Understanding how environmental conditions and urban design characteristics influence bike ridership is essential for promoting sustainable urban mobility; however, most studies focus on city-wide trends and overlook local variations in microclimate and context. This paper presents a methodology that integrates automated bike count data with high-resolution micrometeorological modeling and urban spatial indicators to quantify how thermal comfort, weather conditions, and streetscape characteristics affect bike ridership at hourly and seasonal timescales. The methodology addresses two critical data limitations in bike count analysis: (i) short temporal gaps due to sensor malfunctions, and (ii) spatial coverage constraints. We develop a temporal count prediction model to impute missing data and a new station prediction model to estimate counts at unmonitored locations. The proposed methodology demonstrates the benefits of using bike counter data and enables cities with different urban contexts to quantify local environmental barriers to biking, predict usage at potential counter locations. Applied to the Brussels Capital Region (BCR) using data from 18 bike counters (2022–2024), the study focuses on weekend biking to isolate environmental comfort effects from commuting patterns. Statistical analysis reveals that rain suppresses biking by (−7.40% to −15.38%), while combined rain and wind conditions suppress biking (−2.18% to −16.15%), cold stress showed stronger reductions in biking activity than heat stress (−6.19% to −8.49% vs. −3.35% to −3.66%), while greenness alone shows small independent effects (−6.23% to +8.02%). Shade was not reportable due to insufficient exposed observations, while during heat stress periods greenness shows small effects (−5.53% to +2.11%).