In this talk, we present new models and control techniques for transportation on large-scale networks. First, we introduce a new two-dimensional traffic model based on partial differential equations (PDEs). We show the validation of the model based on synthetic and real data. Then, we propose an innovative control design, based on the 2D model, that considerably simplifies control design for traffic systems evolving in large-scale networks. The idea consists in projecting the flow evolution into a new space where the control problem can be decomposed in a finite number of one-dimensional problems. Lastly, we show how emergent behaviors in transportation, described with control theory and systems, can be complemented with big data and machine learning algorithms to address more complex societal implications.