5 papers
UrbanFusion: Stochastic Multimodal Fusion for Contrastive Learning of Robust Spatial Representations
Dominik J. Mühlematter, Lin Che, Ye Hong +2
Forecasting urban phenomena such as housing prices and public health indicators requires the effective integration of various geospatial data. Current methods primarily utilize tas…
GeOT: A spatially explicit framework for evaluating spatio-temporal predictions
Nina Wiedemann, Théo Uscidda, Martin Raubal
When predicting observations across space and time, the spatial layout of errors impacts a model's real-world utility. For instance, in bike sharing demand prediction, error patter…
Spatially-aware station based car-sharing demand prediction
Dominik J. Mühlematter, Nina Wiedemann, Yanan Xin +1
In recent years, car-sharing services have emerged as viable alternatives to private individual mobility, promising more sustainable and resource-efficient, but still comfortable t…
Bike network planning in limited urban space
Nina Wiedemann, Christian Nöbel, Lukas Ballo +2
The lack of cycling infrastructure in urban environments hinders the adoption of cycling as a viable mode for commuting, despite the evident benefits of (e-)bikes as sustainable, e…
Vehicle-to-grid for car sharing -- A simulation study for 2030
Nina Wiedemann, Yanan Xin, Vasco Medici +3
The proliferation of car sharing services in recent years presents a promising avenue for advancing sustainable transportation. Beyond merely reducing car ownership rates, these sy…