Join us for a talk by Silke Kaiser on exploring the application of Graph Neural Networks (GNNs) in analyzing urban cycling volume.
Abstract from speaker:
In contrast to conventional machine learning techniques, Graph Neural Networks (GNNs) possess the unique capability to account for spatial dependencies. In this presentation, I will introduce an early-stage research project that explores the application of GNNs in analyzing urban cycling volume. GNNs combine graph theory with neural networks, allowing the incorporation of spatial dependencies and other similarities through advanced weighting techniques, including the use of multi-GNNs. This modeling approach has transformed the analysis of motorized traffic and has already found successful applications in bike-sharing systems. My study aims to extend this innovative methodology to estimate bicycle volumes, specifically focusing on urban mobility data from the cities Berlin and Copenhagen. Through this research, I seek to uncover the potential of GNNs in revolutionizing our understanding of urban cycling patterns.
Bring your own lunch bag! Light pastries and drinks will be available in case you forget to bring it.
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The Data Science Brown Bag Series is an informal and interactive gathering where participants bring their own brown bag lunch and engage in discussions on research and insights the field of data and computational social science (light pastries and drinks will be available if you forget your lunch bag!).
The series provides a platform for data enthusiasts, researchers, and practitioners to share their experiences, best practices, and emerging methodologies and research in using data science to analyze and understand social and political phenomena. The brown bag talk series is for anyone interested in data science and social science to network, learn, and share ideas in a casual and friendly setting.
