Although traffic models have been extensively studied, obtaining trustful forecast from these models is still challenging, since the evolution of traffic is also exposed to the presence of uncertainties. In this talk, we will investigate the propagation of uncertainties in traffic flow models, especially in macroscopic second order models applying the stochastic Galerkin approach. Hyperbolic preserving stochastic Galerkin formulations are presented in conservative form, and for smooth solutions also in the corresponding non-conservative form. This allows for stabilization results, when the system is relaxed to a first-order model. We will illustrate the theoretical results with numerical simulations.