Robust Timetable Information

In public transportation, timetable information aims at finding a good path from a passenger's origin to his destination. Usually, one important criterion is the minimization of the passenger's travel time. However, in case of delays, connections on such a path may not be maintained and the path may not be viable any more. In this talk we consider robust timetable information, i.e., we want to identify a path which will bring the passenger to the planned destination even in the case of delays.  The classic notion of strict robustness leads to the problem of identifying those transfer activities which will never break in any of the expected delay scenarios. This is in general a strongly NP-hard problem. However, a large subset of strictly robust transfer activities can be identified in polynomial time by dynamic-programming which leads to a heuristic for finding strictly robust path. Furthermore, we transfer the notion of light robustness and recoverable robustness to timetable information.