• Skip to primary navigation
  • Skip to main content
Logo

Work Zone Safety Information Clearinghouse

Library of Resources to Improve Roadway Work Zone Safety for All Roadway Users

  • About
  • Newsletter
  • Contact
  • X
  • Facebook
  • LinkedIn

  • Work Zone Data
    • At a Glance
    • National & State Traffic Data
    • Work Zone Traffic Crash Trends and Statistics
    • Worker Fatalities and Injuries at Road Construction Sites
  • Topics of Interest
    • Advancing Nighttime Work Zone Safety
    • Commercial Motor Vehicle Safety
    • Smart Work Zones
    • Transportation Management Plans
    • Accommodating Pedestrians
    • Worker Safety and Welfare
    • Project Coordination in Work Zones
  • Training
    • Online Courses
    • FHWA Safety Grant Products
    • Toolboxes
    • Flagger
    • Certification and
      Accreditation
    • For New Drivers
  • Work Zone Devices
  • Laws, Standards & Policies
  • Public Awareness
  • Events
  • Contact
  • Search

Information Processing

Meta-Reinforcement Learning With Hypernetworks for Variable Speed Limit Control Under Adverse Weather and Work Zones

Variable speed limits (VSL) have been widely implemented to alleviate highway congestion and enhance operational efficiency. However, most existing studies focus on fixed traffic scenarios, making … [Read more...] about Meta-Reinforcement Learning With Hypernetworks for Variable Speed Limit Control Under Adverse Weather and Work Zones

Analysing Roadwork Zone Incursions and Accidents Using A Sustainable GenAI System

Roadwork zone incursions, which refer to unauthorized or unintended entries into restricted work areas, present significant safety hazards and often lead to road accidents. While these incidents … [Read more...] about Analysing Roadwork Zone Incursions and Accidents Using A Sustainable GenAI System

Evaluating LLMs for Roadway Design Compliance: An MUTCD-Based Evaluation of GPT-4 and Claude 3.5

Traffic design has long relied on rule-based frameworks, empirical data, and professional judgment to ensure safe and efficient roadway operations. Practitioners frequently consult design manuals such … [Read more...] about Evaluating LLMs for Roadway Design Compliance: An MUTCD-Based Evaluation of GPT-4 and Claude 3.5

Leveraging Large Language Models and Deep Learning for Construction Work Zone Intrusion Risk Assessment

Construction work zones are essential for roadway maintenance. In most cases, the traffic flow should not be interrupted, which requires work zones to be set up alongside the ongoing traffic, … [Read more...] about Leveraging Large Language Models and Deep Learning for Construction Work Zone Intrusion Risk Assessment

AutoML-Enhanced Delay Forecasting With SHAP Interpretability in Highway Work Zones Under Diversion Constraints

The increasing scale of highway reconstruction and expansion projects has intensified traffic management challenges in construction zones, particularly within sparse road networks constrained by … [Read more...] about AutoML-Enhanced Delay Forecasting With SHAP Interpretability in Highway Work Zones Under Diversion Constraints

How AI Could Reduce Struck-Bys in Road Work Zones

The article discusses how artificial intelligence (AI), virtual reality (VR), and augmented reality can improve construction and work zone safety by helping workers recognize hazards and maintain … [Read more...] about How AI Could Reduce Struck-Bys in Road Work Zones

  • Page 1
  • Page 2
  • Page 3
  • Interim pages omitted …
  • Page 19
  • Go to Next Page »

Copyright © 2026 American Road & Transportation Builders Association (ARTBA). The National Work Zone Safety Information Clearinghouse is a project of the ARTBA Transportation Development Foundation. It is operated in cooperation with the U.S. Federal Highway Administration and Texas A&M Transportation Institute. | Copyright Statement · Privacy Policy · Disclaimer
American Road and Transportation Builders Association Transportation Development Foundation, American Road and Transportation Builders Association U.S. Department of Transportation Federal Highway Administration Texas A&M Transportation Institute