The development of real-time traffic models is of paramount importance for the purposes of optimising traffic flow. Inspired by the compositional model (CM) [1] and the METANET model [2], [3], this paper proposes an interval approach for macroscopic traffic modeling. We develop an interval compositional model (ICM) and an interval implementation of METANET model (IMETANET) that provide a natural way of predicting traffic flows without the assumption of uniform distribution of vehicles in a cell. The interval macroscopic models are suitable for real-time applications in road networks and can be part of road traffic surveillance and control systems. The performance of the interval approaches are investigated for both the ICM and the IMETANET models. The efficiency of the interval models is demonstrated over simulated data and also over real traffic data from the United Kingdom, from MIDAS data sets.