Guidebook for Digital Twin Programs at Airports

Decision-making in an airport environment is often complex and time-consuming, involving numerous stakeholders and considerable resources, and can result in unanticipated adverse impacts. “Digital twins” are detailed virtual representations of a facility or system kept up-to-date with real-time data. The most advanced examples are supported by machine learning and reasoning. Airports can leverage digital twin technology to build resilience and improve the visualization and situational awareness of their complex environment, leading to optimized business performance and enhanced decision-making. The development, operation, and maintenance of a digital twin will require a significant investment of resources. Airports need help understanding the potential benefits, range of requirements, and the steps for implementing and maintaining a digital twin. The objective of this research is to provide a guidebook for airports to understand the concept of digital twins and the potential stakeholder benefits, and develop a roadmap for implementation of a digital twin program. The guidebook and roadmap should be scalable to airports of all sizes and accompanied by an executive summary for airport leadership.

• Record URL:
  http://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=5205
• Supplemental Notes:
  • Contract to a Performing Organization has not yet been awarded.

Language

• English

Project

• Status: Proposed
• Funding: $350000
• Contract Numbers:

  Project 03-66

• Sponsor Organizations:

  Airport Cooperative Research Program

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

  Federal Aviation Administration

  800 Independence Avenue, SW
  Washington, DC  United States  20591
• Project Managers:

  Griffin, Matthew

• Start Date: 20220420
• Expected Completion Date: 0
• Actual Completion Date: 0

Subject/Index Terms

• TRT Terms: Airport operations; Airport planning; Airports; Decision support systems; Digital simulation; Machine learning; Virtual reality
• Subject Areas: Aviation; Data and Information Technology; Operations and Traffic Management; Planning and Forecasting; Terminals and Facilities;

Filing Info

• Accession Number: 01779210
• Record Type: Research project
• Source Agency: Transportation Research Board
• Contract Numbers: Project 03-66
• Files: TRB, RIP
• Created Date: Aug 16 2021 3:16PM