Join us for a talk by Silke Kaiser on her research into using machine learning to predict bicycle counter in Berlin. This is part of the Data Science Brown Bag series.
Abstract from the speaker:
A higher share of cycling in cities can lead to a reduction in greenhouse gas emissions, a decrease in noise pollution, and personal health benefits. Data-driven approaches to planning new infrastructure to promote cycling are rare, mainly because data on cycling volume are only available selectively. By leveraging new and more granular data sources, I predict bicycle count measurements in Berlin, using data from free-floating bike-sharing systems and Strava data with Machine Learning. My goal is to ultimately predict traffic volume on all streets beyond those with counters and to understand the variance in feature importance across time and space. Therefore, also an interpretable analysis using SHAP will be discussed.
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.
