Exploring the Impact of Artificial Intelligence on the Airport Industry (2026)
Chapter: Front Matter
Exploring the Impact of
Artificial Intelligence on
the Airport Industry
WSP USA
Washington, DC
White Paper for ACRP Project 11-08(25-01)
Submitted December 2025
ACRP Web-Only Document 67
Exploring the Impact of Artificial Intelligence on the Airport Industry
© 2026 by the National Academy of Sciences. National Academies of Sciences, Engineering, and Medicine and the graphical logo are trademarks of the National Academy of Sciences. All rights reserved.
ACKNOWLEDGMENT
This work was sponsored by the Federal Aviation Administration (FAA). It was conducted through the Airport Cooperative Research Program (ACRP), which is administered by the Transportation Research Board (TRB) of the National Academies of Sciences, Engineering, and Medicine.
COPYRIGHT INFORMATION
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The National Academy of Sciences (NAS) grants permission to reproduce written material in this publication for classroom and non-commercial purposes subject to the rights of any third parties and appropriate attribution. Permission is given with the understanding that none of the material will be used to imply NAS, TRB, AASHTO, APTA, FAA, FHWA, FTA, GHSA, or NHTSA endorsement of a particular product, method, or practice. For other uses of the written material, users must request permission from the National Academies Press.
DISCLAIMER
Any opinions, findings, and conclusions or recommendations expressed or implied in this document are those of the researchers who performed the research and are not necessarily those of the Transportation Research Board; the National Academies of Sciences, Engineering, and Medicine; or the program sponsors.
The Transportation Research Board does not develop, issue, or publish standards or specifications. The Transportation Research Board manages applied research projects which provide the scientific foundation that may be used by Transportation Research Board sponsors, industry associations, or other organizations as the basis for revised practices, procedures, or specifications.
The Transportation Research Board, the National Academies, and the sponsors of the Airport Cooperative Research Program do not endorse products or manufacturers. Trade or manufacturers’ names appear herein solely because they are considered essential to the object of the report.
The information contained in this document was taken directly from the submission of the author(s). This material has not been edited by TRB.
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COOPERATIVE RESEARCH PROGRAMS
CRP STAFF FOR ACRP WEB-ONLY DOCUMENT 67
Monique R. Evans, Director, Cooperative Research Programs
Waseem Dekelbab, Deputy Director, Cooperative Research Programs
Marci A. Greenberger, Manager, Airport Cooperative Research Program
Jordan Christensen, Senior Program Officer
Demisha Williams, Senior Program Assistant
Natalie Barnes, Director of Publications
Brian Haefs, Associate Director of Publications
Jennifer Correro, Assistant Editor
ACRP PROJECT 11-08/Topic 25-01 PANEL
Field of Special Projects
Daver Malik, Phoenix Sky Harbor International Airport, Phoenix, AZ (Chair)
Vidya Adgaonkar, Geometrics Engineering PS Inc., Redmond, WA
Andria Aguilar, City of Chicago Department of Aviation, Chicago, IL
Austin Beiler, Lancaster Airport Authority, Lititz, PA
David Danks, University of California, San Diego, La Jolla, CA
Eve Machol, Leidos, North Potomac, MD
Enrique Melendez, The JW Group, Ponte Vedra, FL
Mona Sloane, University of Virginia, Charlottesville, VA
William Reinhardt, FAA Liaison
Marty Reynolds, Airlines for America Liaison
Renie Subin, Airports Council International–North America Liaison
Table of Contents
What is Artificial Intelligence?
Artificial Intelligence in the Airport Environment
The Current Needs and Challenges of Airports
The Opportunities and Challenges of Artificial Intelligence
Generative Artificial Intelligence
Terminal and Landside Operations
Other Zones and Cross-Domain Applications
Artificial Intelligence Action Plan
Types of Artificial Intelligence
Artificial Narrow Intelligence (Narrow AI)
Artificial Superintelligence (Super AI)
Public Perception of Artificial Intelligence
Practical Applications of Artificial Intelligence Technology
Artificial Intelligence in Cybersecurity
List of Figures
Figure 1. Evolution of Artificial Intelligence
Figure 2. Data Fusion Based Unmanned Aircraft System Detection
Figure 3. Unified Framework for Unmanned Aircraft System Safety Management
Figure 6. Aircraft Detection and Speed Estimation Using Deep Learning
Figure 7. Hong Kong Airport’s Smart Kiosk Enables Fully Self-Service Departures
Figure 8. Changi Airport’s Self-Service Baggage Drop
Figure 9. Guide Robot at the Incheon International Airport
Figure 11. AI-Powered Automatic Charging Robot
Figure 12. Self-Driving, Electric Wheelchairs in the Miami International Airport
Acronyms and Abbreviations
| ACRP | Airport Cooperative Research Program |
| AI | Artificial Intelligence |
| CT | Computed Tomography |
| C-UAS | Counter-Unmanned Aircraft System |
| FOD | Foreign Object Debris |
| FAA | Federal Aviation Administration |
| FOD | Foreign Object Debris |
| GenAI | Generative AI |
| ICAO | International Civil Aviation Organization |
| IIoT | Industrial Internet of Things |
| IoT | Internet of Things |
| LLM | Large Language Model |
| MRI | Magnetic Resonance Imaging |
| NLP | Natural Language Processing |
| RAG | Retrieval-Augmented Generation |
| RF | Radio Frequency |
| RPA | Robotic Process Automation |
| TSA | Transportation Security Administration |
| UAS | Unmanned Aircraft System |
| XAI | Explainable Artificial Intelligence |
