This chapter provides a methodology for calibrating network flow models, a fundamental task in transportation science. We address the critical challenge of estimating absolute traffic flows when empirical data is incomplete. We introduce a suite of calibration techniques grounded in the principle of global scaling, which adjusts a relative basis of ideal flow model to match empirical observations. The methodology formalizes the process of using parsimonious data, including traffic counts, average travel times, and average speeds, to estimate absolute flows across the entire network. We establish the theoretical foundations of this approach, presenting a series of propositions and theorems that govern the optimal scaling of an Ideal Flow Network. The chapter demonstrates how these calibration methods can be used as diagnostic tools to validate data and to conduct robust scenario analysis for assessing short-term policy changes.