The REAT Center at FAMU-FSU, a member of the REAT Consortium, presented groundbreaking research at TRB 2025, focusing on AI-driven transportation planning, disaster response, and infrastructure resilience.
🔹 Optimizing Emergency Evacuation Under Pandemic Settings – Razieh Khayamim, Dr. Ren Moses, Dr. Eren Ozguven, Dr. Marta Borowska-Stefańska, Dr. Szymon Wiśniewski, Dr. Seçkin Özkul, Dr. Maxim A. Dulebenets
This research develops a bi-objective optimization model to balance evacuation efficiency and virus transmission risk during pandemics. The findings provide data-driven strategies for emergency planners to improve evacuation routing and sheltering policies.
🔹 Assessing Tornado Impacts Using GIS-Based Analysis - Mehmet Burak Kaya, Dr. Alican Karaer, Dr. Eren Erman Ozguven
This study applies GIS-based spatial analysis to evaluate Kentucky tornado exposure and roadway vulnerability. The research highlights critical areas for emergency response planning and disaster preparedness.
🔹 Truck Rest Area Safety & Fatigue Prevention – Mehmet Burak Kaya, Dr. Mohammadreza Koloushani, Dr. Eren Erman Ozguven
Researchers developed a deficiency metric to assess truck rest area effectiveness by analyzing fatigue-related crash data from 2019-2023. The findings inform policy decisions to enhance truck parking infrastructure and road safety.
🔹 AI-Powered Emergency Evacuation Planning – Razieh Khayamim, Dr. Ren Moses, Dr. Eren Ozguven, Dr. Marta Borowska-Stefańska, Dr. Szymon Wiśniewski, Dr. Maxim A. Dulebenets
This study leverages swarm intelligence algorithms (PSO, ABC, ACO) to optimize evacuation routes and resource allocation for faster and more efficient emergency response.
🔹 Machine Learning for Hurricane Roadway Closure Assessment – Samuel Takyi, Dr. Richard Boadu Antwi, Dr. Leslie Okine, Dr. Eren Erman Ozguven, Dr. Ren Moses
Using satellite imagery and AI models, this study classifies roadway conditions post-hurricane, providing critical insights for disaster response and recovery strategies.
🔹 Post-Tornado Roadway Debris Detection – Dr. Richard Boadu Antwi, Prince Lartey Lawson, Dr. Eren Erman Ozguven, Dr. Ren Moses
A GIS-based satellite imagery model identifies tornado-induced debris accumulation, helping state and local agencies prioritize debris removal and infrastructure recovery.
🔹 AI-Based Detection of Turning Lane Features – Dr. Richard Boadu Antwi, Michael Kimollo, Samuel Takyi, Dr. Eren Erman Ozguven, Dr. Thobias Sando, Dr. Ren Moses, Dr. Maxim Dulebenets
Using computer vision models (YOLO), researchers automate turning lane detection from aerial imagery, significantly improving roadway data collection and traffic safety analysis.
These studies demonstrate how AI and geospatial analysis can improve transportation safety, disaster resilience, and infrastructure planning. Stay tuned for more updates from the REAT Center!
#REATResearch #AIinTransportation #DisasterResponse #GISMapping #SmartMobility
.png)
