areas ◽  
Traffic Noise ◽  
Traffic Volume ◽  
Noise Levels ◽  
Noise Characteristics ◽  
Artificial Neural

Abstract Road traffic vehicular noise is one of the main sources of environmental pollution in urban areas of India. Also, steadily increasing urbanization, industrialization, infrastructures around city condition causing health risks among the urban populations. In this study we have explored noise descriptors (L10, L90, Ldn, LNI, TNI, NC), contour plotting and finds the suitability of artificial neural networks (ANN) for the prediction of traffic noise all around the Dhanbad township in 15 monitoring stations. In order to develop the prediction model, measuring noise levels of five different hours, speed of vehicles and traffic volume in every monitoring point have been studied and analyzed. Traffic volume, percent of heavy vehicles, Speed, traffic flow, road gradient, pavement, road side carriageway distance factors taken as input parameter, whereas LAeq as output parameter for formation of neural network architecture. As traffic flow is heterogenous which mainly contains 59% 2-wheelers and different vehicle specifications with varying speeds also effects driving and honking behavior which constantly changing noise characteristics. From radial noise diagrams shown that average noise levels of all the stations beyond permissible limit and highest noise levels were found at the speed of 50-55 km/h in both peak and non-peak hours. Noise descriptors clearly indicates high annoyance level in the study area. Artificial neural network with 7-7-5 formation has been developed and found as optimum due to its sum of square and overall relative error 0.858 & .029 in training and 0.458 & 0.862 in testing phase respectively. Comparative analysis between observed and predicted noise level shows very less deviation up to ±0.6 dB(A) and the R2 linear values are more than 0.9 in all five noise hours indicating the accuracy of model. Also, it can be concluded that ANN approach is much superior in prediction of traffic noise level to any other statistical method.

scholarly journals Traffic Noise Level Assessment in the Residential Area around Different Road Functions in Malang City, East Java, Indonesia

2021 ◽  
Vol 29 (3) ◽  
Author(s):  
Ngudi Tjahjono ◽  
Imam Hanafi ◽  
Latipun Latipun ◽  
Suyadi Suyadi
Keyword(s):  
Psychological Health ◽  
Noise Level ◽  
Urban Areas ◽  
Negative Impact ◽  
Heavy Traffic ◽  
Traffic Noise ◽  
Sound Level ◽  
Noise Levels ◽  
Significant Difference ◽  
Motorized Vehicles

Noise due to motorized vehicles is a major problem in urban areas which can interfere with physiological and psychological health. This study aims to determine the extent of noise levels outside and inside the house around the function of different roads in Malang City, East Java, Indonesia. The study was conducted by measuring the traffic noise level using a sound level meter. Measurements were taken in the afternoon between 16.00-21.00 during the peak of heavy traffic and outside working hours when residents were already at home. Statistical Student’s t-test analysis was used to test differences in the average noise level outside and inside the house on each road function. Variance analysis was used to compare noise levels around primary arterial roads, secondary arteries, primary collectors, secondary collectors, primary local, and secondary local. From the measurement results, it is known that the noise due to motorized vehicles is 84.28 dB on average. This exceeds the threshold based on the Decree of the State Minister for the Environment Number 48 of 1996. There was a significant difference in noise level between outside and inside the house on each road function. There was no significant difference in noise level between the functions of the road segments both outside and inside the house. The results of the study concluded that the traffic noise level at 16:00 to 21:00 hours on all roads that were targeted for research exceeds the national threshold. It is recommended that the level of traffic noise around roads in the city of Malang can be reduced to minimize the negative impact on physiological and psychological health.

scholarly journals Road Traffic Noise on the Santa Marta City Tourist Route

2021 ◽  
Vol 11 (16) ◽  
pp. 7196
Author(s):  
Dámaris A. Jiménez-Uribe ◽  
Darwin Daniels ◽  
Zoë L. Fleming ◽  
Andrés M. Vélez-Pereira
Keyword(s):  
Traffic Flow ◽  
Noise Level ◽  
Road Traffic ◽  
Traffic Noise ◽  
Environmental Noise ◽  
Time Of Day ◽  
Road Traffic Noise ◽  
Sampling Point ◽  
Heavy Vehicles ◽  
Noise Levels

The objective of this study was to determine the influence of vehicular traffic on the environmental noise levels of the Santa Marta City tourist route on the Colombian coast. An analysis of vehicle types and frequencies at various times of the day over nearly a year helped to track the main sources of environmental noise pollution. Five sampling points were selected, which were distributed over 12 km, with three classified as peripheral urban and two as suburban. The average traffic flow was 966 vehicles/h and was mainly composed of automobiles, with higher values in the peripheral urban area. The noise level was 103.3 dBA, with background and peak levels of 87.2 and 107.3 dBA, respectively. The noise level was higher during the day; however, there were no differences between weekdays and weekends. The results from the analysis of variance showed that the number of vehicles and the noise levels varied greatly according to the time of day and sampling point location. The peak and mean noise levels were correlated with the number of automobiles, buses and heavy vehicles. The mean noise levels were similar at all sample points despite the traffic flow varying, and the background noise was only correlated for automobiles (which varied much more than the heavy vehicles between day and night).

