Forecasting Airport Passenger Flows Using Mobile Network Data: Insights for Sustainable Mobility and Strategic Infrastructure Planning

Airports are strategic and environmental nodes in global transport systems where rapid passenger growth challenges capacity, sustainability, and resilience. This study uses anonymized mobile network data to analyze and forecast passenger dynamics at Lisbon Airport, Portugal, linking data‐driven insights to sustainable mobility and infrastructure strategy. Drawing on over 9 million georeferenced observations, temporal and spatial patterns, seasonality, and differences between domestic and international passengers are examined. Following the CRISP‐DM process and employing Prophet for time‐series modeling, the data reveal clustering behaviors and demand fluctuations that inform energy‐efficient operations, workforce planning, and intermodal coordination to reduce congestion and emissions. The modeling framework significantly outperforms standard time‐series baselines, reducing MAE by 21%, RMSE by 17%, and MAPE by 24% while improving R 2 from 0.74 to 0.83 and effectively capturing peak volatility relevant to both operational reliability and environmental sustainability. The study advances understanding by showing how real‐time mobility signals enhance business decision‐making, efficiency, and environmental outcomes across major transport hubs, bridging the gap between data analytics and sustainable management practice.