Decision-making in the context of the mobility transition requires considering complexity, many actors, and uncertainty about the future. So, choosing effective policies to achieve a more sustainable system is challenging. We build on participatory modeling and decision-making under deep uncertainty to create a novel approach to investigate the capabilities of decision-makers to interact with an agent-based model to explore various transport policies. This paper reports the results of two workshops with students exploring the mobility transition for a fictional version of a city in the Netherlands The participants made decisions in the role of either government or transport provider and evaluated the systemic impact of those decisions. We found that the participants were well-equipped to deliberate policy options under deep uncertainty using model simulations depicting a range of possible outcomes under different scenarios, embracing uncertainty in some respects and ignoring it in others. This study demonstrates the potential of participatory model-based exploration for mobility transitions to deliberate policy options under uncertainty using an agent-based model.

Transitioning to a more sustainable mobility system is a challenging task due to the complexity of the system, the presence of multiple actors with diverging views on the problem, the uncertainty about the system's current state, how the future might unfold and affect the system, and what values to consider. This research contributes a novel approach to address the wicked problem of transitioning to more sustainable mobility. We build on transition modeling, stakeholder engagement, and decision-making under deep uncertainty (DMDU) to enable decision-makers to frame the problem, model the system, generate policies, and deliberate these policies while taking uncertainty into account. By designing, executing, and evaluating DMDU processes involving stakeholders, we make the first steps to establish a practice of participatory DMDU.

Many European cities are investigating how to transition to climate-neutral transport systems. Due to the transport system's complexity and uncertainty about the future, identifying drivers and choosing effective policies to make the city more sustainable is challenging. Additionally, the chosen policies need to be supported by relevant actors. This study aims to support the municipality of The Hague in generating robust policies supported by and within the municipality. We build on participatory modeling and decision-making under deep uncertainty to create a novel approach to address this goal. In two workshops, the participants formulated goals and objectives, created Causal Loop diagrams, and identified potential interventions. Using a set of possible futures, the interventions were then stress-tested to evaluate their robustness. By explicitly linking, for the first time, participatory modeling and decision-making under deep uncertainty approaches, the participants could understand the system better and deal with uncertainty. Participants gained insight into systemic complexity and methods to deal with it, the inter-relatedness of interventions and their effects, and a shared understanding of the problem and its scope. This study demonstrates the potential of a novel approach to generate supported robust interventions to achieve the goal of a climate-neutral transport system.

Visioning has been widely adopted in transport planning as a method to support explorations of possible future transport systems over a long time horizon. There are vast variations in how visioning is applied but given a clear association between visions and the long-time perspective, it is unclear how these processes handle uncertainty surrounding the resulting visions and their implementation. This study reflects on previous visioning processes by systematically reviewing the scientific publications on participatory visioning in passenger transport. The review identifies possible improvements contributing to a systematic approach that produces concrete visions and actions to deal with uncertainties surrounding the vision and its implementation. We address these improvements by proposing a robust and generative visioning framework, which combines the generative approach in Appreciative Inquiry (Ai) and methods to handle uncertainty in the Dynamic Adaptive Planning (DAP). The framework is illustrated in a case study of the Southwest area of the Dutch city of the Hague that involved over 50 participants in a survey and two workshops. The process produced a vision for the mobility system of the area, a set of measures to realize it (i.e. pathways), and concrete actions to ensure that the pathways are robust against different futures that can affect the implementation. The approach can help planners, policymakers, and researchers in designing a visioning process that helps participants to better appreciate the temporal dimension of the visioning process and improves their awareness regarding the need to safeguard policy interventions against possible impacts of (un)certain future events.