scholarly journals Urban Noise Modelling in Boka Kotorska Bay

2014 ◽  
Vol 26 (2) ◽  
pp. 151-157
Author(s):  
Aleksandar Nikolić ◽  
Danilo Nikolić ◽  
Emilija Nikolić ◽  
Vesna Vujačić
Keyword(s):  
Noise Level ◽  
Urban Areas ◽  
Road Traffic ◽  
Noise Source ◽  
Empirical Methods ◽  
Noise Levels ◽  
Sound Effects ◽  
Noise Assessment ◽  
A Site ◽  
The Village

Traffic is the most significant noise source in urban areas. The village of Kamenari in Boka Kotorska Bay is a site where, in a relatively small area, road traffic and sea (ferry) traffic take place at the same time. Due to the specificity of the location, i.e. very rare synergy of sound effects of road and sea traffic in the urban area, as well as the expressed need for assessment of noise level in a simple and quick way, a research was conducted, using empirical methods and statistical analysis methods, which led to the creation of acoustic model for the assessment of equivalent noise level (Leq). The developed model for noise assessment in the Village of Kamenari in Boka Kotorska Bay quite realistically provides data on possible noise levels at the observed site, with very little deviations in relation to empirically obtained values.

scholarly journals Theoretical Comparison of the Effects of Different Traffic Conditions on Urban Road Traffic Noise

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Mohammad Maghrour Zefreh ◽  
Adam Torok
Keyword(s):  
Traffic Flow ◽  
Noise Level ◽  
Urban Areas ◽  
Road Traffic ◽  
Traffic Noise ◽  
Urban Traffic ◽  
Road Traffic Noise ◽  
Traffic Lights ◽  
Urban Road ◽  
Traffic Conditions

Road traffic noise is one of the most relevant sources in the environmental noise pollution of the urban areas where dynamics of the traffic flow are much more complicated than uninterrupted traffic flows. It is evident that different traffic conditions would play the role in the urban traffic flow considering the dynamic nature of the traffic flow on one hand and presence of traffic lights, roundabouts, etc. on the other hand. The main aim of the current paper is to investigate the effect of different traffic conditions on urban road traffic noise. To do so, different traffic conditions have been theoretically generated by the Monte Carlo Simulation technique following the distribution of traffic speed in the urban roads. The “ASJ RTN-Model” has been considered as a base road traffic noise prediction model which would deal with different traffic conditions including steady and nonsteady traffic flow that would cover the urban traffic flow conditions properly. Having generated the vehicles speeds in different traffic conditions, the emitted noise (LWA) and subsequently the noise level at receiver (LA) were estimated by “ASJ RTN-Model.” Having estimated LWA and LA for each and every vehicle in each traffic condition and taking the concept of transient noise into account, the single event sound exposure levels (SEL) in different traffic conditions are calculated and compared to each other. The results showed that decelerated traffic flow had the lowest contribution, compared to congestion, accelerated flow, free flow, oversaturated congestion, and undersaturated flow by 16%, 14%, 12%, 12%, and 10%, respectively. Moreover, the distribution of emitted noise and noise level at receiver were compared in different traffic conditions. The results showed that traffic congestion had considerably the maximum peak compared to other traffic conditions which would highlight the importance of the range of generated noise in different traffic conditions.

scholarly journals Influence of Honking on the Road Traffic Noise Generated at Urban Rotaries for Heterogeneous Traffic

2020 ◽  
Vol 24 (1) ◽  
pp. 23-42
Author(s):  
Kamineni Aditya ◽  
Venkaiah Chowdary
Keyword(s):  
Noise Level ◽  
Road Traffic ◽  
Noise Analysis ◽  
Traffic Noise ◽  
Heterogeneous Traffic ◽  
Road Traffic Noise ◽  
Good Precision ◽  
Heavy Vehicles ◽  
Noise Levels ◽  
Noise Data

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.

scholarly journals Road traffic noise assessment in a hospital area in Umuarama - Brazil

2021 ◽  
Vol 9 (74) ◽  
Author(s):  
Samantha Junqueira Moreira ◽  
Warde Antonieta Da Fonseca-Zang ◽  
Cecília de Castro Bolina ◽  
Stella Alonso Rocha ◽  
Paulo Henrique Trombetta Zannin
Keyword(s):  
Strong Correlation ◽  
Sound Pressure ◽  
Road Traffic ◽  
Traffic Noise ◽  
Noise Pollution ◽  
Road Traffic Noise ◽  
Vehicular Traffic ◽  
Noise Levels ◽  
Hospital Environments ◽  
Noise Assessment

