Given a sequence of possibly sparse and noisy GPS traces and a map of the road network, map matching algorithms can infer the most accurate trajectory on the road network. However, if the road network is wrong - e.g., due to missing roads, misdirected turn restrictions or misdirected one-way streets, - map matching algorithms fail to reconstruct the correct trajectory. Last year, we proposed an extension of existing map matching algorithms to make them robust against such map errors. This year, we show how this extension is used at Lyft to detect the above-mentioned map errors, and share our results to the OSM community.
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Previous talkOpenStreetMap for Location Data Privacy
Location data that maps our movements is increasingly ubiquitous and increasingly open. This data has important benefits to society and our planet, but also carries risks that need to be carefully mitigated...
