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floor strategy. Tasks with either a floor change or a building part change result in
no problems, with the participants first changing to the correct floor or building
part. However, for tasks with changes in both vertical and horizontal direction,
additional information is required to disambiguate the path choice. An algorithm
that wants to minimize the risk of getting lost in a building necessarily needs to
account for these general indoor wayfinding strategies as they correspond to the
natural way of multilevel building navigation for all types of participants.
6 Conclusions
In this chapter, the least risk path algorithm as developed by Grum ( 2005 ) in out-
door space was implemented and tested in an indoor environment. The results
of the tests on an indoor dataset show an average increase in path length of only
4 % compared to the shortest paths for theoretically less risky paths. However, it
appears to be difficult to visually see and understand using a benchmark parameter
set what the actual improvement in risk is when calculating the total risk. The least
risk path often passes by a great amount of complex intersections with many short
edges. These paths will likely not be perceived by the wayfinder as less risky com-
pared to the shortest path. As such, at this point, the least risk path calculations
return non-stable results in terms of selecting least risky edges.
Our main conclusions from the analysis suggest that improvements to the indoor
variant of the least risk algorithm are required. Changes in the calculation of the
risk value, together with a weighing of the parameters will be tested. Also, the
influence of the network structure will be investigated in future research in a search
for optimizing the algorithm to be more compliant to the cognitive notion of indoor
wayfinding. This research will help with the development of appropriate tools to
improve navigation experiences in indoor spaces. Instead of using the shortest path,
a small increase in path length might open up a much simpler and easier route to
explore and will help unfamiliar users in their wayfinding undertakings.
Acknowledgments Financial support from the Flanders Research Foundation (FWO-
Vlaanderen) is gratefully acknowledged. Many thanks also to the reviewers for their constructive
comments on the initial chapter presented at the 8th 3D Geo-Info conference (Istanbul, 2013).
The use of the building CAD files was kindly permitted by the Department of Infrastructure and
Facility Management of Ghent University.
References
Anagnostopoulos C, Tsetsos V, Kikiras P, Hadjiefthymiades SP (2005) OntoNav: a semantic
indoor navigation system. Paper presented at the 1st workshop on semantics in mobile envi-
ronments, Ayia Napa, Cyprus
Atila U, Karas I, Rahman A (2013) A 3D-GIS implementation for realizing 3D network analysis and
routing simulation for evacuation purpose. In: Pouliot J, Daniel S, Hubert F, Zamyadi A (eds)
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