Abstract: We conduct an experimental study for the timetabling problem in a public railway network under disruptions. We investigate three bicriteriaoptimization problems introduced recently that model the robustness of a timetable towards delays. We experimentally evaluated these models against various waiting time rules at stations. Our results constitute the rst proofs-of-concept for these new robust timetabling approaches.
Abstract: We consider two approaches that model timetable information in public transportation systems
as shortest-path problems in weighted graphs. In the time-expanded approach, every event at
a station, e.g., the departure of a train, is modeled as a node in the graph, while in the timedependent
approach the graph contains only one node per station. Both approaches have been
recently considered for (a simplified version of) the earliest arrival problem, but little is known
about their relative performance. Thus far, there are only theoretical arguments in favor of the
time-dependent approach. In this paper, we provide the first extensive experimental comparison of
the two approaches. Using several real-world data sets, we evaluate the performance of the basic
models and of several new extensions towards realistic modeling. Furthermore, new insights on
solving bicriteriaoptimization problems in both models are presented. The time-expanded approach
turns out to be more robust for modeling more complex scenarios, whereas the time-dependent
approach shows a clearly better performance.
Abstract: We consider two approaches that model timetable information in public transportation systems
as shortest-path problems in weighted graphs. In the time-expanded approach, every event at
a station, e.g., the departure of a train, is modeled as a node in the graph, while in the timedependent
approach the graph contains only one node per station. Both approaches have been
recently considered for (a simplified version of) the earliest arrival problem, but little is known
about their relative performance. Thus far, there are only theoretical arguments in favor of the
time-dependent approach. In this paper, we provide the first extensive experimental comparison of
the two approaches. Using several real-world data sets, we evaluate the performance of the basic
models and of several new extensions towards realistic modeling. Furthermore, new insights on
solving bicriteriaoptimization problems in both models are presented. The time-expanded approach
turns out to be more robust for modeling more complex scenarios, whereas the time-dependent
approach shows a clearly better performance.