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 time-dependent 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 bicriteria optimization 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.

Mobile augmented reality (MAR) technology creates unprecedented possibilities for delivering engaging, immersive experiences to the visitors of cultural heritage sites. Despite the proliferation of available prototypes, the relevant literature still lacks studies investigating the way that users interact with MAR interfaces as well as identifying major usability problems and technology acceptance factors. Herein, we present KnossosAR, a MAR guide implemented for the archaeological site of Knossos (in Crete, Greece) which serves as a testbed for pursuing the abovementioned research objectives while also comparing the (dis)advantages of MAR vs. map-based mobile interfaces in outdoor cultural heritage sites. Among other technical contributions, KnossosAR addresses the occlusion problem, which is commonly encountered in location-based AR applications; that is, it employs an efficient method for estimating the field of view (FoV) of the user in order to handle situations wherein a point of interest is occluded by a physical obstacle (e.g. building). We have conducted field trials which provide preliminary evidence of the efficiency, effectiveness and utility of KnossosAR (including the incorporated FoV estimation approach).