A comparative study of noise levels in some residential, industrial and commercial areas of Delhi

Singh, Braj Bihari; Jain, V. K.

doi:10.1007/bf02396406

Cited by 15 publications

(9 citation statements)

References 16 publications

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“…Singh et. al report their findings on noise levels of three types of city regions: residential, industrial, and commercial [22]. As social media has been being widely adopted in people's daily lives, social media data becomes an important data source for related research.…”

Section: Urban Region Divergencesmentioning

confidence: 99%

Mining Shopping Patterns for Divergent Urban Regions by Incorporating Mobility Data

Song

Wang

et al. 2016

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

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What people buy is an important aspect or view of lifestyles. Studying people's shopping patterns in different urban regions can not only provide valuable information for various commercial opportunities, but also enable a better understanding about urban infrastructure and urban lifestyle. In this paper, we aim to predict citywide shopping patterns. This is a challenging task due to the sparsity of the available data -over 60% of the city regions are unknown for their shopping records. To address this problem, we incorporate another important view of human lifestyles, namely mobility patterns. With information on "where people go", we infer "what people buy". Moreover, to model the relations between regions, we exploit spatial interactions in our method. To that end, Collective Matrix Factorization (CMF) with an interaction regularization model is applied to fuse the data from multiple views or sources. Our experimental results have shown that our model outperforms the baseline methods on two standard metrics. Our prediction results on multiple shopping patterns reveal the divergent demands in different urban regions, and thus reflect key functional characteristics of a city. Furthermore, we are able to extract the connection between the two views of lifestyles, and achieve a better or novel understanding of urban lifestyles.

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Section: Urban Region Divergencesmentioning

confidence: 99%

Mining Shopping Patterns for Divergent Urban Regions by Incorporating Mobility Data

Song

Wang

et al. 2016

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

View full text Add to dashboard Cite

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Section: Introductionmentioning

confidence: 99%

“…followed by industrial areas, and then residential areas had the lowest levels of noise (Singh and Jain, 1995). In India, only a limited amount of research has been done comparing noise monitoring in residential and commercial areas (Singh and Jain, 1995;Tripathi et al, 2006;Tyagi et al, 2006). In a different piece of research, the authors described the National Ambient Noise level (NANL) in India's most important cities (Garg et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Comparative assessment of road traffic noise through 2D noise mapping: A case study of an urban area

et al. 2022

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Noise pollution has risen to a worrying degree in all zones of the city (residential, commercial, industrial, and silent) because of the rapid rise in urbanization, industrialization, and other connectivity of transport systems in all zones of the city. It has always been challenging to identify noise hotspots where immediate remedial measures are required. In addition to providing the propagation of noise in the X and Z direction, 2D noise mapping is an essential method for identifying regions where noise levels may reach a hazardous level. Thus, the comparative examination of all residential areas inside a city provides a clear picture of noise exposure. The main focus of this study is to carry out comparative analysis of noise exposure level of selected locations such as residential commercial, industrial and silent area of Delhi city using 2D noise maps. For noise monitoring, Sound Level Meter (SLM) Larson & Davis 831 in compliance with the standard procedure of CPCB is used for monitoring peak traffic hours of working day and night. After that, using sound PLAN (acoustic) and MapInfo Pro, 2-D (Desktop GIS) noise maps were developed, visualized, and analyzed. According to the findings, residential areas have been recorded as having the highest levels of noise, followed by commercial and silent zones, and industrial areas have been seen to have the lowest levels of noise. The high noise level in residential area may be due to heavy traffic volume and road surrounded by high rise buildings. Whereas minimum noise has been observed in industrial area because the industries along the major roads have been surrounded with parapets and trees. As a result, only a small amount of industrial noise entered major highways/roads. Additionally, 2D noise map revealed that the surrounding structures of road (high rise buildings, brick walls, grilled boundaries, trees etc.) significantly influence propagation of noise in all directions. These maps may also be utilized by decision makers in the process of formulating noise control strategies or implementing corrective measures.

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“…The growing awareness of community towards noise pollution has led to serious concerns and implementation of strict measures for mitigation of noise as well as systematic planning beforehand for new projects. Long-term noise monitoring has been reported in only a few studies in India due to lack of infrastructural support [17][18][19][20][21][22] . CPCB initiatives in this regard in setting up of noise monitoring stations in various parts of the country are an effective approach for analyzing the ambient noise levels and planning for suitable measures for .…”

Section: Introductionmentioning

confidence: 99%

Applications of AutoRegressive Integrated Moving Average (ARIMA) approach in time-series prediction of traffic noise pollution

Garg¹,

Soni²,

Saxena³

et al. 2015

noise cont engng j

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The paper analyzes the long-term noise monitoring data using the AutoRegressive Integrated Moving Average (ARIMA) modeling technique. Box-Jenkins ARIMA approach has been adapted to simulate the daily mean L Day (06-22 h) and L Night (22-06 h) in A-and C-weightings in conjunction with single-noise metrics, daynight average sound level (DNL) for a period of 6 months. The autocorrelation function (ACF) and partial autocorrelation function (PACF) have been obtained to find suitable orders of autoregressive (p) and moving average (q) parameters for ARIMA (p, d, q) models so developed for all the single-noise metrics. The ARIMA models, namely, ARIMA(0,0,14), ARIMA(0,1,1), ARIMA(7,0,0), ARIMA(1,0,0) and ARIMA(0,1,14), have been developed as the most suitable for simulating and forecasting the daily mean L Day dBA, L Night dBA, L Day dBC, L Night dBC, and day-night average sound level (DNL) respectively. The performance of the model so developed is ascertained using the statistical tests. The work reveals that the ARIMA approach is reliable for time-series modeling of traffic noise levels. © 2015 Institute of Noise Control Engineering.

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A comparative study of noise levels in some residential, industrial and commercial areas of Delhi

Cited by 15 publications

References 16 publications

Mining Shopping Patterns for Divergent Urban Regions by Incorporating Mobility Data

Mining Shopping Patterns for Divergent Urban Regions by Incorporating Mobility Data

Comparative assessment of road traffic noise through 2D noise mapping: A case study of an urban area

Applications of AutoRegressive Integrated Moving Average (ARIMA) approach in time-series prediction of traffic noise pollution

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