The Next Generation Air Transportation System (NextGen) is transforming the way planes move on the ground as well as in the sky. Some of the proposed changes, such as automated scheduling algorithms to generate taxi clearances and speed-based taxi clearances, require thorough testing to ensure safety and reliability. Cognitive modeling is able to uniquely address these issues in a compromise between costly human-in-the-loop simulations and deterministic computer simulations. In this thesis, I present an ACT-R cognitive model to emulate pilot taxiing behavior in a dynamic environment, in order to predict human behavior in novel situations imposed by NextGen constraints. The model is validated by comparing taxi routes generated by the model to routes driven by real pilots while on the job.