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.
The guidebook should address and/or include, at a minimum:
- A glossary of terms;
- An introduction and definition of digital twins;
- The range of requirements (security, legal, data systems, functionality, personnel, budget, training, etc.) for implementation of digital twins;
- An overview of current data visualization techniques, platforms, and technologies;
- Explore partnerships for both funding and data sourcing;
- General use cases for digital twins focusing on different airports functions (i.e., landside, terminal, airside, and cargo operations);
- At least four (4) specific airport case studies incorporating airports of various sizes, technological maturity, etc.; and
- At least one (1) case study from outside the aviation industry.
Make sure to address the following elements in the roadmap, at a minimum:
- Identifying and developing a digital twin use case template;
- Identifying and collecting necessary data, both real-time and historical;
- Incorporating predicative analytics tools;
- Reporting and using the outcomes to make decisions;
- Program governance including data and platform ownership; and
- Program maintenance and upgrades.
STATUS: Research in Progress