In hospital environments, high noise levels can result in damage to patients' treatments, delaying their rest and recovery. The sound pressure level (SPL) in hospital areas during the day must not exceed 50 dB and 45 dB (A) at night, according to NBR 10.151/2019. This research aimed to carry out environmental monitoring of equivalent sound pressure levels (LAeq) at fifteen points in the vicinity of three hospitals in the central region of the municipality of Umuarama-PR, during working days, at four different times, in the months of August, September and November 2018 and continued in March 2019. To this end, we sought to map the LAeq of the points, compare them with data from municipal and federal legislation and relate the LAeq to the volume of vehicular traffic. The collected SPL were higher than recommended by NBR 10.151 at all times and measurement points, during the week, and when considering the municipal regulations, only one point is in the equipment's accuracy limit. From the statistical analysis, a very strong correlation was observed between LAeq and the total volume of vehicles, and also a strong correlation between the descriptors L10 and L50 and the volume of vehicles. The Traffic Noise Index (TNI) was also calculated and the LAeq values ​​were compared with a subjective noise rating. The results show a scenario of noise pollution in the area and there is a need for the application of mitigating measures.

scholarly journals Spatial Mapping of Traffic Noise Levels in Urban Areas

2011 ◽  
Vol 47 (2) ◽  
Author(s):  
Mohammed Taleb Obaidat
Keyword(s):  
Noise Level ◽  
Urban Areas ◽  
City Planning ◽  
Traffic Noise ◽  
Sound Level ◽  
Signalized Intersections ◽  
Mapping Technique ◽  
Residential Areas ◽  
Noise Levels ◽  
High Traffic Volume

This paper combines field data with an analytical approach to spatially map noise levels due to traffic movements at relatively high traffic volume signalized intersections utilizing the potential of Geographic Information Systems (GIS). Noise data were collected using a discrete mapping technique at 29 signalized intersections, as well as between intersections, and at their respective neighborhood areas in Amman, capital of Jordan. Data were collected in three different highly congested traffic peak periods: 7:30 a.m.-9:00 a.m., 1:30 p.m.-3:00 p.m., and 9:00 p.m.-11:00 p.m. A portable precision sound level meter capable of measuring noise levels from 34 to 134 decibels (dB) was used during the data collection process. The highest recorded noise level at some signals was 80 dB, while the lowest was 34 dB. In fact, some signalized intersections showed higher noise levels than the acceptable or the standard ones, i.e., 65 dB for daytime and 55 dB for nighttime in residential areas at city center. Two-dimensional (2D) vector and raster maps of noise levels, at different time periods for signals' areas and neighborhoods, were spatially displayed. Results showed that the developed GIS maps could be useful for city planning and other environmental management applications for the purpose of: 1) temporal monitoring and queries of noise level changes as a function of time, 2) spatial queries to find the highest noise disturbance location and its time of the day, 3) development of an online noise information system, 4) using noise level based spatial maps as indicators of variation in land prices, and 5) forecasting and current assessment of the acoustic climate of urban areas.

scholarly journals MODELLING URBAN NOISE IN CITYGML ADE: CASE OF THE NETHERLANDS

2017 ◽  
Vol IV-4/W5 ◽  
pp. 73-81 ◽  
Author(s):  
K. Kumar ◽  
H. Ledoux ◽  
T. J. F. Commandeur ◽  
J. E. Stoter
Keyword(s):  
Urban Areas ◽  
Input Data ◽  
Road Traffic ◽  
Traffic Noise ◽  
Environmental Noise ◽  
Motor Vehicles ◽  
Road Traffic Noise ◽  
Noise Levels ◽  
Noise Mapping ◽  
Urban Noise

Road traffic and industrial noise has become a major source of discomfort and annoyance among the residents in urban areas. More than 44 % of the EU population is regularly exposed to road traffic noise levels over 55 dB, which is currently the maximum accepted value prescribed by the Environmental Noise Directive for road traffic noise. With continuously increasing population and number of motor vehicles and industries, it is very unlikely to hope for noise levels to diminish in the near future. Therefore, it is necessary to monitor urban noise, so as to make mitigation plans and to deal with its adverse effects. The 2002/49/EC Environmental Noise Directive aims to determine the exposure of an individual to environmental noise through noise mapping. One of the most important steps in noise mapping is the creation of input data for simulation. At present, it is done semi-automatically (and sometimes even manually) by different companies in different ways and is very time consuming and can lead to errors in the data. In this paper, we present our approach for automatically creating input data for noise simulations. Secondly, we focus on using 3D city models for presenting the results of simulation for the noise arising from road traffic and industrial activities in urban areas. We implemented a few noise modelling standards for industrial and road traffic noise in CityGML by extending the existing Noise ADE with new objects and attributes. This research is a steping stone in the direction of standardising the input and output data for noise studies and for reconstructing the 3D data accordingly.

Sign in / Sign up

Export Citation Format

Share Document