  Even though clustering trajectory data attracted considerable attention in
the last few years, most of prior work assumed that moving objects can move
freely in an euclidean space and did not consider the eventual presence of an
underlying road network and its influence on evaluating the similarity between
trajectories. In this paper, we present two approaches to clustering
network-constrained trajectory data. The first approach discovers clusters of
trajectories that traveled along the same parts of the road network. The second
approach is segment-oriented and aims to group together road segments based on
trajectories that they have in common. Both approaches use a graph model to
depict the interactions between observations w.r.t. their similarity and
cluster this similarity graph using a community detection algorithm. We also
present experimental results obtained on synthetic data to showcase our
propositions.
