In this paper, an affordance-based Colored Petri Net (CPN) model for representing driver behavior is proposed. We adopt a simulation-based approach and conduct an analysis of driver affordances on a driving task on highway systems. The computational CPN model is an extension of the initial conceptual CPN model and allows experimenters to enforce driving preferences as preferential turn probabilities for individual drivers on the highway system. There are two types of driver models: Confederate Driver Model (CDM) and Subject Driver Model (SDM). Whilst, the CDM follows a pre-scripted path of a confederate driver in actual empirical scenarios, the SDM uses a computational algorithm (implemented within the CPN model) to plan a path based on SDM and CDM affordance derived from attributes such as position, velocity and acceleration. This model allows experimenters to analyze and compare the set of affordances that are available for each driver within this dynamic environment. We conclude by providing a descriptive statistical analysis of the results obtained by comparing the empirical and model-predicted driver data for specific scenarios.