A methodology for calculating the unmet passenger demand in the air transportation industry

A methodology to estimate the unmet demand is developed using machine learning algorithms. The unmet demand in an origin-destination airports pair (OD pair) is the unattended number of passengers that could not fly because of economic conditions of supply and demand. The forecast of the unmet demand is important for strategic decisions of new planning such as opening new routes, increasing/decreasing number of services, and aircraft choice. The first contribution of this paper is to develop a single-class methodology to unconstraint or detruncate pax demand to estimate the market size of an OD pair. This methodology mixes time-series methods with the bootstrap distribution function and machine learning algorithms. This methodology considers socioeconomic variables at community zone and airport levels to forecast the market size of an OD pair. The second contribution of this paper is to design a methodology that estimates the unmet demand of an OD pair. The advantage is its ability to simulate the unmet demand based on statistical analysis with a confidence level of (1-α)%. The calculations are evaluated by describing the distribution of the market size historical data because distribution functions give the possibility to calculate pax demand without knowing the parameters that have an influence on it. Finally, the third contribution of this paper is to develop an approach to identify new airline OD pairs which could be considered as potential airline markets with certain risk level. This approach is based on the calculations of the OD pair unmet demand and OD pair pax demand forecast on four scenarios. The proposed methodology is applied to the US air pax industry as case study. The results indicate that hubs airports are under extreme competition. Small and primary airports located in big cities are not under competition in some quarters meaning that socioeconomic factors among airports change according with the seasonality of year.