Simulation is a valuable tool for traffic planning that requires reliable modeling of traffic dynamics. Free flow speeds in bicycle traffic depend on the characteristics and preferences of the bicyclists, infrastructure design, and environmental conditions. However, existing models are limited in capturing changes in the speed during free riding, thereby reducing their applicability in bicycle traffic analysis. This study advances microscopic bicycle traffic simulation by developing and evaluating simulation models for free riding dynamics, aiming to capture the heterogeneous and context-dependent effects of infrastructure design (slopes, curves, and presence of intersections) and wind on bicyclist behavior. We implement three models within SUMO —(1) context-based speed distributions, (2) a speed regression model, and (3) a physics-based speed model derived from power output— and benchmark them against built-in models. All models are evaluated using empirical trajectory data from 57 bicycle commuters in semi-controlled experiments in Sweden and Germany. Results demonstrate that the proposed models outperform the existing baselines in replicating speed patterns. In this regard, the physics-based model provides the closest alignment to observed speeds. Omitting context dependency in free riding can result in substantially larger errors in speeds on uphills and downhills, and in deceleration on curves or when crossing intersections. Context-sensitive models enhance the accuracy of bicycle traffic simulation, thereby increasing their usefulness in planning and evaluating bicycling facilities that accommodate the diverse preferences of bicyclists.