Title: Kharita: Robust Map Inference using Graph Spanners,
Abstract: The widespread availability of GPS information in everyday devices such as
cars, smartphones and smart watches make it possible to collect large amount of
geospatial trajectory information. A particularly important, yet technically
challenging, application of this data is to identify the underlying road
network and keep it updated under various changes. In this paper, we propose
efficient algorithms that can generate accurate maps in both batch and online
settings. Our algorithms utilize techniques from graph spanners so that they
produce maps can effectively handle a wide variety of road and intersection
shapes. We conduct a rigorous evaluation of our algorithms over two real-world
datasets and under a wide variety of performance metrics. Our experiments show
a significant improvement over prior work. In particular, we observe an
increase in Biagioni f-score of up to 20% when compared to the state of the art
while reducing the execution time by an order of magnitude. We also make our
source code open source for reproducibility and enable other researchers to
build on our work.