We consider the line planning problem in public transporta- tion, under a robustness perspective. We present a mechanism for robust line planning in the case of multiple line pools, when the line operators have a different utility function per pool. We conduct an experimen- tal study of our mechanism on both synthetic and real-world data that shows fast convergence to the optimum. We also explore a wide range of scenarios, varying from an arbitrary initial state (to be solved) to small disruptions in a previously optimal solution (to be recovered). Our ex- periments with the latter scenario show that our mechanism can be used as an online recovery scheme causing the system to re-converge to its optimum extremely fast.