Traffic noise assessment based on mobile measurements

Quintero, G.; Balastegui, A.; Garbí, Jordi Romeu

doi:10.1016/j.eiar.2020.106488

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…Sensors have been placed in vehicles, bicycles, or backpacks of pedestrians for opportunistic surveillance. Meteorological data and built environment data have been collected from mobile monitoring sensors and stationary sites to expand the spatial extent and temporal resolution [20,[36][37][38]. Overall, there are various innovative approaches and technologies for monitoring urban environments efficiently and effectively.…”

Section: System Requirements and Designmentioning

confidence: 99%

Portable Arduino-Based Multi-Sensor Device (SBEDAD): Measuring the Built Environment in Street Cycling Spaces

Luo,

Hui,

Shang

et al. 2024

Sensors

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The built environment’s impact on human activities has been a hot issue in urban research. Compared to motorized spaces, the built environment of pedestrian and cycling street spaces dramatically influences people’s travel experience and travel mode choice. The streets’ built environment data play a vital role in urban design and management. However, the multi-source, heterogeneous, and massive data acquisition methods and tools for the built environment have become obstacles for urban design and management. To better realize the data acquisition and for deeper understanding of the urban built environment, this study develops a new portable, low-cost Arduino-based multi-sensor array integrated into a single portable unit for built environment measurements of street cycling spaces. The system consists of five sensors and an Arduino Mega board, aimed at measuring the characteristics of the street cycling space. It takes air quality, human sensation, road quality, and greenery as the detection objects. An integrated particulate matter laser sensor, a light intensity sensor, a temperature and humidity sensor, noise sensors, and an 8K panoramic camera are used for multi-source data acquisition in the street. The device has a mobile power supply display and a secure digital card to improve its portability. The study took Beijing as a sample case. A total of 127.97 G of video data and 4794 Kb of txt records were acquired in 36 working hours using the street built environment data acquisition device. The efficiency rose to 8474.21% compared to last year. As an alternative to conventional hardware used for this similar purpose, the device avoids the need to carry multiple types and models of sensing devices, making it possible to target multi-sensor data-based street built environment research. Second, the device’s power and storage capabilities make it portable, independent, and scalable, accelerating self-motivated development. Third, it dramatically reduces the cost. The device provides a methodological and technological basis for conceptualizing new research scenarios and potential applications.

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Section: System Requirements and Designmentioning

confidence: 99%

Portable Arduino-Based Multi-Sensor Device (SBEDAD): Measuring the Built Environment in Street Cycling Spaces

Luo,

Hui,

Shang

et al. 2024

Sensors

View full text Add to dashboard Cite

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“…Many of these proposals are aligned with the prospective of smart city, where ubiquitous IoT devices are extensively deployed to collect, relay, and analyze data from various sources. The literature is rich of noise monitoring system proposals that consider multiple arrangements of fixed stations, WSN, and MCS that can work isolatedly or collaboratively [14][15][16][17][18][19][20] Bello et al [21] presented an urban noise monitoring system that adopts WSN for collecting sound measurements from stationary low-cost sensor terminals, and applies data analytics and machine learning (ML) techniques to identify sound source and visualize noise maps. Similarly, Fernandez-Prieto et al [22] designed and implemented a city-wide, longterm WSN-based noise monitoring system that connects and reports data to a private cloud.…”

Section: Related Workmentioning

confidence: 99%

“…They reported that the mobile measurements with spatial interpolation is efficient even using few and short samples. Quintero et al [20,37] suggested through field experiments a 34m radius for mobile samples aggregation to minimize estimation error.…”

Section: Related Workmentioning

confidence: 99%

Efficient Mobile Crowdsourcing for Environmental Noise Monitoring

Alashaikh

Al-Hazemi

2022

IEEE Access

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Governments and commissions all around the globe have worked on developing policies and rules as well as actions and measures for continuous assessment and remedies for environmental noise pollution. All of which requires continuous and accurate measurements of noise levels. The advent of smart city and IoT-based monitoring makes it possible for different technologies to cooperate in collecting and reporting environmental noise levels for longer duration and wider geographical region. Among others, mobile crowdsourcing (MCS) appears to be a promising technique for environmental noise monitoring with minimal upfront cost and almost no recurrent cost for regulators. This is due to the assumption of voluntary participation from mobile device owners who may be reluctant to participate if the recurrent cost of mobile resource consumption is high. In this work, an approach to improve mobile device resources efficiency for mobile crowdsourcing application in environmental noise monitoring is proposed. That is, noise samples from a device are collected only if it is significant to the accuracy of the measurements. Also this paper defines and mathematically formalizes the problem under study and develops two algorithms to optimize both resource and measurements efficiency. The results demonstrate the efficiency of the proposed solution. INDEX TERMS Environmental Noise monitoring, Mobile crowdsourcing (MCS), measurement samplingThis article has been accepted for publication in IEEE Access.

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How to enhance urban noise management: Exploring the influencing factors of noise complaints at multiple scales integrating citizen perception

Jiao,

Wang,

et al. 2025

Environmental Impact Assessment Review

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Traffic noise assessment based on mobile measurements

Cited by 6 publications

References 19 publications

Portable Arduino-Based Multi-Sensor Device (SBEDAD): Measuring the Built Environment in Street Cycling Spaces

Portable Arduino-Based Multi-Sensor Device (SBEDAD): Measuring the Built Environment in Street Cycling Spaces

Efficient Mobile Crowdsourcing for Environmental Noise Monitoring

How to enhance urban noise management: Exploring the influencing factors of noise complaints at multiple scales integrating citizen perception

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