  We consider the line planning problem in public transportation, 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 experimental 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 experiments 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.
