A numerical optimization approach for calibration of dynamic emission models based on aggregate estimation of ARTEMIS

Abstract

In this paper, we propose a numerical approach to calibrate dynamic emission models when on-road or in-lab instantaneous emission measurements are not fully available. Microscopic traffic simulation is applied to generate dynamic vehicle states in the second-by-second level. Using aggregate estimation of ARTEMIS as a standard reference, a numerical optimization scheme on the basis of a stochastic gradient approximation algorithm is applied to find optimal parameters for the dynamic emission model. The calibrated model has been validated on several road networks with traffic states generated by the same simulation model. The results show that with proper formulation of the optimization objective function the estimated dynamic emission model can reasonably capture the trends of online emissions of traffic fleets.