  A persistent challenge in the field of Intelligent Transportation Systems is
to extract accurate traffic insights from geographic regions with scarce or no
data coverage. To this end, we propose solutions for speed prediction using
sparse GPS data points and their associated topographical and road design
features. Our goal is to investigate whether we can use similarities in the
terrain and infrastructure to train a machine learning model that can predict
speed in regions where we lack transportation data. For this we create a
Temporally Orientated Speed Dictionary Centered on Topographically Clustered
Roads, which helps us to provide speed correlations to selected feature
configurations. Our results show qualitative and quantitative improvement over
new and standard regression methods. The presented framework provides a fresh
perspective on devising strategies for missing data traffic analysis.
