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we can draw the same conclusions from our results of the calculations in indoor
space as those from outdoor space. Also, the question is raised if the size of the
difference is equivalent to outdoors.
A comparison with the result obtained by Grum (
2005
) is difficult as the
author only calculated a single path in outdoor space. In both cases, the total risk
value for the least risk path is minimal and the length is longer than its shortest
path. The outdoor least risk path is 9 % longer than the shortest path, while in our
dataset an average increase of 4 % is detected. However, the number of turns in
our example path (Fig.
5
) is higher for the least risk path compared to the shortest
path. Other paths in our dataset have less turns than their shortest path equiva-
lent. This does not seem to match with the results from the outdoor variant. An
explanation could be that the author only works with a limited outdoor dataset.
Also, the least risk path indoor might have a different connotation because of the
description of the indoor network. Due to the transformation of the corridor nodes
to a linear feature with projections for each door opening, the network complex-
ity is equivalent to a dense urban network. However, the perception for an indoor
wayfinder is totally different. While in outdoor space each intersection represents
a decision point; in buildings, the presence of door openings to rooms on the side
of a corridor is not necessarily perceived as single intersection where a choice
has to be made. Often these long corridors are traversed as if it were a single long
edge in the network.
Simplest paths have similarly to least risk paths the idea of simplifying the
navigation task for people in unfamiliar environments. The cost function in both
simplest and least risk paths accounts for structural differences of intersections,
but not for functional aspects (direction ambiguity, landmarks in instructions…)
like the simplest instructions algorithm (Richter and Duckham
2008
). However,
the simplest path algorithm does not guarantee when taking one wrong decision
that you will still easily reach your destination, while the least risk path tries to
incorporate this while at the same time keeping the complexity of the instruc-
tions to a minimum. Several of the comparison calculations are similar to the
ones calculated for simplest paths (Duckham and Kulik
2003
). At this point, we
cannot compare actual values as it covers a different algorithmic calculation.
In the future, we plan to implement the simplest path algorithm also in indoor
spaces. However, it might be useful at this point to compare general trends
obtained in both.
With regard to the variability of the standard deviations (Fig.
4
) similar con-
clusions can be drawn. At the transition between denser network areas and more
sparse regions, the variability tends to increase as a more diverse set of paths
can be calculated. The sparse and very dense areas have similar ratios show-
ing similar network options and path calculations. The worst-case example can
also be compared to a worst-case dataset of the outdoor simplest path. A similar
trend in 'stripes' as found in the graph in Fig.
6
is also found in the outdoor
simplest path results, also due to sequences of paths that are equal for many
adjacent nodes.
