ACRP is soliciting Letters of Interest from those interested in performing the research work. A cover letter plus a resume or CV should convey a concise idea of your knowledge of the topic and your related work and experience in the subject area. A statement that you can comply with the terms of the contract should be included. A panel of topic experts will select the contractor at their first panel meeting. The deadline for letters of interest is February 1, 2025 by 5pm eastern and can be submitted at https://survey.alchemer.com/s3/8093208/ACRP-Synthesis-LOI-2025

Tentative Scope

Airports today face an increasingly complex landscape characterized by a few challenges, such as operational inefficiencies, fluctuating passenger demands, and the ever-growing need to enhance the customer experience. These challenges are exacerbated by the dynamic nature of air travel, which demands rapid adaptation to changing conditions, whether due to unexpected surges in passenger numbers, shifts in travel patterns, or the need to respond to sudden disruptions. Traditional approaches to managing these issues relied on human inputs, which can cause delays, increased operational costs, and suboptimal passenger experiences. Large Language Models (LLMs) have the ability to interpret diverse forms of user textual inputs and generate contextual responses on demand. By processing vast amounts of data in real-time and efficiently delivering responses, LLMs have the potential to revolutionize the way airports interact with passengers and enhance operational efficiencies. Recognizing this potential, some hub airports, such as Dallas Fort-Worth International Airport and Hartsfield-Jackson Atlanta International Airport, have begun integrating this technology into their operations. However, it remains unclear how the broader airport industry can fully leverage LLMs. The potential costs and risks associated with adopting LLMs have yet to be thoroughly explored. The synthesis aims to provide a better understanding of LLMs and the opportunities they provide to airports.

The objective of this synthesis is to document how Large Language Models are being integrated the airport environment. 

Information to be describe in a concise report includes (but is not limited to):

• a literature review of LLM’s and how they can be incorporated into the airport environment.
• challenges that airports have faced during the planning and implementation phases.
• the benefits and risks asscoiated with the adopton of LLMs.
• Case examples of airport practices that have integrated, or are planning to adopt, Large Language Models (LLMs) into their operations.

Information will be collected through literature review, a survey of airports if applicable, and interviews with selected airports for the development of case examples. Knowledge gaps and suggestions for future research to address those gaps will also be identified. 

Potential Information Sources:

ACRP Report 157, Improving the Airport Customer Experience (2016) 

IATA, Generative AI and Aviation: Finding crossroads for future implementation (2023)