  Path representations are critical in a variety of transportation
applications, such as estimating path ranking in path recommendation systems
and estimating path travel time in navigation systems. Existing studies often
learn task-specific path representations in a supervised manner, which require
a large amount of labeled training data and generalize poorly to other tasks.
We propose an unsupervised learning framework Path InfoMax (PIM) to learn
generic path representations that work for different downstream tasks. We first
propose a curriculum negative sampling method, for each input path, to generate
a small amount of negative paths, by following the principles of curriculum
learning. Next, \emph{PIM} employs mutual information maximization to learn
path representations from both a global and a local view. In the global view,
PIM distinguishes the representations of the input paths from those of the
negative paths. In the local view, \emph{PIM} distinguishes the input path
representations from the representations of the nodes that appear only in the
negative paths. This enables the learned path representations to encode both
global and local information at different scales. Extensive experiments on two
downstream tasks, ranking score estimation and travel time estimation, using
two road network datasets suggest that PIM significantly outperforms other
unsupervised methods and is also able to be used as a pre-training method to
enhance supervised path representation learning.
