ABSTRACTTraffic flow models are a valuable tool to design road networks, assess the effectiveness of traffic controls and provide real-time information for traffic management centers and users. When integrating data, they become the cornerstone for traffic monitoring and forecasting. However, these models are based on a simplified representation of the traffic, subject to uncertainties and with parameters likely to be refined. The paper proposes the formulation of a new traffic flow modeling framework that accounts for error propagation. It is based on the mesoscopic approach of the Lighthill-Whitham-Richards model, with traffic variables represented as Gaussian-Mixture probability density functions. Sensitivity for the error propagation model is analyzed regarding: the network topology, traffic flow parameters and parameterization of the error model. Applications for traffic indicators associated to their errors/uncertainties are illustrated. Novel indicators and decision-support tools can be built for advanced traffic modeling solutions based on the proposed model.