Copula-based scenario generation for urban traffic models

One the most attractive features of urban traffic network models is the possibility of running hypothetical scenarios to evaluate the impact of strategic and tactical decisions. In order to provide statistically meaningful results, the simulation runs should be able to capture the complex multivariate distributions characterizing the involved variables, and a key factor is the correct modeling of possible statistical dependence among the generated inputs used to define the desired scenarios. Here we introduce a data-driven method for scenario generation based on the statistical concept of copula models, through which the marginals of single input parameters can be chosen freely without altering the joint multivariate dependence structure of the inputs. This approach is particularly suited to running what-if scenarios, in which the marginal distributions of the inputs are changed, while retaining the general joint dependence scheme. The method exploits only a finite set of measures from the network and copes with arbitrary sets of input parameters without requiring any assumption on the kind of traffic model or the shape of the involved multivariate distributions. Simulation tests involving different scenarios show that the proposed method is able to capture complex multivariate distributions of the simulation outcomes and yield reliable inferences in what-if analyses, significantly better than in the case the joint dependence is ignored. •Statistically-sound what-if scenario generation for urban traffic models.•Data-driven generation through copula models trained on available data.•The method can be applied to any traffic model and arbitrary sets of input parameters.•Joint dependence of generated inputs is preserved, beyond simple correlation analysis.•Simulation results are provided for both a large-scale and a microscopic model.