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14.05 Noise Mapping

Route survey research of US military aircraft at Futenma Air Base and Kadena Air Base in Okinawa Prefecture
Time: 6:40 pm

Author: 武 渡嘉敷

Abstract ID: 1616

Currently, about 186.09 km2 of US military bases are located in Okinawa Prefecture, accounting for about 10.4% of the prefecture's land area, and about 70% of US military bases nationwide are concentrated. Many of the US military bases are located in or near urban areas, and have an impact on the city planning of related municipalities. Among them, the aircraft noise problem is serious, and noise exceeding 100 dB (value observed in the residential area at the measurement point) such as takeoff and landing noise and engine adjustment noise is generated on a daily basis, which greatly deteriorates the living environment of the local residents. It is a factor. In response to this, the national government has taken measures such as soundproofing work. In this study, the subjects were Kadena Air Base and Futenma Air Base, and continuous measurements were made around the bases to investigate the surrounding sound environment.

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Exploiting data from the NoiseCapture application for environmental noise measurements with a smartphone
Time: 11:20 am

Author: Juddicaël Picaut

Abstract ID: 2316

NoiseCapture is a smartphone application initially developed as part of a participative approach for environmental noise mapping. After more than 3 years, the database produced from all over the world contributions is considerable (more than 77k contributors, nearly 300k tracks representing about 72 million 1-second measurements, in nearly 200 countries). Beyond the initial objective, other uses of the application have emerged: individually by users for their own needs, by associations of people in charge of the fight against noise pollution, within the framework of educational activities, by researchers for the realization of their own research, by communities to address the subject of noise pollution. As these new applications emerged, the development team of NoiseCapture was led to extend the possibilities of exploitation of these data. Thus, in this paper, we present different possibilities for a user to perform his own data analysis, namely: a local export of data from the smartphone, access to raw data and pre-processed data from the NoiseCapture server, access to formatted GIS layers from OGC standard service. All these methods are enabled thanks to the open source ecosystem, such as Python libraries, R software suite and GIS tools.

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Acoustic Prediction Modeling and Sound Mapping of public transport users’ exposure at a bus stop.
Time: 6:00 pm

Author: Dayane Cristina Lima Estercio

Abstract ID: 2591

The objective of this research is to develop a mathematical model to predict the road traffic noise level at the bus stop, to assess the level of noise that users of these urban facilities are exposed daily. To help assess the exposure and the environmental impact, sound mapping will be performed using the IMMI software. In the model, the calculation of direct paths and specular reflections and diffuse was adopted. The study was applied in three sections in the city of Maringá, Brazil. At each point, the user was simulated standing and sitting. The sound source was positioned on the axis of each strip, every five meters. In total, 5124 readings of source positions were evaluated in 84 measured points. For the validation, the Anderson-Kurze, Kang, Yang and Zhang, Bistafa and Naish model were applied, and then the t-Student test were applied. The results showed a correspondence between the developed model, the data of the measurements and the reference models in the range of 25 Hz to 10000 Hz, there was a greater variance between the models applied in the high frequencies. It is concluded that the model was able to estimate the sound level of the stretches evaluated.

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A closer look at rail methodology in the BTS National Transportation Noise Map
Time: 6:20 pm


Abstract ID: 3069

In 2017, the Bureau of Transportation Statistics released the inaugural national, multi-modal transportation noise map prototype.  The noise modeling and mapping effort was envisioned as a way to facilitate the geographic tracking of national trends and provide insight into transportation noise-related questions as changes occur over time - changes between modes, types of vehicles within modes and the geographic shifts of populations.  How do changes in aircraft technology change the transportation noise landscape? Does increased high speed rail availability affect highway-related noise? How does a population shift away from urban centers affect the soundscape?  The inaugural model included aviation and highway sources. The first update, released in November 2020, includes passenger rail-related noise in addition to aviation and highway sources. Operations in this new mode include commuter rail mainline, high-speed electric, light rail, heavy rail and streetcars, along with commuter rail horns at highway-rail grade crossings. The data for this noise map were modeled based on USDOT methods, with adjustments and simplifications to model on a national scale.  This paper focuses on the modeling methods and geospatial approach used to develop the passenger rail noise data layer.

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Road traffic noise mapping based on aerial photographs – sound power level determination of road vehicles
Time: 7:40 pm

Author: Shinichi Sakamoto

Abstract ID: 3130

As the first step to obtain a city urban area noise map of road traffic noise, sound power levels of vehicles on the roads should be accurately estimated over a wide area. In Japan, ASJ RTN-Model 2018 was proposed as the representative road traffic noise prediction model, and by using the model sound power level of a vehicle can be determined if the vehicle type, traveling speed, and driving mode are known. As such data on urban road network, the Ministry of Land, Infrastructure and Transport of Japan publishes the road traffic census including road traffic volume and travel speed of major roads in Japan. The data, however, is limited to major roads and there is no data on minor roads. In this study, to estimate noise condition and situation on arbitrary road, a method for estimating the traveling speed and the traffic volume of vehicles on the road from aerial photographs was examined. Road traffic noise levels along several roads in Tokyo were analyzed by the proposed method and the validity of the calculation results were verified by comparing with short-time measurement results obtained along the target roads.

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