Spatial Mapping of Traffic Noise Levels in Urban Areas

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.

Download Full-text

Assessment of traffic noise due to transverse rumble strips at residential areas

MATEC Web of Conferences ◽

10.1051/matecconf/201825002006 ◽

2018 ◽

Vol 250 ◽

pp. 02006

Author(s):

Zaiton Haron ◽

Darus Nadirah ◽

Supandi Mohamad Afif ◽

Yahya Khairulzan ◽

Nordiana Mashros ◽

...

Keyword(s):

Noise Level ◽

Traffic Noise ◽

Middle Layer ◽

Sound Level ◽

Skid Resistance ◽

Residential Areas ◽

Noise Levels ◽

Traffic Characteristics ◽

Level Meter ◽

Road Characteristics

Transverse rumble strips (TRS) are commonly being installed to alert the drivers through sound and vibration effects. The sound produced affects the existing traffic noise level which caused noise annoyance to the nearby residents. This study aims to assess the traffic noise due to TRS at residential areas by determining the roadside noise levels, traffic and road characteristics and evaluating the relationship between these parameters. Middle overlapped (MO), middle layer overlapped (MLO) and raised rumbler (RR) TRS profiles with same thickness were selected. The measurements of roadside noise levels and skid resistance were conducted using sound level meter (SLM) and British pendulum tester (BPT) respectively. Traffic characteristics were evaluated using previous data measured using automatic traffic counter (ATC). In overall, MLO produced highest roadside noise levels with increase of 20.5dBA from baseline. Generally, the increase of roadside noise level due to TRS is strong with speed, weak to medium with skid resistance of TRS and no relationship with traffic volume. Based on three TRS profile types, MLO is not suitable to be installed on the roadways adjacent to the residential areas as the increase of roadside noise level is significant which is more than 5dBA compared to MO and RR.

Download Full-text

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

Pertanika Journal of Science and Technology ◽

10.47836/pjst.29.3.05 ◽

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.

Download Full-text

Effect of distance from road intersection on developed traffic noise levels

Canadian Journal of Civil Engineering ◽

10.1139/l04-016 ◽

2004 ◽

Vol 31 (4) ◽

pp. 533-538 ◽

Cited By ~ 8

Author(s):

Saad Abo-Qudais ◽

Arwa Alhiary

Keyword(s):

Noise Level ◽

Traffic Noise ◽

Sound Level ◽

Signalized Intersections ◽

Environment Pollution ◽

Noise Levels ◽

The Road ◽

Traffic Intersection ◽

Noise Measurements ◽

Level Meter

The main objective of this study was to evaluate the variation in traffic equivalent noise levels as distance from the road intersection increases. To achieve this objective, traffic volume and equivalent noise level were monitored at 40 signalized intersections in Amman, the capital of Jordan. An integrated sound level meter (ISLM) was used to measure 1 min equivalent noise level along all approaches of the evaluated intersections. A total of 3326 noise measurements were performed. The collected data were analyzed to evaluate the variation of noise levels as distance from the intersection increases. The results indicated that equivalent noise levels were significantly affected by distance from the signal stop line. The equivalent noise levels at distances 50 and 100 m from the intersection were found to be 1.5 to 2.0 dB less than those at 0 m. While at 200, 250, and 300 m from the intersection, the monitored equivalent noise levels were found to be 3.8 to 4 dB higher than that at 0 m. At distances farther than 250 m, the measured equivalent noise levels tend to keep constant value of equivalent noise level as distance increased.Key words: noise, traffic, intersection, environment, pollution.

Download Full-text

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

10.21203/rs.3.rs-707329/v1 ◽

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.

