128 citations · 323 across the 13 of their papers we have counts for
6 papers · 1 filter
A causal intervention framework for synthesizing mobility data and evaluating predictive neural networks
Ye Hong, Yanan Xin, Simon Dirmeier +2
Deep neural networks are increasingly utilized in mobility prediction tasks, yet their intricate internal workings pose challenges for interpretability, especially in comprehending…
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…
Where you go is who you are -- A study on machine learning based semantic privacy attacks
Nina Wiedemann, Ourania Kounadi, Martin Raubal +1
Concerns about data privacy are omnipresent, given the increasing usage of digital applications and their underlying business model that includes selling user data. Location data i…
Uncertainty Quantification for Image-based Traffic Prediction across Cities
Alexander Timans, Nina Wiedemann, Nishant Kumar +2
Despite the strong predictive performance of deep learning models for traffic prediction, their widespread deployment in real-world intelligent transportation systems has been rest…
Evaluating geospatial context information for travel mode detection
Ye Hong, Emanuel Stüdeli, Martin Raubal
Detecting travel modes from global navigation satellite system (GNSS) trajectories is essential for understanding individual travel behavior and a prerequisite for achieving sustai…
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…