This paper discusses the development and application of a new high resolution traffic emissions and fuel consumption model. The model is needed to adequately address increasingly complex policy and research questions. Over recent years, a large body of test data has become available in Australia, which amounts to hundreds of hours of second-by-second emissions and driving behaviour data for relevant vehicle classes. The data were measured using real-world driving cycles that were developed from Australian on-road driving data. This large amount of data inspired the development of a new hybrid model with a number of innovative aspects. The model uses (new) model variables that reflect vehicle and driving aspects known to influence vehicle emissions (e.g. speed fluctuation, delta power, power oscillation) and employs a statistical approach to find the best empirical relationships. The algorithms are designed to combine an engineering and a statistical approach. This paper will discuss that the information generated by the model can be used in various ways, for instance to develop an emission inventory, to analyse the impacts of particular traffic management measures (e.g. dynamic speed limits, traffic signal coordination, metering signals). In this paper we will demonstrate this by examining the effects of congestion on emissions and fuel consumption.