Geoffery Agorku
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Formerly Arkansas Graduate Research Assistant, University of Arkansas through 2027.
Research Areas
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Biography and Research Information
OverviewAI-generated summary
Geoffery Agorku's research focuses on the application of computer vision and machine learning techniques to address real-world transportation and logistics challenges. He has investigated the use of traffic cameras and deep learning for real-time barge detection on inland waterways, as well as for identifying helmet violations. His work also includes predicting barge tow size using vessel trajectory data and mapping major freight corridors across waterborne, rail, and truck networks.
Agorku's academic background includes a PhD in Civil Engineering. His research interests encompass computer vision, waterborne and multimodal freight, intelligent transportation systems, and machine learning. He has collaborated with researchers at the University of Arkansas at Fayetteville, including Kwadwo Amankwah-Nkyi, Sarah V. Hernandez, Subhadipto Poddar, and Hayley Hames, on multiple shared publications. His scholarship metrics include an h-index of 2 and 8 total publications with 12 citations.
Metrics
- h-index: 2
- Publications: 8
- Citations: 12
Positions
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Graduate Research Assistant 2024–2027University of Arkansas at Fayetteville Civil Engineering ORCID
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Research Civil Engineer Intern 2024United States Army Corps of Engineers Engineer Research and Development Center (ERDC) ORCID
Selected Publications
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Predicting barge tow size on inland waterways using vessel trajectory-derived features: proof of concept (2026)
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Real-Time Barge Detection Using Traffic Cameras and Deep Learning on Inland Waterways (2024)
Collaboration Network
Top Collaborators
- Real-Time Barge Detection Using Traffic Cameras and Deep Learning on Inland Waterways
- Real-Time Barge Detection Using Traffic Cameras and Deep Learning on Inland Waterways
- Real-Time Barge Detection Using Traffic Cameras and Deep Learning on Inland Waterways
- Real-Time Barge Detection Using Traffic Cameras and Deep Learning on Inland Waterways
- Predicting barge tow size on inland waterways using vessel trajectory-derived features: proof of concept
- Predicting barge tow size on inland waterways using vessel trajectory-derived features: proof of concept
- Predicting barge tow size on inland waterways using vessel trajectory-derived features: proof of concept
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