Excessive vibration of footbridges caused by pedestrian excitation is an important design consideration that has received increased attention in recent years following serviceability-related failures of several notable pedestrian bridges. Numerous models have been proposed and modified for simulating both individual pedestrian footfall excitations as well as groups of persons. However, experimental validation of pedestrian load modeling has yet to be extensively performed for this challenging human-structure interaction problem. This thesis evaluates the performance of published pedestrian load models by comparing time history simulations from a calibrated finite element model of a pedestrian bridge to experimental data obtained from full-scale testing of the structure.Operational modal analysis of a steel twin I-girder span is conducted through ambient vibration monitoring using a distributed wireless sensor network with triaxial accelerometers. The experimentally obtained estimates of the modal parameters are then used to calibrate a finite element model of the structure through structural identification, or finite element model updating, using a genetic algorithm optimization routine. Following this field calibration of the dynamic properties of the model, modal superposition time history analyses of the response of the span obtained using the finite element model with published pedestrian load models are compared to experimental measurements acquired during controlled pedestrian loading. The comparisons reveal that published pedestrian load models significantly underpredict the measured peak accelerations for this case study. Parameter identification of variables within each model through optimization of the model correlation with the measured response was performed to calibrate coefficients within each model to the case study data. The results indicate that, for this structure, a single harmonic periodic load with a force amplitude larger than recommended in standardized models produced strong correlation with the measurement data.