Behavioral Models for the Estimation of the Air Transport Demand: The Case Study of Rome-London Flight Connection

Abstract

Obtaining reliable estimates of the air transport demand is fundamental for both the short and the long term: namely, an underestimation or an overestimation of demand leads to an inefficient use of human, management and economic resources. The key players in the air transport system are airline companies and airport operators: for the airline companies, the forecasts of the demand level allows to define in the short-term the supply characteristics, while in the long term allow to plan investments consistent with the characteristics of the expected demand. On the other hand, airport operators need to forecast the levels of demand in the short term in order to define the design and operation of the spaces, while in the long term to plan investments for both land-side and air-side.The paper develops different utility models (Multinomial Logit MNL, Nested Logit NL, Cross-Nested Logit) in order to describe the choice of the users about the flight. The models have been calibrated using different data sets for the real case study of the flight connection between the city of Rome (Italy) and the city of London (England). Results demonstrated the capability of the models to be a reliable decision support system for both the airline companies and the airport operators.