Although many transportation agencies have invested substantially in efforts to improve cycling environments, limitations on methodologies used to estimate bicycle exposure (i.e., volume) continue to impact the decision process. Traditional methods for measuring bicycle volume have been proven to be challenging and costly, especially when planning for non-motorized facilities at network level. One of the potential supplement to traditional methods is to use crowdsourced cycling data. This study explored the potential of incorporating crowdsourced data in estimation methods to improve the spatial-temporal estimation of bicycle exposure. Different probabilistic and machine learning models were tested, including the Negative Binomial (NB) model, Random Forest (RF), Support Vector Machines (SVM), Artificial Neural Network (ANN) and K-Nearest Neighbors (KNN). In terms of prediction, the Random Forest model was found to have a better prediction capability. The addition of Strava counts, which had an average observed penetration rate of 7 percent, improved the RF model significantly by increasing its ability to explain variations in hourly bicycle volume from 65 percent (R-Sqrd = 0.65) to 71 percent (R-Sqrd = 0.71). The study also conducted a simulation study to assess the change in model performance based on different simulated Strava penetration rates and found that a unit change in the percent of simulated Strava penetration rate has a very significant influence on the model’s prediction performance. The products of this study can assist planners to make informed decisions currently or in the future by providing them with a reliable method for estimating bicycle exposure.