Traffic noise mapping of Indian roads through smartphone user community participation

Kumar, Rajiv; Mukherjee, Abhijit; Singh, Vishal

doi:10.1007/s10661-016-5741-1

Cited by 18 publications

(5 citation statements)

References 12 publications

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“…The amount of noise is typically expressed as an equivalent continuous sound level (Leq) and is referred to using the decibel (dB) measurement system. Leq, which stands for "equivalent continuous sound level" is a valuable tool for describing varying noises as a single number (Kumar et al, 2017). Several distinct kinds of sound level metres (SLM) are utilised for noise level monitoring.…”

Section: Noise Pollution Monitoring and Mappingmentioning

confidence: 99%

A Review on Noise Pollution Monitoring, Mapping, Modelling, and Health Impacts

Kumar,

Ahirwar,

Prasad

2023

J ENVIRON INFORM LET

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Most people are not aware that noise from road traffic is the principal cause of annoyance to exposed people. In relation to this, the review aims at providing an overview of research carried out on noise pollution monitoring, mapping, modelling, and health impacts in recent years in different countries. The electronic databases were searched to select the studies related to noise monitoring, mapping, modelling, and health impacts on humans. A total of 91 most relevant studies were reviewed in this article. A comparison of different instruments available for noise monitoring was done. Studies related to monitoring and mapping reveal that most of the locations have higher noise levels in the environment, breaching the noise limits. Only a few articles enumerated health impacts and zone-specific monitoring. Many researchers have conducted a questionnaire-based study related to health to identify the impact of noise. Noise models for many developing countries are not available, so their development and modification are still in progress. Proper zone-specific studies and noise maps will help the town planner with better land use planning. Continuous monitoring stations for noise in crowded cities shall be implemented to mitigate noise pollution. Based on the review, it is concluded that long-term research on noise pollution is very much needed worldwide.

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Section: Noise Pollution Monitoring and Mappingmentioning

confidence: 99%

A Review on Noise Pollution Monitoring, Mapping, Modelling, and Health Impacts

Kumar,

Ahirwar,

Prasad

2023

J ENVIRON INFORM LET

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“…The findings of the research were analyzed based on the Likert scale and single-factor ANOVA. The Likert scale was used to evaluate the attitude of the response of respondents on the degree of strongly agree to strongly disagree on a particular statement, opinions, or observations (31). The mean, standard deviation, percentages, cumulative mean, and one-way ANOVA were evaluated using MS Excel (2019 version) and SPSS version 26.…”

Section: Design and Administration Of Questionnairesmentioning

confidence: 99%

Public perception on environmental noise pollution: A case study in Zaria city, Kaduna state, Nigeria

Abraham

Sunday

Sani

et al. 2022

Environ Health Eng Manag

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Background: One of the key indicators of the degradation of the environment is the noise level. This has necessitated this study on the evaluation of the public, perceptional awareness, sources, effects, and mitigation measures on environmental noise pollution. Methods: The population was estimated and 385 structured questionnaires were estimated and administered by random purposive sampling. About 358 questionnaires were retrieved. Data were analyzed using SPSS and Excel statistical software. Results: About 90.2% of the respondents had relevant awareness and its effects on environmental noise while 9.8% of the respondent did not. Traffic, generators, commercial and light industry sources of noise, and their severity were ranked in a descending order using the Likert scale. Hearing impairment, annoyance, stress, distraction during exposure were ranked in a descending order using the Likert scale. Single-factor ANOVA on the sources of noise and their severity, awareness of the various effects of noise, and responses during exposure showed that there were significant differences as P<0.05 using a confidence level of 95%. About 61.7% of respondents complained of environmental noise, 72.6% respondents received complaints about environmental noise, 87.7% of respondents were not aware of any government agency monitoring noise pollution, 72.2% of the respondents had done nothing regarding noise prevention, and 91.1% respondents wanted a proactive decision in mitigating environmental noise pollution. Conclusion: There is an inadequate coping strategy. Strategic planning in mitigating environmental noise in urban and semi-urban areas is a necessity and there is a need for public enlightenment by government monitoring agencies.

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“…Authors in [1] used a standard mobile device for detecting the quality and irregularities on the road. Traffic noise mapping using smartphones had been reported in [9] where the authors had used a community sensing framework for predicting the noisy areas in different regions. Researchers in [15] proposed and implemented a system based on MCS paradigm to monitor noise without using specialized sensors.…”

Section: Related Workmentioning

confidence: 99%

Street Light Monitoring using Smartphones

Kumar*

2019

IJEAT

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Rapid urbanization poses challenge to the maintenance agencies in monitoring and maintaining city-wide services. Traditional ways, to monitor the services, involve huge manpower and time. Technical revolutions opens up smarter ways of monitoring the civic services with the use of miniature sensors embedded in smart devices and potentially covers wider area. Over the past decade, the smart city concept has emerged as a potential research area where many devices work in a network and has been applied as city-wide smart monitoring technique. In this paper, author has highlighted the use of mobile crowd sensing though smart phones to devise civic infrastructural monitoring application specifically for monitoring street lights conditions and generating heat maps accordingly. A client-server based mobile crowd sourcing framework has been proposed where the client represents smart phones equipped with devised phone application to crowd sense data and the open-source server is used for data analysis and distribution of results. The framework works in sequential manner with three key modules i) data harvesting, ii) analyze and iii) visual reporting module. Smartphones are used to harvest city-wide contextual data and transfer it to the server. Effective measures to protect privacy of the user have been applied during data harvesting. Server analyzes the harvested data and retrieves useful metrics as computed luminous index which are communicated back to clients (phones) as heat maps visualized on Google® maps. The proposed mobile crowd sourcing framework helps in quick data sensing and spotting the poor street light conditions. The resulted maps potentially disseminate the information to the city residents and the administration to respond accordingly. Whether there is need to install more street lamp posts or to repair the malfunctioning lamp post. A better lighting condition in the streets enhances the visibility and thus the safety of the residents, as the dark areas are prone to accidents or promotes crime.

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Traffic noise mapping of Indian roads through smartphone user community participation

Cited by 18 publications

References 12 publications

A Review on Noise Pollution Monitoring, Mapping, Modelling, and Health Impacts

A Review on Noise Pollution Monitoring, Mapping, Modelling, and Health Impacts

Public perception on environmental noise pollution: A case study in Zaria city, Kaduna state, Nigeria

Street Light Monitoring using Smartphones

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