The influence of road gradient in an integrated approach of real driving cycles and emissions factors model

Transport activities contribute significantly to the air pollution and its impact on emissions is a key element in the evaluation of any transport policy or plan. Calculation of emissions has therefore gained institutional importance in the European Community. Recently, the scientific community has assessed evidence that exposure to outdoor air pollution causes lung cancer and increases the risk of bladder cancer. Because air pollution in urban areas is mainly caused by transportation, it is necessary to evaluate pollutant exhaust emissions from vehicles during their real-world use. Nevertheless their evaluation and reduction is a key problem, especially in the cities, that account for more than 50% of world population. To obtain emission factors several methods make use only of vehicle mean velocity, which can be easily obtained by vehicle flow and density in the road. Among them it is worth mentioning COPERT IV, MOBILE, INFRAS, MEET models that are widely used in the practice. In ARTEMIS FP project, a new statistical approach has been developed capable to consider more attributes than the simple mean speed to characterize driving behaviour, not only in the determination of driving cycles but also in the emission modelling. In this context, a meso scale emission model, named KEM, Kinematic Emission Model, able to calculate emission factor was developed. However, it is necessary to consider that the input to this model is, in any case, the driving cycle, and that to develop a quantitative method capable to determine the exact mix of driving cycle, on the basis of road characteristics and traffic management rules, results a very hard job. In addition a particular attention could be given to the slope variability along the streets during each journey performed by the instrumented vehicle. So in this paper we try to develop a second version of KEM model that dealt with the problem of describing and introducing same variables relative to road gradient variability in a quantitatively way. In the context of correlation study between driving cycles/emission/geographical