  Understanding the criteria that bicyclists apply when they choose their
routes is crucial for planning new bicycle paths or recommending routes to
bicyclists. This is becoming more and more important as city councils are
becoming increasingly aware of limitations of the transport infrastructure and
problems related to automobile traffic. Since different groups of cyclists have
different preferences, however, searching for a single set of criteria is prone
to failure. Therefore, in this paper, we present a new approach to classify
trajectories recorded and shared by bicyclists into different groups and, for
each group, to identify favored and unfavored road types. Based on these
results we show how to assign weights to the edges of a graph representing the
road network such that minimum-weight paths in the graph, which can be computed
with standard shortest-path algorithms, correspond to adequate routes. Our
method combines known algorithms for machine learning and the analysis of
trajectories in an innovative way and, thereby, constitutes a new comprehensive
solution for the problem of deriving routing preferences from initially
unclassified trajectories. An important property of our method is that it
yields reasonable results even if the given set of trajectories is sparse in
the sense that it does not cover all segments of the cycle network.
