  Advancements in Intelligent Traffic Systems (ITS) have made huge amounts of
traffic data available through automatic data collection. A big part of this
data is stored as trajectories of moving vehicles and road users. Automatic
analysis of this data with minimal human supervision would both lower the costs
and eliminate subjectivity of the analysis. Trajectory clustering is an
unsupervised task.
  In this paper, we perform a comprehensive comparison of similarity measures,
clustering algorithms and evaluation measures using trajectory data from seven
intersections. We also propose a method to automatically generate trajectory
reference clusters based on their origin and destination points to be used for
label-based evaluation measures. Therefore, the entire procedure remains
unsupervised both in clustering and evaluation levels. Finally, we use a
combination of evaluation measures to find the top performing similarity
measures and clustering algorithms for each intersection. The results show that
there is no single combination of distance and clustering algorithm that is
always among the top ten clustering setups.
