Speaker: Dr. Daniel Weiss, German Aerospace Centre (DLR), Institute for Transport Research
Abstract: The transition toward sustainable mobility is a complex challenge that involves various sectors and layers of society. Despite concerted efforts from both policy and industry, we are still witnessing staggering progress. This suggests gaps in both our understanding of the associated innovation dynamics and the analytical methods currently used to study them. Notably, existing methods overlook the substantial volume of unstructured text data, which makes up 80-90% of currently available information and could offer novel insights for the analysis of innovation dynamics.
Against this backdrop, this presentation examines the potential of using Natural Language Processing (NLP) techniques to analyze innovation dynamics in the mobility transition process. The talk will address the conceptual underpinnings of public discourse dynamics and will explore the significant role that text data plays in shaping both public opinion and policy decisions. It will also present research findings on key technologies in the transportation sector.
The presentation concluded by advocating for the adoption of NLP as a cost-effective, timely, and comprehensive analytical tool. Its application not only promises to bridge existing analytical gaps but also offers a more nuanced understanding of innovation dynamics that can inform better-targeted policy measures and resource allocation.
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