Sander Mooren, Andreas Hartmann & Alejandro Tirachini
ABSTRACT
To effectively manage road infrastructure, simulation models are commonly used to forecast traffic flows and support decision-making. However, transport faces deep uncertainty amid ongoing societal developments and climate change. This paper demonstrates the potential of exploratory modelling and analysis (EMA) in road infrastructure. It simulates a large ensemble of scenarios using a four-step traffic-demand model covering national roads in a large economic region in the Netherlands. Three scenario discovery techniques reveal that teleworking and, to a lesser extent, consumer energy prices, the number of jobs, and GDP are influential variables for a high traffic volume in 2050. Locally, however, utilisation of a case asset uncovered different factors, such as the share of loaded trucks and GDP, as significant. The results highlight how EMA can reveal multi-level factors relevant to long-term infrastructure performance and allow infrastructure agencies and policymakers to make more purposeful and robust decisions.
