An Algorithm to Create Test Data for Large-Scale Railway Network Revenue Management Models with Customer Choice

Abstract

Large-scale railway network revenue management models with customer choice behavior are not only a challenge from an optimization perspective, it is also complex and time-consuming to collect and set up test data for large networks. To promote research in this field, we present an algorithm that generates test data based on the schedules of railway companies, e.g., the set of itineraries and corresponding data, such as the resource consumption or product attribute values like travel time, number of transfers, etc. The generated data are also useful for other fields of research, such as crew scheduling or delay management. We show that the algorithm generates realistic test data for large-scale networks in only a few seconds. To promote research in the field of large-scale railway revenue management, we make the programming code (incl. a small schedule dataset) publicly available.