Traffic simulation has become an invaluable part of the traffic engineering toolbox. However, the majority of driver models are designed to recreate traffic performance based on interactions among vehicles. In keeping with this pursuit, most are fundamentally built to avoid collisions. This limits the applicability of using these models for addressing safety concerns, especially those regarding pedestrian safety performance. However, by explicitly including some of the sources of human error, these limitations can, in theory, be overcome. While much work has been done toward including these human factors in simulation platforms, one key aspect of human behavior has been largely ignored: driver distraction. This work presents a novel approach to inclusion of driver distraction in a microsimulation or agent-based model. Distributions of distraction events and inter-distraction periods are derived from eye-glance data collected during naturalistic driving studies. The developed model of distraction is implemented -- along with perception errors, visual obstructions, and driver reaction times -- in a simulated mid-block pedestrian crossing. The results of this simulation demonstrate that excluding any of these human factors from the implemented driver model significantly alters conflict rates observed in the simulation. This finding suggests that inclusion of human factors is important in any microsimulation platforms used to analyze pedestrian safety performance.