Bicycle level of service (LOS) measures are essential tools for transportation agencies to monitor and prioritize improvements to infrastructure for cyclists. While it is apparent that different types of cyclists have varying preferences for the facilities on which they ride, in current research and practice, measures are used that are either insufficiently quantitative and empirical or lack cyclist segmentation. In this study, we conducted a detailed survey on cyclist habits, preferences, and user experience, capturing responses to videos of a bicycle traveling on road segments in the San Francisco Bay Area. The survey provided rich behavioral data, which invited both quantitative and qualitative exploration. We compared facility preferences from the survey to scores from two common measures, NCHRP bicycle level of service (NCHRP BLOS), and level of traffic stress (LTS); and we examined the responses to open-ended questions to gain insights about heterogeneity of preferences among cyclists. Finally, we applied behavioral analysis tools as a proof of concept for a new bicycle level of service measure that accounts for the segmentation of cyclist types via a latent class choice model. Combining statistics and behavioral analysis, we can improve the quality of bicycle level of service measures to make decisions driven by empirically measured cyclist preferences.