  Indoor navigation systems leverage shortest path algorithms to calculate
routes. In order to define the "shortest path", a cost function has to be
specified based on theories and heuristics in the application domain. For the
domain of indoor routing, we survey theories and criteria identified in the
literature as essential for human path planning. We drive quantitative
definitions and integrate them into a cost function that weights each of the
criteria separately. We then apply an exhaustive grid search to find weights
that lead to an ideal cost function. "Ideal" here is defined as guiding the
algorithm to plan routes that are most similar to those chosen by humans. To
explore which criteria should be taken into account in an improved pathfinding
algorithm, eleven different factors whose favorable impact on route selection
has been established in past research were considered. Each factor was included
separately in the Dijkstra algorithm and the similarity of thus calculated
routes to the actual routes chosen by students at the University of Regensburg
was determined. This allows for a quantitative assessment of the factors'
impact and further constitutes a way to directly compare them. A reduction of
the number of turns, streets, revolving doors, entryways, elevators as well as
the combination of the aforementioned factors was found to have a positive
effect and generate paths that were favored over the shortest path. Turns and
the combination of criteria turned out to be most impactful.
