Dockless e-scooters have become increasingly popular in Germany, but their use of inadequate bicycle infrastructure can lead to conflicts with cyclists. A new approach to detecting potential conflict zones was developed by joining crowdsourced bicycle data and aggregated e-scooter flows in the City of Dresden, Germany. We calculated the link-wise proportion of e-scooter trips in relation to bicycle trip volumes by collecting e-scooter API data over 8 weeks, routing the generated OD-data over an OSM network and spatially joined both datasets after extrapolating the bicycle flows to match the data collection timeframe. We found that e-scooters are often used on local roads, and are exposed to high bicycle trip volumes on cycle lanes. This approach is able to detect possible conflict zones, however, it is biased toward a missing e-scooter route choice model, which needs further research.