Air Traffic Forecast in Post-Liberalization Context: A Dynamic Linear Models Approach

Abstract

The process of air transport liberalization in Colombia began in 1991. Liberalization entailed the entry of private capital into the airport sector which subsequently led, in several temporary phases, to the privatization of the country’s main airports. Simultaneously, new air operators entered the market. This new market situation, supported by the complete deregulation of airfares, generated a dynamic and sustained growth of air transport in Colombia for two decades. Within the context of post-liberalization, this article presents a forecast (medium-term – 5 years period) of air traffic in the country’s main airport using DLMs (Dynamic Linear Models). It has the following advantages vs. the usual forecast calculation methodologies: it detects stochastic tendencies that are hidden in the time series. It also detects structural changes that allow estimating the variable effect of exogenous shocks over time without increasing the number of parameters. From the results obtained, it should be noted that the application of DLMs presents MAPE (Mean Absolute Percentage Error) values below 1%, which guarantees predictions of higher accuracy and thus introduces a new alternative model to develop reliable forecasts in air transport, at least in the medium-term.

Development of a Regression Model to Forecast Air Travel Demand at Baghdad International Airport

“…No matter how sophisticated the techniques employed or how precise the numbers are, it is essential to note that "the forecasts are unpredictable and controversial since numerous variables, such as humans and their civilizations, are continually changing." In the aviation sector, forecast timeframes are typically short-term, medium-term, and long-term [9]. Forecasting techniques are classified into three types: qualitative, quantitative, and decisionanalysis.…”

“…They discovered that the following factors significantly influence the aviation system: population size, GDP, ticket prices, the level of service provided at airports, and the frequency of daily flights. In addition, Rodriguez et al (2020) [9] used dynamic linear models to forecast aviation traffic in Colombia. They obtained reliable predictions with high accuracy in medium term at least.…”

System Dynamic Model for Simulating Aviation Demand: Baghdad International Airport as a Case Study

“…Forecast horizons in the aviation industry are often shortterm, medium-term, and long-term: 1, 5, and (20 -25 years) respectively [5].…”

What can we learn from 9 years of ticketing data at a major transport hub? A structural time series decomposition

“…Some short-term prediction work was carried out at an hourly time horizon by Dordonnat et al (2008), also in the electricity consumption field. Long-term predictions (a few years), especially when using aggregated data at larger temporalities, are also proposed in Murthy and Kumar (2020), Rodriguez, Pineda, and Olariaga (2020) and Bian et al (2019), while Chen et al (2019) proposed a multivariate model to account for seasonality to predict long-term tourist demand. Another contribution of the predictions consists in comparing the performances of deterministic models with those of stochastic models (Lisi and Pelagatti 2016).…”