Download Full-text

Traffic Noise Assessment at Residential Areas in Skudai, Johor

E3S Web of Conferences ◽

10.1051/e3sconf/20183402024 ◽

2018 ◽

Vol 34 ◽

pp. 02024

Author(s):

F.S. Sulaiman ◽

N. Darus ◽

N. Mashros ◽

Z. Haron ◽

K. Yahya

Keyword(s):

Noise Level ◽

Traffic Noise ◽

Noise Pollution ◽

Traffic Volume ◽

Sound Level ◽

Pollution Level ◽

Vehicle Speed ◽

Residential Areas ◽

Traffic Characteristics ◽

Noise Assessment

Vehicles passing by on roadways in residential areas may produce unpleasant traffic noise that affects the residents. This paper presents the traffic noise assessment of three selected residential areas located in Skudai, Johor. The objectives of this study are to evaluate traffic characteristics at selected residential areas, determine related noise indices, and assess impact of traffic noise. Traffic characteristics such as daily traffic volume and vehicle speed were evaluated using automatic traffic counter (ATC). Meanwhile, noise indices like equivalent continuous sound pressure level (LAeq), noise level exceeded 10% (L10) and 90% (L90) of measurement time were determined using sound level meter (SLM). Besides that, traffic noise index (TNI) and noise pollution level (LNP) were calculated based on the measured noise indices. The results showed an increase in noise level of 60 to 70 dBA maximum due to increase in traffic volume. There was also a significant change in noise level of more than 70 dBA even though average vehicle speed did not vary significantly. Nevertheless, LAeq, TNI, and LNP values for all sites during daytime were lower than the maximum recommended levels. Thus, residents in the three studied areas were not affected in terms of quality of life and health.

Download Full-text

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

10.4018/978-1-6684-2408-7.ch027 ◽

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.

Download Full-text

2D and 3D mapping of traffic induced noise near major roads passing through densely populated residential area of South Delhi, India

PLoS ONE ◽

10.1371/journal.pone.0248939 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0248939

Author(s):

Pervez Alam ◽

Kafeel Ahmad ◽

Afzal Husain Khan ◽

Nadeem A. Khan ◽

Mohammad Hadi Dehghani

Keyword(s):

Noise Level ◽

Noise Exposure ◽

Standard Procedure ◽

Sound Level ◽

Exposure Level ◽

Residential Areas ◽

Noise Levels ◽

Night Time ◽

Noise Mapping ◽

Regulatory Standards

Noise monitoring and mapping is the critical processes to ensure that the noise level does not reach the harmful levels and provides noise exposure level details. 2-D and 3-D noise mapping has been carried out at pre-selected critical locations of major roads passing through densely populated residential areas, namely, Mathura Road, Lodhi Road, Lala Lajpat Rai Road, and Ring road, along with significant intersections, viz. Moolchand, Ashram, Sabz Burj, and Lodhi road. The monitoring has been performed during the day and night’s peak traffic hours using Sound Level Meter (SLM) Larson & Davis 831as per standard procedure. Then after, 2-D and 3-D noise maps have been prepared, visualized, and analyzed by soundPLAN (acoustic) and MapInfo Pro (Desktop GIS). The maximum noise level is observed at Ashram Chowk [81.1 dB (A)] at 8 pm; however, the minimum noise level is found to be at Lala Lajpat Rai Road [76.4dB (A)] at 7 pm. Monitoring results of noise level show non-compliance of regulatory standards for day time and night time. 2-D noise maps revealed that the noise level is maximum at the centerline of the road and decreases either side with the distance, and remains above the permissible limits at all locations. However, the 3-D noise maps show horizontal as well as vertical noise levels at all locations. The 3-D noise maps also revealed a noise level of 70 dB (A) up to a height of 6.096m at the Ashram Chowk and Moolchand intersection. However, a noise level of 65 dB (A) has been observed at the height of 5.486m at Lala Lajpat Rai Marg and Sabz Burj. This study will explore noise levels in both horizontal and vertical directions near roads surrounded by high-rise buildings. It will help the decision-makers take remedial measures.

