High-Precision Dynamic Traffic Noise Mapping Based on Road Surveillance Video
High-precision dynamic traffic noise maps can describe the spatial and temporal distributions of noise and are necessary for actual noise prevention. Existing monitoring point-based methods suffer from limited spatial adaptability, and prediction model-based methods are limited by the requirements f...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-08-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/11/8/441 |
| _version_ | 1797409906095030272 |
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| author | Yanjie Sun Mingguang Wu Xiaoyan Liu Liangchen Zhou |
| author_facet | Yanjie Sun Mingguang Wu Xiaoyan Liu Liangchen Zhou |
| author_sort | Yanjie Sun |
| collection | DOAJ |
| description | High-precision dynamic traffic noise maps can describe the spatial and temporal distributions of noise and are necessary for actual noise prevention. Existing monitoring point-based methods suffer from limited spatial adaptability, and prediction model-based methods are limited by the requirements for traffic and environmental parameter specifications. Road surveillance video data are effective for computing and analyzing dynamic traffic-related factors, such as traffic flow, vehicle speed and vehicle type, and environmental factors, such as road material, weather and vegetation. Here, we propose a road surveillance video-based method for high-precision dynamic traffic noise mapping. First, it identifies dynamic traffic elements and environmental elements from videos. Then, elements are converted from image coordinates to geographic coordinates by video calibration. Finally, we formalize a dynamic noise mapping model at the lane level. In an actual case analysis, the average error is 1.53 dBA. As surveillance video already has a high coverage rate in most cities, this method can be deployed to entire cities if needed. |
| first_indexed | 2024-03-09T04:22:20Z |
| format | Article |
| id | doaj.art-c52ff8e5e547401e96f6130c31697836 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-09T04:22:20Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-c52ff8e5e547401e96f6130c316978362023-12-03T13:46:27ZengMDPI AGISPRS International Journal of Geo-Information2220-99642022-08-0111844110.3390/ijgi11080441High-Precision Dynamic Traffic Noise Mapping Based on Road Surveillance VideoYanjie Sun0Mingguang Wu1Xiaoyan Liu2Liangchen Zhou3Key Laboratory of Virtual Geographic Environment of Ministry of Education, Nanjing Normal University, Nanjing 210023, ChinaKey Laboratory of Virtual Geographic Environment of Ministry of Education, Nanjing Normal University, Nanjing 210023, ChinaKey Laboratory of Virtual Geographic Environment of Ministry of Education, Nanjing Normal University, Nanjing 210023, ChinaKey Laboratory of Virtual Geographic Environment of Ministry of Education, Nanjing Normal University, Nanjing 210023, ChinaHigh-precision dynamic traffic noise maps can describe the spatial and temporal distributions of noise and are necessary for actual noise prevention. Existing monitoring point-based methods suffer from limited spatial adaptability, and prediction model-based methods are limited by the requirements for traffic and environmental parameter specifications. Road surveillance video data are effective for computing and analyzing dynamic traffic-related factors, such as traffic flow, vehicle speed and vehicle type, and environmental factors, such as road material, weather and vegetation. Here, we propose a road surveillance video-based method for high-precision dynamic traffic noise mapping. First, it identifies dynamic traffic elements and environmental elements from videos. Then, elements are converted from image coordinates to geographic coordinates by video calibration. Finally, we formalize a dynamic noise mapping model at the lane level. In an actual case analysis, the average error is 1.53 dBA. As surveillance video already has a high coverage rate in most cities, this method can be deployed to entire cities if needed.https://www.mdpi.com/2220-9964/11/8/441traffic noisedynamic noise maproad surveillance videonoise simulation |
| spellingShingle | Yanjie Sun Mingguang Wu Xiaoyan Liu Liangchen Zhou High-Precision Dynamic Traffic Noise Mapping Based on Road Surveillance Video ISPRS International Journal of Geo-Information traffic noise dynamic noise map road surveillance video noise simulation |
| title | High-Precision Dynamic Traffic Noise Mapping Based on Road Surveillance Video |
| title_full | High-Precision Dynamic Traffic Noise Mapping Based on Road Surveillance Video |
| title_fullStr | High-Precision Dynamic Traffic Noise Mapping Based on Road Surveillance Video |
| title_full_unstemmed | High-Precision Dynamic Traffic Noise Mapping Based on Road Surveillance Video |
| title_short | High-Precision Dynamic Traffic Noise Mapping Based on Road Surveillance Video |
| title_sort | high precision dynamic traffic noise mapping based on road surveillance video |
| topic | traffic noise dynamic noise map road surveillance video noise simulation |
| url | https://www.mdpi.com/2220-9964/11/8/441 |
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