Robust Decision-Making for Transport Policies: Exploratory Modelling & Analysis (EMA) framework with case studies

Transport planning increasingly operates under conditions of deep uncertainty, driven, on the one hand, by technological shifts, behavioural changes, and evolving societal trends, and, on the other, by unexpected geopolitical, economic, and extreme climate events. To overcome the limitations of traditional planning approaches, often based on a narrow set of forecast scenarios and providing limited insight into how transport strategies perform across a wide range of plausible futures, this research adopts an exploratory approach to support robust and adaptive transport decision-making. The proposed Exploratory Modelling and Analysis (EMA) framework addresses two core objectives: (1) stress-testing transport policy packages to evaluate robustness and reveal potential failure points, and (2) designing strategies that maintain robust performances across diverse and uncertain conditions. The approach aligns with the direction advocated by Lempert et al. (2020), who emphasize the value of Robust Decision Making (RDM) for transport planning under uncertainty. It also reflects emerging best practices from national transport agencies, which increasingly integrate the use of exploratory scenarios into long-term strategic development plans (ITF, 2023). The framework will be applied to case studies encompassing both infrastructure investments and mobility plans. These will be assessed across multidimensional uncertainty spaces involving demand, technology adoption, user behaviour, and system disruptions. The analysis aims to generate insight into how different strategies perform under stress, contributing to advancing practical methods for transport planning under deep uncertainty, by focusing on policy robustness and adaptability rather than predictive accuracy or optimality under fixed assumptions.