Automatic generation of fine-grained traffic load spectrum via fusion of weigh-in-motion and vehicle spatial–temporal information
Accurate measurement of traffic loads is critical in bridge assessments, but current methods are not sufficient for refined analysis of bridge structures. The authors propose a data fusion method to generate fine-grained traffic load spectra using weigh-in-motion data, vehicle spatial-temporal data, and data on passing vehicles. The method is tested on an interchange viaduct in Shaanxi, China. The average biases of the longitudinal and transverse locations of moving vehicles, identified using the method, are 1.31 and 0.14 meters, respectively. The accuracy in these directions improved by 19% and 56%, respectively, compared a video identification method based on deep learning. The accuracy of the axle number identification is nearly 100%. Also, an automatically generated, very accurate, fine-grained traffic load spectrum is shown. The method can be applied in other scenarios for analysis and prediction of bridge performance under traffic loads.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/10939687
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Authors:
- Yang, Gan
- Wang, Ping
- Han, WanShui
- Chen, Shizhi
- Zhang, Shuying
- Yuan, Yangguang
- Publication Date: 2022
Language
- English
Media Info
- Media Type: Web
- Pagination: pp 485-499
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Serial:
- Computer-Aided Civil and Infrastructure Engineering
- Volume: 37
- Issue Number: 4
- Publisher: Blackwell Publishing
- ISSN: 1093-9687
- Serial URL: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-8667
Subject/Index Terms
- TRT Terms: Data fusion; Interchanges; Traffic data; Traffic loads; Viaducts; Weigh in motion
- Geographic Terms: Shaanxi (China)
- Subject Areas: Bridges and other structures; Data and Information Technology; Highways; Maintenance and Preservation;
Filing Info
- Accession Number: 01843681
- Record Type: Publication
- Files: TRIS
- Created Date: Apr 25 2022 10:07AM