A large language model–based method for improving decision-making in aviation

Abstract

The rapid emergence of advanced air mobility technologies such as air taxis and delivery drones is transforming the aviation landscape by introducing new layers of complexity for decision-making in dynamic and crowded airspaces. To support this evolving environment, we propose a Large Language Model (LLM)-based method that improves decision-making in aviation operations by leveraging users’ natural language queries alongside validated aviation expert models and real-time data sources. This method interprets user intent, selects appropriate expert tools such as models for trajectory prediction, runway configuration, and Traffic Management Initiative (TMI) forecasting, and provides contextualized responses using relevant aviation data sources. Unlike general purpose LLM applications, the proposed system grounds its outputs in aviation-validated tools and data sources, addressing the risk of hallucination in a domain where inaccurate information can have serious operational consequences. Evaluated on 1,000 domain-specific aviation queries across nine tools using two models, GPT-4o and GPT-4o-mini, the system achieves tool selection accuracy ranging from 86% to 100% across all tools, with results independently validated on a held-out test set. This work illustrates the potential of LLMs as decision support tools in aviation when carefully grounded in validated domain-specific models and datasets.