A Federated Learning-based Civil Aviation Passenger Value Analysis Method and MaaS Construction Considerations in the Epidemic Background

Airline customer demand has plummeted since the COVID-19 pandemic, with
about two-thirds of the world's fleet grounded. Under such circumstances, the
airline's revenue cannot cover its high operating costs, and it is urgent to adjust
the market strategy and form a differentiated competitive advantage. Mining the
value of passengers and providing differentiated services for passengers with
different values is the key to the differentiated competition of airlines, and it is
also the core of airlines' digital strategy implementation. In the case of ensuring
data privacy, this study introduces a privacy-preserving federated learning
method, which combines airline internal data with external operator data,
comprehensively considers multiple dimensional characteristics of passengers.
This study compares a unilateral model using airline data with a joint model
combining airline internal data and operators through federated learning. It can
be concluded that the results of the joint model based on federated learning is
more accurate than the unilateral model. Based on this result, this study puts
forward the thinking about passenger mining and insight in the construction of
MaaS under the epidemic situation, constructs a customer journey map
according to the characteristics of the segmented population, and proposes the
idea of providing different transportation services for the segmented population.
This research provides important theoretical and practical implications for the
airline digital transformation and MaaS construction under the epidemic.