With what objectives do policymakers make transport policy? This fundamental question is subject to a paradigm shift within transport policymaking in the Netherlands. There is an increasing call to let go of the predominant focus on economic well-being and consider a broader notion of well-being instead. However, how exactly this 'broad prosperity' should be operationalized and integrated within ex-ante policy evaluations remains a challenge. Therefore this thesis aims to aid this integration of broad prosperity, by suggesting an alternative approach to operationalizing broad prosperity. One that moves away from the content (i.e. what should it include) and towards the process (i.e. how should it be determined). By approaching broad prosperity as a context-dependent and normative concept, effective value-oriented participation becomes the vehicle to integrate broad prosperity into ex-ante policy evaluations. Through literature review and expert validation, this approach is translated into a practical framework with 5 categories of 11 process-related criteria. It is suggested that these criteria together constitute an effective integration of broad prosperity into ex-ante policy evaluations. By performing desk research and semi-structured interviews, this practical framework is then applied to the ex-ante evaluations of two Dutch transport projects: the Meerjarenprogramma Infrastructuur, Ruimte en Transport (MIRT) projects Oeververbindingen Rotterdam and A15 Papendrecht-Gorinchem. The results suggest that while broad prosperity is being used to some extent in Dutch ex-ante policy evaluations, there is still room for improvement. The great efforts to reduce the information asymmetry among participants, appreciate the participants' knowledge, and ensure all participants' contributions within MIRT processes, suggest that the projects are on their way to integrating broad prosperity. The main areas for improvement include the limited influence of local stakeholders on project objectives and scope, the transport-oriented scope of the (MIRT) process, and the extent of representative value-oriented participation. Future research could examine how to further improve in these areas, address the challenges of a process-based approach, improve the practical framework, or expand the data collection. Overall, a process-based approach to broad prosperity that prioritizes effective value-oriented participation is a promising direction for a more holistic transport system that better serves everyone's values.

The Ministry of Infrastructure and Water Management, responsible for major infrastructure projects in the Netherlands, is exploring methods to incorporate well-being in its project evaluations and designs. To address the limitations of expressing well-being in monetary terms, a new evaluation method called Participatory Value Evaluation (PVE) has been developed. PVE engages citizens in policy decisions, mapping their values and concerns, and providing a more inclusive approach to decision-making. This research focuses on investigating the added value of the PVE when applied to national infrastructure projects, with a case study of the Lelylijn project, a proposed railway line connecting regions in the Netherlands. The research addresses three key areas: where the PVE can be applied in the project process and its perceived added value in the research phase, how policymakers experience the PVE, and how participants perceive their involvement in the PVE. Through interviews with policymakers and analysis of PVE responses, the study found that the PVE has the potential to incorporate public values into decision-making, enhance the quality of decisions, and promote accessibility and inclusivity. Policymakers perceive the PVE as valuable in understanding public opinion comprehensively and improving decision-making. Participants appreciate the PVE's accessibility and educational aspect but suggest improvements in question design and representation of diverse groups. In conclusion, this research highlights the potential benefits of using the PVE in national infrastructure projects, providing valuable insights into its impact on decision-making and public involvement. The study contributes to the growing emphasis on well-being in policy decisions and offers important considerations for the effective implementation of the PVE in future projects.

A relatively novel approach of public participation is the Participatory Value Evaluation (PVE) in which a dilemma of a policymaker is provided to citizens (Mouter, Shortall, et al., 2021). In a PVE, citizens face a realistic choice task in which the policy dilemma is explained to the participant (Hernandez et al., 2023). Participants have to divide a set budget over several options. Currently, there are three main methods for analyzing PVE datasets: descriptive statistics, Latent Class Cluster Analysis (LCCA) and choice modelling. A tool that can identify relations between participant features and their preferences can provide additional insights to policy makers and researchers. If machine learning is to be applied to PVEs, it could be used to predict choice task outcomes based on features of the participant. However, the opaque algorithms in machine learning can make it difficult for a human to understand how the results were produced, which can make it difficult to interpret. The field of Explainable AI has risen as a response to this issue: the aim of explainable AI methods is to give insight in the inner workings of the machine learning algorithm. An example we further research is SHAP (SHapley Additive exPlanations). This thesis focuses on the implementation of SHAP in PVE analysis, under the following research question: ”What additional insights does machine learning with SHAP provide for PVE quantitative analysis compared to conventional methods?” To answer the research question, a case study is performed by applying the SHAP method to the PVE of the National Programme Regional Energy Strategy (NP RES). A Random Forest machine learning model provided the best fit to the dataset. When these results of SHAP analysis of the random forest model are compared to the results of the LCCA, it is apparent that SHAP provides more insights (26 versus 11). SHAP is able to reveal patterns on a smaller scale than LCCA. The resulting insights are different to the results of the LCCA analysis. Many of the insights from SHAP analysis are not seen in the LCCA. Overall, it can be concluded that applying SHAP results in new insights that were not found with other methods used on the NP RES PVE case. This study has shown that SHAP can be a relevant tool to gather insights about the PVE data and the differences among participants. It can gather individual effects of demographic variables on the choices participants make. Therefore it can lead to more and refined policy advice to governments. PVE experiments are still in their infancy. The ability of SHAP to provide additional insights into PVE experiments within this thesis provides an incentive to further use SHAP in PVE experiments, including academic PVEs. SHAP predominantly provides insights into the relation between participant characteristics and their valuation of options in the choice task, allowing for the diversity of groups within the participant population to be directly addressed. This direct address broadens the range of results and does justice to the diversity of our society.