Cycling is a typical green traffic mode, and takes a growing part of urban traffic volume. Yet limited cyclist behavior models shed light on cases at unsignalized intersections with heterogeneous traffic, where bicycle behavior is characterized by frequent confrontations with other road users (vehicles, bicycles, and pedestrians). This study developed a microscopic simulation model for cyclist behavior analysis at unsignalized intersection with heterogeneous traffic. The cyclist crossing model applied fuzzy logic and social force theory for this purpose. The parameters are either estimated directly based on empirical data or derived indirectly through maximum likelihood estimation. Finally model performance was confirmed through comparisons between estimations and observations on individual trajectory, minimum distances, and average riding speeds of collision avoidance behaviors with different conflicting road users. Simulation results indicated that the model can represent cyclist crossing behavior at unsignalized intersection with heterogeneous traffic as in the real world.