165 citations · 411 across the 13 of their papers we have counts for
17 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…
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…
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…
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…
Metropolitan Segment Traffic Speeds from Massive Floating Car Data in 10 Cities
Moritz Neun, Christian Eichenberger, Yanan Xin +7
Traffic analysis is crucial for urban operations and planning, while the availability of dense urban traffic data beyond loop detectors is still scarce. We present a large-scale fl…
National-scale bi-directional EV fleet control for ancillary service provision
Lorenzo Nespoli, Nina Wiedemann, Esra Suel +3
Deploying real-time control on large-scale fleets of electric vehicles (EVs) is becoming pivotal as the share of EVs over internal combustion engine vehicles increases. In this pap…
Training Efficient Controllers via Analytic Policy Gradient
Nina Wiedemann, Valentin Wüest, Antonio Loquercio +3
Control design for robotic systems is complex and often requires solving an optimization to follow a trajectory accurately. Online optimization approaches like Model Predictive Con…
Trackintel: An open-source Python library for human mobility analysis
Henry Martin, Ye Hong, Nina Wiedemann +2
Over the past decade, scientific studies have used the growing availability of large tracking datasets to enhance our understanding of human mobility behavior. However, so far data…
Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes
Christian Eichenberger, Moritz Neun, Henry Martin +34
The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggrega…
Traffic Forecasting on Traffic Moving Snippets
Nina Wiedemann, Martin Raubal
Advances in traffic forecasting technology can greatly impact urban mobility. In the traffic4cast competition, the task of short-term traffic prediction is tackled in unprecedented…
An Optimization Framework for Power Infrastructure Planning
Nina Wiedemann, David Adjiashvili
The ubiquitous expansion and transformation of the energy supply system involves large-scale power infrastructure construction projects. In the view of investments of more than a m…
Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis
Jannis Born, Nina Wiedemann, Gabriel Brändle +3
Controlling the COVID-19 pandemic largely hinges upon the existence of fast, safe, and highly-available diagnostic tools. Ultrasound, in contrast to CT or X-Ray, has many practical…
POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS)
Jannis Born, Gabriel Brändle, Manuel Cossio +4
With the rapid development of COVID-19 into a global pandemic, there is an ever more urgent need for cheap, fast and reliable tools that can assist physicians in diagnosing COVID-1…
A Tracking System For Baseball Game Reconstruction
Nina Wiedemann, Carlos Dietrich, Claudio T. Silva
The baseball game is often seen as many contests that are performed between individuals. The duel between the pitcher and the batter, for example, is considered the engine that dri…