The REAT Center at Stony Brook University (SBU), a member of the REAT Consortium, presented cutting-edge research at TRB 2025, focusing on AI-driven transportation solutions and workforce development trends.
🔹Acquiring and Accruing Knowledge from Diverse Datasets: A New Approach to Multi-Label Driving Scene Classification – Ke Li, Chenyu Zhang, Dr. Ruwen Qin
This study introduces the Knowledge Acquisition & Accruement Network (KA2N), a model that sequentially learns from diverse driving datasets to improve multi-label scene classification. The framework effectively mitigates domain shift challenges across datasets and achieves high classification accuracy while reducing annotation costs.
🔹 Identification of Workforce Skills Sought in the Transportation Industry: What Do Job Ads Look For? - Alireza Ershad, Dr. Anil Yazici
This research analyzes over 8,000 job ads to identify emerging workforce skill demands in transportation, including AI, automation, and data science. Findings highlight the gap between sought-after skills and existing workforce training, emphasizing the need for competitive salaries and in-house expertise in state DOTs.
These projects contribute to the integration of AI in transportation planning and help shape workforce strategies for a rapidly evolving industry. Stay tuned for more updates from the REAT Center!
#REATResearch #AIinTransportation #WorkforceDevelopment #SmartMobility #TransportationInnovation
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