A novel forecasting methodology for business aviation movements

Given the intrinsic features of Business Aviation (BA) services (on-demand, operational flexibility, lower fleet homogeneity, cost pressure, and typically short flight distance), the goal of this paper is to propose a novel methodology to model and forecast worldwide BA movements, while assessing the potential introduction and benefits of new greener aircraft technologies.The paper develops parametric and machine learning predictive models to capture and investigate the impact of major factors that drives the Business Aviation market segment. As explanatory variables, we consider both socio-economic and supply-related attributes capturing tailored elements and specificities of BA services. A series of scenarios concerning the evolution of key exogenous drivers, new technologies and trends are considered to forecast future BA flight operations and fleets on a worldwide scale through 2050. We validate the performance and benefits of the proposed modeling approaches through an empirical case study based on the worldwide Business Aviation network in 2019. Given the lack of reliable readily-available datasets of BA flight movements, we combine large-scale microscopic (flight-level) ADB-S data with fleet composition dataset to build a comprehensive dataset for model development. Once calibrate, we deploy the mode to predicting the future BA network considering the potential connectivity and environmental impact of newer technologies.Literature on non-scheduled air transport is significantly scarcer than the scheduled counterpart, with only a few contributions focusing on BA services (i.e., Maertens et al, 2019 Jacobs et al 2020). However, the significant role of BA (as demonstrated by almost 3 million movements in 2019) combined with its characteristics makes it a no-neglectable market to be better understood and a promising testing bed for new and green aircraft technologies in the short-run