Improving the variability of urban traffic microsimulation through the calibration of generative parameter models

The calibration of urban traffic microsimulation models is addressed in this paper, focusing on the multivariate distribution of traffic features. This allows to take into account day-to-day variability and possibly complex statistical dependencies, enabling robust validation and the computation of more accurate statistics. Variability in traffic models can be obtained, in principle, by suitably tuning origin-destination demand flows, but this becomes problematic for microsimulation models when complex multivariate distributions are considered, due to excessive dimensionality and nonlinearity. Thus, here we investigate how complex multivariate behavior can be achieved by suitably varying only a subset of the parameters ruling the microsimulation dynamics, without imposing any distribution on the demand flows. To this purpose, rather than looking for fixed values of the parameters, we exploit distribution models having the structure of mixtures of uniforms, through which the simulator parameter values can be randomly sampled, thus replacing the simulator inner randomization mechanism. We formalize the optimization of the mixture parameters through a maximum mean discrepancy principle involving the simulator dynamics, which leads to a large-dimensional non-differentiable problem that we solve through a method based on cross-entropy. Preliminary experiments concerning the application of the proposed methodology to the SUMO simulator show how it is possible to capture quite complex multivariate distributions of target flows varying only four parameters.