Kwadwo Amankwah-Nkyi Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
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Biography and Research Information
OverviewAI-generated summary
Kwadwo Amankwah-Nkyi's research focuses on the application of data-driven methods and machine learning to address challenges in transportation systems and infrastructure. His work includes developing real-time systems for detecting violations such as helmet use and identifying traffic elements like barges on inland waterways using deep learning techniques. Amankwah-Nkyi also investigates methods for assessing the resilience and criticality of transportation assets, incorporating stakeholder input through analytical processes. He has published on topics including real-time helmet violation detection, data-driven resilience assessment of roadway networks, and the use of traffic cameras with machine learning for barge detection. His research network includes collaborators such as Sarah Hernandez, Suman Mitra, and Maria Falquez from the University of Arkansas at Fayetteville, with whom he has co-authored multiple publications. His scholarly output is reflected in an h-index of 2 and a total of 8 publications.
Metrics
- h-index: 2
- Publications: 8
- Citations: 14
Selected Publications
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Highway-Transportation-Asset Criticality Estimation Leveraging Stakeholder Input Through an Analytical Hierarchy Process (AHP) (2025)
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Data-Driven Methods to Assess Transportation System Resilience: Case Study of the Arkansas Roadway Network (2024)
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Real-Time Barge Detection Using Traffic Cameras and Deep Learning on Inland Waterways (2024)
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Board 37A: Driving Simulators as Educational Outreach for Freight Transportation (2024)
Collaboration Network
Top Collaborators
- Data-Driven Methods to Assess Transportation System Resilience: Case Study of the Arkansas Roadway Network
- Highway Transportation Asset Criticality Estimation Leveraging Stakeholder Input through an Analytical Hierarchy Process (AHP)
- Highway-Transportation-Asset Criticality Estimation Leveraging Stakeholder Input Through an Analytical Hierarchy Process (AHP)
- Real-Time Barge Detection Using Traffic Cameras and Deep Learning on Inland Waterways
- Board 37A: Driving Simulators as Educational Outreach for Freight Transportation
Showing 5 of 6 shared publications
- Real-Time Helmet Violation Detection Using YOLOv5 and Ensemble Learning
- Real-Time Barge Detection Using Traffic Cameras and Deep Learning on Inland Waterways
- Traffic Cameras to detect inland waterway barge traffic: An Application of machine learning
- Highway Transportation Asset Criticality Estimation Leveraging Stakeholder Input through an Analytical Hierarchy Process (AHP)
- Highway-Transportation-Asset Criticality Estimation Leveraging Stakeholder Input Through an Analytical Hierarchy Process (AHP)
- Highway Transportation Asset Criticality Estimation Leveraging Stakeholder Input through an Analytical Hierarchy Process (AHP)
- Real-Time Barge Detection Using Traffic Cameras and Deep Learning on Inland Waterways
- Traffic Cameras to detect inland waterway barge traffic: An Application of machine learning
- Real-Time Helmet Violation Detection Using YOLOv5 and Ensemble Learning
- Real-Time Helmet Violation Detection Using YOLOv5 and Ensemble Learning
- Real-Time Helmet Violation Detection Using YOLOv5 and Ensemble Learning
- Real-Time Helmet Violation Detection Using YOLOv5 and Ensemble Learning
- Real-Time Helmet Violation Detection Using YOLOv5 and Ensemble Learning
- Traffic Cameras to detect inland waterway barge traffic: An Application of machine learning
- Traffic Cameras to detect inland waterway barge traffic: An Application of machine learning
- Board 37A: Driving Simulators as Educational Outreach for Freight Transportation
- Real-Time Barge Detection Using Traffic Cameras and Deep Learning on Inland Waterways
- Data-Driven Methods to Assess Transportation System Resilience: Case Study of the Arkansas Roadway Network
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