This article investigates how workout trajectories from a mobile sports tracking application can be used to provide automatic route suggestions for bicyclists. We apply a Hidden Markov Model (HMM)‐based method for matching cycling tracks to a “bicycle network” extracted from crowdsourced OpenStreetMap (OSM) data, and evaluate its effective differences in terms of optimal routing compared with a simple geometric point‐to‐curve method. OSM has quickly established itself as a popular resource for bicycle routing; however, its high‐level of detail presents challenges for its applicability to popularity‐based routing. We propose a solution where bikeways are prioritized in map‐matching, achieving good performance; the HMM‐based method matched correctly on average 94% of the route length. In addition, we show that the extremely biased nature of the trajectory dataset, which is typical of volunteered user‐generated data, can be of high importance in terms of popularity‐based routing. Most computed routes diverged depending on whether the number of users or number of tracks was used as an indicator of popularity, which may imply varying preferences among different types of cyclists. Revising the number of tracks by diversity of users to surmount local biases in the data had a more limited effect on routing.