Robust map matching for heterogeneous data via dominance decompositions

Abstract

For a given sequence of location measurements, the goal of the geometric map matching problem is to compute a sequence of movements along edges of a spatially embedded graph which provides a 'good explanation' for the measurements. The problem gets challenging as real world data, like traces or graphs from the OpenStreetMap project, does not exhibit homogeneous data quality. Graph details and errors vary in areas and each trace has changing noise and precisions. Hence formalizing what a 'good explanation' is, becomes quite difficult. We propose a novel map matching approach which locally adapts to the data quality by constructing what we call dominance decompositions. While our approach is computationally more expensive than previous approaches, our experiments show that it allows for high quality map matching even in presence of highly variable data quality without parameter tuning.