12 citations · 41 across the 7 of their papers we have counts for
7 papers
Towards Autonomous Railway Operations: A Semi-Hierarchical Deep Reinforcement Learning Approach to the Vehicle Rescheduling Problem
Alberto Castagna, Stefan Zahlner, Adrian Egli +4
Managing disruptions in railway traffic management is a major challenge. Rising traffic density and infrastructure limits increase complexity, making the Vehicle Routing and Schedu…
Scope Restriction for Scalable Real-Time Railway Rescheduling: An Exploratory Study
Erik Nygren, Christian Eichenberger, Emma Frejinger
With the aim to stimulate future research, we describe an exploratory study of a railway rescheduling problem. A widely used approach in practice and state of the art is to decompo…
Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors
Moritz Neun, Christian Eichenberger, Henry Martin +27
The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem…
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…
Profiling and Improving the PyTorch Dataloader for high-latency Storage: A Technical Report
Ivan Svogor, Christian Eichenberger, Markus Spanring +2
A growing number of Machine Learning Frameworks recently made Deep Learning accessible to a wider audience of engineers, scientists, and practitioners, by allowing straightforward…
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…