Jiaxun, S;
Qi, M;
Mary, N;
Jian, K;
(2023)
A traffic big data analysis on relationships between urban planning and traffic noise level--taking Dongguan Demonstration Area, China as an example.
In:
Proceedings of the 51st International Congress and Exposition on Noise Control Engineering.
(pp. pp. 784-794).
Institute of Noise Control Engineering (INCE)
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Abstract
With the promotion of smart city research, traffic big data has become a new way to study urban traffic noise. Taking Dongguan Demonstration Area, China as an example, this research discussed the relationships between traffic noise levels and urban plannings using Geographic information system (GIS), global positioning system (GPS) techniques and OpenITS Organization OpenData. The results showed that, for the whole area, some planning factors, say global integration, local integration (R=500m), global betweenness, local betweenness (R=500m) and number of points of interest (POIs) had significant positive correlations with the daytime traffic noise levels. Among them, the number of POIs had the strongest correlation with the traffic noise levels (r=0.560 p<0.01). However, the degree of influence of each variable on traffic noise levels can be changed with geographical locations. This research also identified specific areas where traffic noise levels were negatively corβelated with local integration, which had great potential to provide a recreational and peaceful place for people to walk. Therefore, traffic noise can be effectively controlled by making changes in urban planning.
Type: | Proceedings paper |
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Title: | A traffic big data analysis on relationships between urban planning and traffic noise level--taking Dongguan Demonstration Area, China as an example |
Event: | InterNoise22 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.3397/IN_2022_0110 |
Publisher version: | http://dx.doi.org/10.3397/in_2022_0110 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources |
URI: | https://discovery.ucl.ac.uk/id/eprint/10196105 |
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