This research introduces a decision-support system that leverages Large Language Models to automate and enhance traffic incident management on highways.

• Reduces decision-making burden on radio room operators during high-stakes emergencies
• Improves emergency response times and resource allocation efficiency
• Creates a closed-loop system that continuously learns from incident management data
• Enhances public safety through more consistent and rapid incident handling

For security professionals, this work demonstrates how AI can augment human decision-making in critical infrastructure protection without replacing human oversight, potentially reducing casualties and economic losses from traffic incidents.

Automating the loop in traffic incident management on highway