The previous chapters have explored how people can view the same transport phenomena and come away with varying, even oppositional decisions about their origins, impact on society, and ways to improve outcomes. Reflecting on the case studies, this chapter first contrasts issues in sequencing data and findings, recognizing that prioritizing datasets has implications on later analysis and possibly, conclusions. Mixed data can be integrated at different levels, as well. A first approach for researchers may prioritize the tradition with which the researcher is most familiar. However, a fully integrated approach of simultaneous quantitative-qualitative data collection and analysis may reveal connections between each strand of data, enabling real-time adjustments that better reflect realities. This chapter includes a typology of mixed methods research designs, shown in Figure 7.1, that will aid alignment of knowledge to action. As an example of an emerging approach, I focus on how crowdsourcing—a digital approach to gather knowledge from a large group—can support transport planning in a co-productive approach. In co-production, local participants and planners are considered equals in the production of knowledge, development of solutions, and implementation—when the toolsets and power dynamics are shared. This approach can nonetheless result in conflicting answers to the same problems, which is why this approach for triangulating knowledge may be so powerful. Following recent guidance on integrating mixed methods (Creamer, 2018), this chapter shows how researchers can construct meta-inferences from divergent approaches to transport problems.