Modelling regime-switching aviation demand under recurrent global disruptions: A joint Passenger–Cargo resilience and forecasting study of Heathrow Airport

This study develops a disruption-aware, region-segmented, joint passenger–cargo resilience and forecasting framework for Heathrow Airport using 20 years of monthly data spanning multiple global crises. Heathrow is examined as a dual passenger–cargo hub whose traffic has been repeatedly affected by the 2008 global financial crisis, Brexit, and the COVID-19 pandemic. The dataset comprises market-wise passenger volumes and cargo tonnage for key regions, enabling long-horizon analysis of traffic evolution and structural change. Methodologically, the study combines structural break detection (including Chow and Bai–Perron tests), a market-wise resilience index based on shock depth, recovery speed, and volatility, passenger–cargo decoupling analysis via rolling correlation and lead–lag patterns, and disruption-aware forecasting models (ARIMA, Prophet, and XGBoost) for a 2026–2035 horizon. Results show that COVID-19 induced persistent regime shifts rather than temporary shocks, with cargo traffic displaying systematically higher and faster resilience than passenger traffic and clear post-2020 decoupling of cargo from passenger demand in several regions. Disruption-aware models reduce long-term forecast errors by approximately 25–40% relative to conventional time-series baselines, particularly around shock periods. The study contributes an integrated empirical framework for understanding regime-switching aviation demand and offers hub-level strategic insights for capacity planning, cargo infrastructure investment, and market diversification under recurrent disruption.