Taking deep uncertainty into account in traffic modelling

Covid-19 has proven how uncertain the future can be. However, traffic models fail to fully acknowledge this uncertainty. Therefore, decisions made based on these models are not very robust. In this research, Robust Decision Making (RDM) method is applied on a traffic model made for the municipality of Groningen. This method, consisting of 5 steps, can make the decision making process more robust. First RDM frames the system in which the decision is taken. Then, within the boundaries of the system, the model in run on a large set of possible futures. Finally, with the results obtained, based on robustness metrics the most robust policy can be chosen. With the application of RDM in traffic models good policies and interesting scenarios are found. However, the high level of detail in traffic models make the run time quite long. This makes the application of RDM both difficult and hard to fully exploit. The discussion of this research hands some possible tools to manage the run time.