An Artificial Intelligence–Driven Approach for Real-Time Detection of Traffic-Sign Deficiencies
Published by
Transportation Research Record: Journal of the Transportation Research Board
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Mohamad Karasneh is a dedicated and driven professional with a strong academic background and expertise in the field of transportation engineering. Currently, he is a master's student at the University of Cincinnati, and a Graduate Research Assistant at the Center for Smart, Sustainable, and Resilient Infrastructure (CSSRI). With a keen interest in emerging technologies, Mohamad specialized in developing a real-time detection and evaluation framework driven by artificial intelligence (AI) for transportation infrastructure. His research involved leveraging cutting-edge tools and advanced software to integrate sensor data into traffic signs evaluation and other infrastructure assets, enabling proactive and data-driven decision-making. Throughout his academic journey, Mohamad has demonstrated proficiency in computer skills and research methodologies. His research experience spans various areas, including traffic modeling and simulation, asset management, autonomous vehicles (AVs) sensors, and machine learning. He is also interested in getting involved in more areas of transportation engineering such as: CAVs, Automated vehicle driving and testing, & Traffic control systems. Learn more about the CSSRI at: https://ceas.uc.edu/research/centers-labs/center-for-smart-sustainable-and-resilient-infrastructure.html
Graduate Research Assisstant
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Graduate Research Assisstant
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Ph.D. in Civil Engineering
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Master of Science in Civil Engineering
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Bachelor degree of Science in Engineering Technology - Civil Engineering
Published by
Transportation Research Record: Journal of the Transportation Research Board
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Published by
Buildings
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Published by
Buildings
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Published by
Innovative Infrastructure Solutions
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