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Traffic4cast 2021 – Temporal and Spatial Few-Shot Transfer Learning in Traffic Map Movie Forecasting + Q&A
Moritz Neun · Christian Eichenberger · Henry Martin · Pedro Herruzo · David Jonietz · Fei Tang · Daniel Springer · Markus Spanring · Avi Avidan · Luis Ferro · Ali Soleymani · Rohit Gupta · Bo Xu · Kevin Malm · Aleksandra Gruca · Johannes Brandstetter · Michael Kopp · David Kreil · Sepp Hochreiter
Event URL: https://www.iarai.ac.at/traffic4cast/ »
Traffic is said to follow `hidden rules' that can be transferred across domain shifts. Our competition sets out to explore this meta topic with two few-shot learning tasks: predictions across a temporal shift brought about by COVID-19 and across a spatio-temporal shift in hitherto unseen cities. We provide an unprecedented, large data set from $10^{12}$ real world GPS probes in $10$ cities binned in space and time into multi-channel movie frames, as well as static data on the basic road connections of the underlying road network. Thus participants can approach these transfer tasks using graph based approaches encoding knowledge about the road network or approaches from computer vision like U-nets, which were highly successful in our previous competitions. Any advance in these questions will have a large impact on smart city planning, on mobility in general and thus, ultimately, our way of living more sustainably.
Traffic is said to follow `hidden rules' that can be transferred across domain shifts. Our competition sets out to explore this meta topic with two few-shot learning tasks: predictions across a temporal shift brought about by COVID-19 and across a spatio-temporal shift in hitherto unseen cities. We provide an unprecedented, large data set from $10^{12}$ real world GPS probes in $10$ cities binned in space and time into multi-channel movie frames, as well as static data on the basic road connections of the underlying road network. Thus participants can approach these transfer tasks using graph based approaches encoding knowledge about the road network or approaches from computer vision like U-nets, which were highly successful in our previous competitions. Any advance in these questions will have a large impact on smart city planning, on mobility in general and thus, ultimately, our way of living more sustainably.
Author Information
Moritz Neun (IARAI)
Christian Eichenberger (IARAI)
Henry Martin (ETH Zurich & IARAI)
Pedro Herruzo (Institute of Advanced Research in Artificial Intelligence)
David Jonietz (HERE Technologies)
Fei Tang (HERE Technologies)
Daniel Springer (IARAI)
Markus Spanring (IARAI)
Avi Avidan (IARAI)
Luis Ferro (Institute of Advanced Research in Artificial Intelligence)
Ali Soleymani (HERE Technologies)
Rohit Gupta (HERE Technologies B.V.)
Bo Xu (HERE Technologies)
Kevin Malm (HERE Technologies)
Aleksandra Gruca (Silesian University of Technology)
Johannes Brandstetter (LIT AI Lab / University Linz)
Michael Kopp (Institute of Advanced Research in Artificial Intelligence (IARAI) GmbH)
David Kreil (Institute of Advanced Research in Artificial Intelligence (IARAI))
Sepp Hochreiter (LIT AI Lab / University Linz / IARAI)
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