Subhadipto Poddar Source Confirmed

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

Researcher

University of Arkansas at Fayetteville

faculty

6 h-index 20 pubs 213 cited

Research Areas

Advanced Neural Network Applications Urban Transport and Accessibility Traffic Safety and Performance Analysis Data-Driven Infrastructure Monitoring Computer Vision for Environmental Surveillance SARS-CoV-2 and COVID-19 Research Technology Adoption and User Behaviour

Links

ORCID

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OverviewAI-generated summary

Subhadipto Poddar's research focuses on the application of data-driven methods and advanced computational techniques to address complex real-world problems, particularly in transportation and infrastructure monitoring. His work has explored the use of deep learning for object detection in unmanned aerial systems for construction stormwater practice inspections and the analysis of traffic camera data for real-time barge detection on inland waterways.

Poddar has investigated methods to improve traffic intersection safety and performance through video analytics, including data-driven approaches for congestion identification and classification. His research has also examined the impact of events like COVID-19 on traffic signal systems and pedestrian activity, and has involved the development of modern intersection data analytics systems for pedestrian and vehicular safety. He has also contributed to the evaluation of arterial performance using probe-based data and the assessment of specific corridor operations.

His scholarly output includes 20 publications, accumulating 213 citations, and an h-index of 6. He has collaborated with researchers such as Sarah Hernandez and Maria Falquez at the University of Arkansas at Fayetteville.