Kharita: Robust Map Inference using Graph Spanners

Œewidespread 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 ecient algorithms that can generate accuratemaps in both batch and online seŠings. Our algorithms utilize techniques from graph spanners so that they produce maps can e‚ectively 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 signi€cant 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.