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In the past, much research had been dedicated to compute optimum railway timetables. A typical objective was the minimization of passenger waiting times. But only the planned nominal waiting times were addressed, whereas delays, as they occur in daily operation, were neglected. Rather, conceptually, delays were treated mainly in an online-context, and solved as a separate optimization problem, called delay management.
We provide the first computational study which aims at computing delay resistant periodic timetables. In particular we assess the delay resistancy of a timetable by evaluating it subject to several delay scenarios, to which optimum delay management will be applied.
We arrive at computing delay resistant timetables by selecting a new objective function which we design to be in the middle of the traditional simple timetabling objective and the sophisticated delay management objective. This is a slight extension of the concept of "Light Robustness", as it was proposed by Fischetti and Monaci (2006). Moreover, in our application we are able to provide accurate interpretations for the ingredients of Light Robustness.
We apply this new technique to real-world data of a part of the German railway network of Deutsche Bahn AG. Our computational results suggest that a significant decrease of passenger delays could be obtained at a relatively small price of robustness.
The timetable is the essence of the service offered by any provider
of public transport'' (Jonathan Tyler, CASPT 2006). Indeed, the
timetable has a major impact on both operating costs and on passenger
comfort. Most European agglomerations and railways use periodic timetables in which operation repeats in regular intervals. In contrast, many North and South American municipalities use trip timetables in which the vehicle trips are scheduled individually subject to frequency constraints. We compare these two
strategies with respect to vehicle operation costs. It turns out that
for short time horizons, periodic timetabling can be suboptimal; for
sufficiently long time horizons, however, periodic timetabling can
always be done in an optimal way'.