  Travel time estimation is a fundamental problem in transportation science
with extensive literature. The study of these techniques has intensified due to
availability of many publicly available large trip datasets. Recently developed
deep learning based models have improved the generality and performance and
have focused on estimating times for individual sub-trajectories and
aggregating them to predict the travel time of the entire trajectory. However,
these techniques ignore the road network information. In this work, we propose
and study techniques for incorporating road networks along with historical
trips' data into travel time prediction. We incorporate both node embeddings as
well as road distance into the existing model. Experiments on large real-world
benchmark datasets suggest improved performance, especially when the train data
is small. As expected, the proposed method performs better than the baseline
when there is a larger difference between road distance and Vincenty distance
between start and end points.
