There is a growing recognition that traditional forecasting and decision-making approaches might fall short considering the many uncertainties and complexities facing the development of the transport system. The project Managing deep Uncertainty in planning for Sustainable Transport (MUST), funded by Trafikverket and conducted by KTH ITRL and VTI, aims to explore emerging methods for improving the handling of deep uncertainty in the long-term planning of future transport systems. The core of MUST is to explore, develop, and demonstrate tools and methods grounded in Decision Making under Deep Uncertainty (DMDU) and Exploratory Modeling and Analysis (EMA). These approaches are intended to support a shift towards more robust and adaptable planning methodologies.

The project is performed in two phases, with the first phase dedicated to laying a foundational understanding of deep uncertainty in transport planning. This report covers the first phase which has included the following tasks: 

• A literature review on deep uncertainty and existing decision-making and system analysis methods under such conditions, with a focus on transportation. 
• A workshop series with Trafikverket identifying transport planning challenges marked by deep uncertainty.
• A case study of applying DMDU through a case study on climate policy robustness (primarily reported in other deliverables).

The literature review covers how the nature of uncertainty in socio-technical systems can be understood, classified, and analyzed. For policy analysis and decision making, the literature underscores the importance of considering multiple futures in model-based analysis when faced with deep uncertainties. DMDU and EMA methods are reviewed and summarized, and their application to transport are discussed. The literature also summarizes studies on uncertainty in model-based transport planning and policy analysis and concludes that the primary location of deep uncertainty is in the model inputs in the form of “scenario uncertainty”. In the workshop series, uncertainty related to producing the base forecast (Swe: basprognos) and policy analysis for domestic transport climate policy was analyzed. This analysis suggested that scenario uncertainty is a main source of deep uncertainty, but also uncertainty related to the system boundaries where highlighted. Furthermore, potential benefits and drawbacks of EMA and DMDU were discussed. In the case study, it is explored how the Scenario tool can be further leveraged by DMDU. More specifically, MORDM (see Section 2.2.3) is applied to assess to what extent it may allow a broader set of policy options to be explored, and how it can provide a better understanding of the robustness and vulnerabilities of different types of policies. 

A key takeaway from MUST phase 1 is that DMDU and EMA could provide several potential benefits and that methods and tools for applying them are maturing. However, it is possibly a long way to go before DMDU and EMA can be integrated as a regularly used method during the planning process. This is due to organization and process-related issues, as well as technical issues on how to effectively apply DMDU and EMA to Trafikverket’s national transport models. These technical issues will partly be explored in MUST phase 2.