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 needs to adjust its market strategy. Mining the value of passengers and providing differentiated services for passengers with different values are key to the differentiated competition of airlines. 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. The result shows that 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.