Population growth and city sprawl have been driving increasing amounts of traffic congestion in multiple major cities worldwide. In this scenario, developing efficient public transportation networks becomes critical to ensure adequate mobility. Hub network location models address the problems of designing public transit networks to model —and to optimize— passenger mobility. More specifically, hub-line location problems (HLLP) play an essential role in the design of rapid transit corridors and subway lines. In this work we address the profit-oriented hub-line location problem (ED-HLLP) for which we introduce a column generation method to solve the linear relaxation of a mixed-integer model. The proposed methodology leads to the calculation of primal and dual bounds. We assess the performance of the new approach on some classic datasets from the HLLP literature. Furthermore, we conduct a more realistic study on a problem instance representing the metropolitan area of Montreal, Canada. Finally, we conduct a sensitivity analysis to assess the major attributes driving our results, both from an algorithmic point of view as well as from a planning perspective.