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comparing its results. Section 5 discusses multiple improvements to be made to the
original algorithm to be more compatible with the specificities of indoor environ-
ments. This chapter is completed with a conclusion on the discussed issues.
2 Least Risk Path Algorithm
The ultimate goal of cognitive algorithms is to lower the cognitive load during
the wayfinding experience. Various cognitive studies have indicated that humans
during navigation value the form and complexity of route instructions equally as
much as the total path length (Duckham and Kulik 2003 ). This is the reason why
several algorithms have been developed for outdoor space with the purpose of
simplifying the navigation task for unfamiliar users. In this chapter, the least risk
algorithm forms our focal point as it is implemented in a three-dimensional indoor
environment (Grum 2005 ). More specifically, we want to investigate whether the
results of the least risk path algorithm have the same connotation and importance
in indoor spaces as in outdoor space where it was originally developed. Also, the
least risk path algorithm is analysed for its applicability in providing route instruc-
tions that are more adhering to the natural wayfinding behaviour of unfamiliar
users in indoor space.
The least risk path as described by Grum ( 2005 ) calculates the path between
two points where a wayfinder has the least risk of getting lost along the path, by
selecting all edges and intersections with a minimal risk value. The risk of getting
lost is measured at every intersection with the cost of the risk calculated as the cost
for taking the wrong decision at the intersection. This algorithm assumes (1) that
the person taking the path is unfamiliar with its environment (and as such local
landmarks). Also, (2) when taking a wrong path segment, the wayfinder notices
this immediately and turns back at the next intersection (Grum 2005 ). While the
algorithm assumes that an unfamiliar user immediately notices a wrong choice,
Grum ( 2005 ) also acknowledges that the algorithm needs to be tested for its repre-
sentativeness of the actual behaviour of users (Fig. 1 ).
The formula for the calculation of the risk value at a certain intersection and the
total risk of an entire path p is as follows:
Total _ Risk ( p ) = risk _ values ( i ) + lengths
Risk _ Value ( i ) = 2 length _ wrong _ choices
possible _ choices
Equation 2 indicates that the risk value is dependent on the number of street seg-
ments converging on the intersection, combined with twice the length of each
individual segment (as it assumes the user will return through the same edge
when going in the wrong direction). The risk value of an intersection increases
with more extensive intersections and with many long edges that could be taken
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