Humans engage in wayfinding many times a day. We try to find our way in urban environments when walking towards our work places or when visiting a city as tourists. In order to reach the targeted destination, we have to make a series of wayfinding decisions of varying complexity. Previous research has focused on classifying the complexity of these wayfinding decisions, primarily looking at the complexity of the decision point itself (e.g., the number of possible routes or branches). In this paper, we proceed one step further by incorporating the user, instructions, and environmental factors into a model that assesses the complexity of a wayfinding decision. We constructed and evaluated three models using data collected from an outdoor wayfinding study. Our results suggest that additional factors approximate the complexity of a wayfinding decision better than the simple model using only the number of branches as a criterion.