Forecasting high-granular air passenger demand flows: An integrated modeling framework applied to the Italian airport system

Demand forecasting is a pivotal aspect of the multifaceted business of airlines and airports, significantly influencing long-term strategic decisions. For airports, accurate traffic forecasts are particularly crucial for aligning infrastructure capacity with future needs, necessitating tailored approaches to capture complex demand dynamics. This paper proposes a novel modeling framework to formulate high-granular itinerary-level demand forecasts, ultimately ensuring robust system-level predictions. The modeling framework leverages a state-of-the-art integrated demand modeling coupled with a customized scenario analysis tool. We demonstrate the validity of the proposed approach in supporting airport strategic planning by reporting the outcomes of its application on the Italian airport system, formulating traffic forecasts up to 2035 and testing predictive ability based on actual traffic data for 2024. We showcase the adaptability of the framework in addressing diverse challenges that decision-makers and policymakers will face in the near future, such as implementing policies to support the aviation industry’s transition to net-zero emissions.