Implementing Human-AI Collaboration for Enhancing TMC Freeway Operations

The rapid proliferation of artificial intelligence (AI) is fundamentally transforming real-time operations and decision-making across industries, including transportation. Human operators, while skilled and experienced, are inherently limited in their ability to process and interpret vast streams of data, especially under time pressure and uncertainty. These limitations can lead to overconfidence in judgment, susceptibility to cognitive biases, and challenges in maintaining situational awareness during complex or high-stress events. In contrast, AI excels at analyzing large datasets, identifying patterns, and providing objective, data-driven decision support, making it a powerful tool for augmenting human capabilities. The concept of Intelligence Augmentation (IA) centers on leveraging AI not to replace human decision makers, but to enhance and amplify their reasoning, problem solving, and decision-making. IA emphasizes collaborative partnership, where AI systems handle computationally intensive tasks and humans contribute strategic oversight, contextual understanding, and ethical judgment. This approach preserves human agency while unlocking new levels of operational performance. Traffic Management Centers (TMCs) serve as the central command hubs for monitoring and managing regional transportation networks, including freeways. TMCs rely on a diverse workforce to monitor, detect, and manage traffic incidents, congestion, and emergencies. As transportation systems become more complex and data-rich, the opportunity to integrate AI into TMC operations grows. AI can support TMC staff by automating routine analysis, predicting incidents, optimizing response strategies, and enabling proactive management of traffic flows. However, realizing these benefits requires a thoughtful framework for human-AI collaboration that addresses technical, organizational, and human factors. The objective of this research is to develop a comprehensive technical guide for state departments of transportation (DOTs) and other transportation agencies to effectively incorporate human–AI collaboration into TMC freeway operations. This guide will provide actionable strategies, best practices, and implementation pathways to optimize decision-making, operational efficiency, and safety through the integration of AI technologies alongside human expertise.

• Record URL:
  • http://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=6031

Language

• English

Project

• Status: Proposed
• Funding: $400,000.00
• Contract Numbers:

  03-159

• Sponsor Organizations:

  National Cooperative Highway Research Program

  Transportation Research Board
  500 Fifth Street, NW
  Washington, DC  United States  20001

  American Association of State Highway and Transportation Officials (AASHTO)

  444 North Capitol Street, NW
  Washington, DC  United States  20001

  Federal Highway Administration

  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590
• Project Managers:

  Deng, Zuxuan

• Start Date: 20261001
• Expected Completion Date: 0
• Actual Completion Date: 0

Subject/Index Terms

• TRT Terms: Artificial intelligence; Freeway operations; Human machine systems; Implementation; State departments of transportation; Traffic control centers
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01991705
• Record Type: Research project
• Source Agency: Transportation Research Board
• Contract Numbers: 03-159
• Files: TRB, RIP
• Created Date: Jun 2 2026 11:32AM