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Map matching has been used to reduce the noisiness of the location estimatesby aligning them to the road network on a digital map. A growing number ofapplications, e.g. energy-efficient localization and cellular provider sidelocalization, depend on the availability of only sparse and coarse-grainedpositioning data; leading to a challenging map matching process.  In this paper, we present semMatch: a system that can provide accurateHMM-based map matching for challenging positioning traces. semMatch leveragesthe smartphone's inertial sensors to detect different road semantics; such asspeed bumps, tunnels, and turns; and uses them in a mathe-matically-principledway as hints to overcome the sparse, noisy, and coarse-grained inputpositioning data, improving the HMM map matching accuracy and efficiency. To dothat, semMatch applies a series of preprocessing modules to handle the noisylocations. The filtered location data is then processed by the core of semMatchsystem using a novel incremental HMM algorithm that combines asemantics-enriched digital map and the car's ambient road semantics in itsestimation process.  We have evaluated semMatch using traces collected from different citiescovering more than 150km under different harsh scenarios includingcoarse-grained cellular-based positioning data, sparse GPS traces withextremely low sampling rate, and noisy traces with a large number ofback-and-force transitions. The results show that semMatch significantlyoutperforms traditional map matching algorithms under all scenarios, with anenhancement of at least 416% and 894% in precision and recall respectively inthe most difficult cases. This highlights its promise as a next generation mapmatching algorithm for challenging environments.

semMatch: Road Semantics-based Accurate Map Matching for Challenging Positioning Data