Managing Uncertainties in Mobility Policy

Abstract

Policy-makers in Dutch passenger rail face deep uncertainty in long-term demand forecasting as population growth, urbanisation, and climate change undermine the reliability of traditional models. This research applies Exploratory Modelling and Analysis (EMA) and Multi-Objective Robust Optimisation (MORO) to better capture uncertainty and identify fare policies that remain effective across a wide range of future conditions. Using a simplified elasticity-based simulation model and a multi-objective evolutionary algorithm, the study evaluates thousands of scenarios to explore how fare strategies perform against conflicting objectives (ridership, revenue, CO₂ emissions, and capacity). The results show that extreme fare policies are not robust: eliminating fares boosts ridership and lowers emissions but causes unsustainable revenue losses, while high fares secure revenue but suppress demand and climate benefits. Instead, hybrid strategies emerge as more balanced and resilient. For example, an affordable flat-fare travel pass (akin to Germany’s €49 Deutschlandticket) combined with modest peak-hour surcharges can significantly increase ridership and cut emissions while maintaining financial viability. A long-term analysis (2024–2070) further indicates that no single static policy remains optimal; adaptive fare pathways are needed as conditions evolve. Robust policy trajectories generally feature reduced base fares coupled with a rush-hour surcharge to manage capacity and funding. This adaptive, exploratory approach shifts focus from predicting a single future to preparing for many possible futures, supporting more resilient and sustainable transport policy decisions.