Pronóstico pospandemia de tráfico aéreo. Caso de Colombia

Abstract

Airport planning, and therefore the development of air infrastructure, depends to a large extent on the levels of demand that are forecast for the future. To plan investments in infrastructure of an airport system and to be able to meet future needs, it is essential to predict the level and distribution of demand, both for passengers and air cargo. In this thesis work, a medium-long term forecast (10 years) of the demand for passengers and air cargo was made, applied to a specific case study, Colombia, and where the impact on the air traffic during the most severe period of the COVID-19 pandemic, 2020, and the post-pandemic transition period (2021). To achieve this objective, and as a methodological approach, a model of the Bayesian Structural Time Series (BSTS) type is developed, designed to work with time series data, and widely used for feature selection, time series forecasting, immediate, and the inference of the causal impact. From the results obtained, two relevant aspects can be highlighted, firstly, that both demand and its growth trend will recover very soon (in just a couple of years), compared to the pre-pandemic year-2019, in which analyzed case study. And, secondly, the model presents very acceptable MAPE values (between 1% and 7%, depending on the variable to be forecast), which makes the BSTS method a viable alternative methodology for calculating air traffic forecasts.