Transportation and transit governance are highly technical practices. Agencies rely on a vast array of science and engineering inputs to design infrastructure, site and plan operations, and make policies. Revolutions in transportation with automation, electrification, and shared mobility are going to be shaped by how and where agencies access scientific information about these emergent technologies. Moreover, unintended consequences and distributional inequities will likely occur where blind spots exist, and agencies do not incorporate relevant information. By showing where and when science (including research, patents, and tools) is used in transportation governance, we can better understand temporal and spatial patterns of innovation and diffusion, observe patterns of technology transfer from research to practice, and diagnose key informational gaps. While the nature of emergent transportation technologies is of course uncertain, by analyzing historical and ongoing trends in where and how public managers access and use science, we can better understand innovation adoption and how future innovations are likely to unfold. In turn, by showing where public managers access scientific information and the characteristics of science that managers access, researchers and science-policy boundary organizations (e.g., ITS) can increase the uptake of scientific products and design network interventions to improve information access.
First, we will analyze statistical trends in science-use by individual agencies in terms of the bibliometric measures described above (quality, breadth of focus, novelty), both over time and for different project areas. Second, we will analyze the use of science and technology by different agencies related to key emerging technologies (e.g., electrification, automation, mobile data) to understand contextual drivers of innovation (e.g., do agencies operating near major research universities, or in areas with a higher rate of relevant patent filings, tend to incorporate more relevant and recent science in planning and decision-making?). Third, we will assess publication impact, analogous to citation counts and impact factors in academic publications but based on references in transportation policy and planning documents. For instance, which publications, authors, organizations, and journals have the greatest impact on transportation policymaking? Finally, we will generate a bibliometric network that shows flows of information between research organizations and transportation/transit agencies. Using temporal graph-based modeling approaches (e.g., generalized exponential random graph models, latent order logistic regression models), we will model structural properties and growth of science and technology transfer over time.
These models will identify: (1) core information nodes and central actors; (2) developing patterns of information flow (e.g., increasing connections between electrification and transportation technologies); and (3) existing structural gaps where science is not currently bridging to applications.