  Access to quality travel time information for roads in a road network has
become increasingly important with the rising demand for real-time travel time
estimation for paths within road networks. In the context of the Danish road
network (DRN) dataset used in this paper, the data coverage is sparse and
skewed towards arterial roads, with a coverage of 23.88% across 850,980 road
segments, which makes travel time estimation difficult. Existing solutions for
graph-based data processing often neglect the size of the graph, which is an
apparent problem for road networks with a large amount of connected road
segments. To this end, we propose a framework for predicting road segment
travel speed histograms for dataless edges, based on a latent representation
generated by an adversarially regularized convolutional network. We apply a
partitioning algorithm to divide the graph into dense subgraphs, and then train
a model for each subgraph to predict speed histograms for the nodes. The
framework achieves an accuracy of 71.5% intersection and 78.5% correlation on
predicting travel speed histograms using the DRN dataset. Furthermore,
experiments show that partitioning the dataset into clusters increases the
performance of the framework. Specifically, partitioning the road network
dataset into 100 clusters, with approximately 500 road segments in each
cluster, achieves a better performance than when using 10 and 20 clusters.
