Kwadwo Amankwah-Nkyi
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Researcher
Also affiliated: Jacobs (United States) (2025)
Unknown Researcher
Research Areas
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Biography and Research Information
OverviewAI-generated summary
Kwadwo Amankwah-Nkyi's research focuses on the application of machine learning and data-driven methods to improve transportation systems. His work includes developing real-time systems for detecting helmet violations and barges on inland waterways, utilizing deep learning models like YOLOv5. Amankwah-Nkyi also investigates methods for assessing the resilience of transportation networks, such as the Arkansas roadway system, and estimates the criticality of highway transportation assets through analytical hierarchy processes incorporating stakeholder input. He has explored safety-aware reinforcement learning for adaptive traffic signal optimization in work zone environments. His publications also touch upon the use of driving simulators for educational outreach in freight transportation. Amankwah-Nkyi collaborates with several researchers at the University of Arkansas at Fayetteville, including Sarah Hernandez, Suman Mitra, Subhadipto Poddar, and Maria Falquez, with whom he has co-authored multiple 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
- Real-Time Barge Detection Using Traffic Cameras and Deep Learning on Inland Waterways
- Highway-Transportation-Asset Criticality Estimation Leveraging Stakeholder Input Through an Analytical Hierarchy Process (AHP)
- Board 37A: Driving Simulators as Educational Outreach for Freight Transportation
- Board 37A: Driving Simulators as Educational Outreach for Freight Transportation
- 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
- 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)
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