Post-COVID-19 recovery and resilience in passenger and cargo traffic: A Bayesian vector autoregressive analysis of India’s top 10 busiest airports

Abstract

This study examines the post-COVID-19 resilience of India’s ten busiest airports using passenger and cargo traffic data from 2016 to 2024. A Bayesian vector autoregression (BVAR) model generates counterfactual forecasts, enabling a comparative assessment to classify airports as outperformers, forecast achievers, or underperformers. Beyond performance categorisation, the study investigates the role of airport infrastructure in shaping resilience outcomes through Spearman correlation and ordered logistic regression (OLOGIT) analysis. Results indicate that infrastructure attributes such as cargo terminal availability, runway capacity, and metro connectivity are significantly associated with higher resilience. Airports with stronger and more adaptive infrastructure recovered more effectively from pandemic disruptions. These findings offer actionable insights for infrastructure planning, crisis preparedness, and long-term policy strategies aligned with national initiatives such as the UDAN regional connectivity scheme.