Download Full-text

Traffic-Noise Assessment at Rush Hours in Quetta City

Journal of Applied and Emerging Sciences ◽

10.36785/buitems.jaes.353 ◽

2020 ◽

pp. 50-56

Keyword(s):

Health Care ◽

Noise Level ◽

Noise Exposure ◽

Traffic Noise ◽

Sound Level ◽

Mitigation Strategies ◽

Noise Levels ◽

Contributing Factors ◽

Community Noise ◽

Noise Assessment

Noise is an environmental stressor, which leads to various ailments due to the physiological and psychological stresses it creates. It is essential to understand and evaluate the contributing factors of environmental noise, especially in densely polluted areas near major roads, railways and airports, for public health policy and planning. Noise level measurement permits precise and scientific analysis of noise annoyance, and therefore, this study aimed to determine the average noise levels of Quetta city. Seventy-three (73) location’s equivalent noise levels (Leq) were measured at peak rush hours for three consecutive days. Selected areas for measurement included health care centres, educational centres, government offices, public places, residential and commercial areas. All the selected sites were located near to main roads, where the traffic noise was the most prominent noise source. Noise was measured through calibrated microprocessor sound level meter. The results were computed by taking the mean of the three readings. The results showed 74 dBA as average noise level of Quetta city. It has been found that 90% of the selected locations in Quetta city exceeded the 65dBA, while 10 % of the total locations ranged between 55 to 65 dBA. The average noise exposure of the Quetta city was greater than the permissible international noise standard. This study identified the main traffic hubs of Quetta city, which requires mitigation strategies by the policy makers specifically for Health care and Educational sectors. It also requires adequate updated plans for community noise survey and ordinance.

Download Full-text

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

International Journal of Business Data Communications and Networking ◽

10.4018/ijbdcn.2019070106 ◽

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

Download Full-text

Methods for Noise Event Detection and Assessment of the Sonic Environment by the Harmonica Index

Applied Sciences ◽

10.3390/app11178031 ◽

2021 ◽

Vol 11 (17) ◽

pp. 8031

Author(s):

Rosa Ma Alsina-Pagès ◽

Roberto Benocci ◽

Giovanni Brambilla ◽

Giovanni Zambon

Keyword(s):

Event Detection ◽

Noise Level ◽

Urban Areas ◽

Road Traffic ◽

Time History ◽

Sound Level ◽

Road Traffic Noise ◽

Noise Levels ◽

Start Time ◽

Sonic Environment

Noise annoyance depends not only on sound energy, but also on other features, such as those in its spectrum (e.g., low frequency and/or tonal components), and, over time, amplitude fluctuations, such as those observed in road, rail, or aircraft noise passages. The larger these fluctuations, the more annoying a sound is generally perceived. Many algorithms have been implemented to quantify these fluctuations and identify noise events, either by looking at transients in the sound level time history, such as exceedances above a fixed or time adaptive threshold, or focusing on the hearing perception process of such events. In this paper, four criteria to detect sound were applied to the acoustic monitoring data collected in two urban areas, namely Andorra la Vella, Principality of Andorra, and Milan, Italy. At each site, the 1 s A-weighted short LAeq,1s time history, 10 min long, was available for each hour from 8:00 a.m. to 7:00 p.m. The resulting 92-time histories cover a reasonable range of urban environmental noise time patterns. The considered criteria to detect noise events are based on: (i) noise levels exceeding by +3 dB the continuous equivalent level LAeqT referred to the measurement time (T), criteria used in the definition of the Intermittency Ratio (IR) to detect noise events; (ii) noise levels exceeding by +3 dB the running continuous equivalent noise level; (iii) noise levels exceeding by +10 dB the 50th noise level percentile; (iv) progressive positive increments of noise levels greater than 10 dB from the event start time. Algorithms (iii) and (iv) appear suitable for notice-event detection; that is, those that (for their features) are clearly perceived and potentially annoy exposed people. The noise events detected by the above four algorithms were also evaluated by the available anomalous noise event detection (ANED) procedure to classify them as produced by road traffic noise or something else. Moreover, the assessment of the sonic environment by the Harmonica index was correlated with the single event level (SEL) of each event detected by the four algorithms. The threshold value of 8 for the Harmonica index, separating the “noisy” from the “very noisy” environments, corresponds to lower SEL levels for notice-events as identified by (iii) and (iv) algorithms (about 88–89 dB(A)) against those identified by (i) and (ii) criteria (92 dB(A)).

Download Full-text