Systematic identification and analysis of map-matching anomalies in road network models for urban cycling

OpenStreetMap (OSM) is now an important data source for many mobility services. In particular, the OSM road network model is often used by cycling applications and studies. A very common operation with cycling data is map-matching, where the GPS traces of cycling trips are matched against the road network model. However, cyclists can take many unconventional paths that do not always match the official road network model. This fuzziness can severely compromise the ability of map-matching algorithms to produce valuable results. In this work, we introduce the concept of map-matching anomaly as a systematic mismatch between cycling traces and the road network data model to which the traces are expected to be matched. Contrary to sporadic map-matching errors, anomalies will recurrently occur for similar traces and will therefore accumulate at specific locations. This paper proposes a methodology to support the systematic and large-scale identification of map-matching anomalies in urban environments and discusses how knowledge about these anomalies can help cities uncover novel and actionable insights about cycling behaviour. The proposed methodology achieved 84% precision in identifying locations prone to map-matching anomalies. We identified several cases where the OSM road network was incorrect or incomplete. We also identified several locations where a deeper intervention is needed to improve the road network infrastructure.