Dockless e-scooter schemes have seen increasing popularity in 28 German cities. Increasing use on insufficiently dimensioned bicycle infrastructure can lead to conflicts between e-scooter riders and cyclists. A new approach was developed in order to detect potential zones of conflict by overlaying aggregated bicycle and e-scooter trajectories in the City of Dresden, Germany. Bicycle data is being obtained by the annual STADTRADELN campaign where cyclists record and transmit daily trips via GPS for a period of three weeks. Simultaneously, e-scooter API data has been collected over a course of 8 weeks from June to September 2021. Origin/Destination data has been generated and routed over a OSM network in order to obtain aggregate d e-scooter flows. We extrapolated the aggregated bicycle data to match them with the timeframe of the e-scooter data acquisition. Afterwards we spatially joined both: bicycle and e-scooter flows and calculated the link wise proportion of e-scooter trips in relation to bicycle trip volumes. Two important findings emerged: (1) Residential roads have a higher proportion of e-scooter trips. (2) E-scooters are exposed to high bicycle trip volumes on primary roads with bicycle infrastructure. We conclude that this approach can detect possible links of conflict, where overtaking cyclists or insufficient space can lead to dangerous situations. That approach is biased towards a missing route choice model for e-scooter riders or better route data of e-scooters, which needs further research.