  We consider robust shortest path problems, where the aim is to find a path
that optimizes the worst-case performance over an uncertainty set containing
all relevant scenarios for arc costs. The usual approach for such problems is
to assume this uncertainty set given by an expert who can advise on the shape
and size of the set.
  Following the idea of data-driven robust optimization, we instead construct a
range of uncertainty sets from the current literature based on real-world
traffic measurements provided by the City of Chicago. We then compare the
performance of the resulting robust paths within and outside the sample, which
allows us to draw conclusions what the most suited uncertainty set is.
  Based on our experiments, we then focus on ellipsoidal uncertainty sets, and
develop a new solution algorithm that significantly outperforms a
state-of-the-art solver.
