Improving Work Zone Management and Safety through AI-Powered Connected Vehicle Data Analysis

Work zones are integral to infrastructure development and maintenance but can pose significant challenges related to traffic congestion, safety hazards, and delays. Traditional work zone management approaches often fall short in addressing the dynamic nature of these challenges. The research team, building on their prior work in automatic incident detection (AID) and dynamic message sign (DMS) optimization, intend to harness the power of connected vehicle data (CVD), artificial intelligence (AI) algorithms, and Industry 4.0 principles to develop a cutting-edge smart work zone management system. By leveraging CVD, the research team aims to enhance traffic incident detection accuracy and predictability. Incorporating an end-to-end cloud-based system, the team will capitalize on the scalability, flexibility, cost-efficiency, security, and data integration capabilities of Industry 4.0, ultimately creating a smart work zone that optimizes traffic flow, reduces congestion, and bolsters overall safety for both workers and road users with the wealth of insights provided by CVD through the following: (1) Data-Driven Optimization: Harnessing real-time data from connected vehicles enables data-driven decision-making, optimizing routes, and enhancing operational efficiency; (2) Predictive Maintenance: By utilizing vehicle sensors, predictive maintenance strategies can be employed, minimizing downtime, reducing costs, and ensuring optimal fleet performance; and (3) Smart Traffic Management: Enabling intelligent traffic management, optimized traffic signals, and congestion reduction. The primary objectives of this research are as follows: (1) to explore the potential opportunities, challenges, and limitations of using AI for work zone management; and (2) to develop an AI-driven framework that integrates CVD augmented with other available data sources to enhance work zone management across the entire life cycle, from planning through operations.

Language

• English

Project

• Status: Active
• Sponsor Organizations:

  Iowa Department of Transportation

  800 Lincoln Way
  Ames, IA  United States  50010
• Managing Organizations:

  Iowa Department of Transportation

  800 Lincoln Way
  Ames, IA  United States  50010
• Project Managers:

  Clute, Khyle

• Performing Organizations:

  Iowa State University, Ames

  Center for Transportation Research and Education
  2711 South Loop Drive, Suite 4700
  Ames, IA  United States  50010-8664
• Principal Investigators:

  Sharma, Anuj

• Start Date: 20240430
• Expected Completion Date: 20250630
• Actual Completion Date: 0

Subject/Index Terms

• TRT Terms: Artificial intelligence; Connected vehicles; Data analysis; Incident detection; Work zone safety; Work zone traffic control
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Safety and Human Factors;

Filing Info

• Accession Number: 01917225
• Record Type: Research project
• Source Agency: Iowa Department of Transportation
• Files: RIP, STATEDOT
• Created Date: Apr 30 2024 2:09PM