High-Precision Dynamic Traffic Noise Mapping Based on Road Surveillance Video
Bibliographic Data
| ID | 22033792 |
|---|---|
| Authors | Yanjie Sun (0000-0002-7099-5133, Nanjing Normal University), Mingguang Wu (0000-0001-6086-4503, Nanjing Normal University, corresponding author), Xiaoyan Liu (0000-0002-8276-907X, Nanjing Normal University), Liangchen Zhou (0000-0002-5183-9610, Nanjing Normal University) |
| Year | 2022 |
| Volume | 11 |
| Issue | 8 |
| Pages | 441 |
| Publication date | 2022-08-04 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi11080441 |
| OpenAlex | W4289856740 |
| Language | EN |
| References cited | 42 |
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
Adaptability · Computer network · Noise reduction · Real-time computing · Traffic noise · Automated Road and Building Extraction · Computer Science · Noise Effects and Management · Video Surveillance and Tracking Methods · Artificial Intelligence
| Highly cited | No |
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