Forecast to grow: Aviation demand forecasting in an era of demand uncertainty and optimism bias

摘要

Errors in forecasting airport passenger demand arise from uncertain economic climates and planners' optimism, leading airport planners to make misinformed infrastructure investments. We use publicly available data to develop and test methodologies that enable airport planners to (1) predict the probability of a severe contraction in passenger volumes and (2) improve forecast accuracy by systematically incorporating past forecast errors of airport peers thus "grounding" optimistic forecasts. By incorporating past forecast errors from like airports into airport forecasting models, we build a methodology that is grounded in established demand forecasting practices and is able to significantly improve the accuracy of aviation demand forecasting models.