Scientific Committee

Committee members:

Prof. Fakhreddine (Fakhri) KarrayUniversity of Waterloo, Canada

Fakhri Karray is a University Research Chair Professor and a Loblaws Research Chair in Artificial Intelligence in the Department of Electrical and Computer Engineering at the University of Waterloo, Canada. He is also co-Director of the Waterloo Artificial Intelligence Institute. He received his Ph.D. at the University of Illinois-Urbana-Champaign in 1989. His research interests are in the areas of operational artificial intelligence, autonomous machines, cognitive robotics, natural human-machine interaction, intelligent systems, and voice and media concept extraction.

David Levinson

Prof. David P. Levinson, University of Sydney, Australia

David Levinson is a Professor of Transport Engineering at the School of Civil Engineering at the University of Sydney. He is an honorary affiliate of the Institute of Transport and Logistics Studies, where he is also a member of the Board of Advice. He received his Ph.D. in Transportation Engineering at the University of California at Berkeley in 1998. His research interests span transport, from engineering and design, through policy and planning, to geography and economics. His most recent research emphasizes transport-land use interactions, accessibility, and transport system evolution.

Xiaolei Ma

Prof. Xiaolei Ma, Beihang University, China

Xiaolei Ma is an Associate Professor in the department of Transportation Engineering at the School of Transportation Science and Engineering at the Beihang University in Beijing. He received his Ph.D. from the Civil and Environmental Engineering department at the University of Washington, Seattle in 2013. His research interests mainly lie in urban transit operation and optimization, data-driven transportation network modeling, and transportation data mining.


Dr. Piotr Mirowski, Deep Mind, UK

Piotr Mirowski is a staff research scientist at DeepMind, where he is a member of Deep Learning department and Dr. R. Hadsell’s team. His work focuses on navigation-related research and scaling up autonomous agents to real world environments. He obtained his Ph.D. in Computer Science at New York University in 2011. His previous work experience encompasses epileptic seizure prediction from EEG, the inference of gene regulation networks, WiFi-based geo-localization, simultaneous localization and mapping on a smartphone, robotics, natural language processing, and search query auto-completion.

Razvan Pascanu

Dr. Razvan Pascanu, Deep Mind, UK

Razvan Pascanu is a research scientist at DeepMind. He received his Ph.D. from the University of Montreal in 2014. His research mainly focuses on deep learning and deep reinforcement learning. His research interests include theory of deep neural networks, graph neural networks, memory and recurrent neural networks, and learning with multiple tasks from continual learning to transfer learning, multi-task, curriculum or meta-learning.

Martin Raubal

Prof. Martin Raubal, ETH Zürich, Switzerland

Martin Raubal is a Professor for Geoinformation Engineering in the Department of Civil, Environmental and Geomatic Engineering at the Institute of Cartography and Geoinformation at ETH Zürich. He received his Ph.D. at the Vienna University of Technology in 2001. His main research interest is geographic information science, specializing in mobile geographic information systems (GIS) and spatial information technologies. His research group focuses on GIS and location-based services (LBS), which include navigation devices, information and emergency services, or generally services that provide the user with information on the current location.


Prof. Cyrus Shahabi, University of Southern California, USA

Cyrus Shahabi is a Professor of Computer Science, Electrical Engineering and Spatial Sciences, Helen N. and Emmett H. Jones Professor, the chair of the Computer Science Department, and the director of the Integrated Media Systems Center at USC’s Viterbi School of Engineering. He is also a co-founder of two USC spin-offs, Geosemble Technologies and Tallygo. He received his Ph.D. in Computer Science from the University of Southern California in 1996. His research focused on geospatial and multidimensional data management, including traffic prediction, spatial crowdsourcing, geo-social networks, privacy in location-based services, video data management, and many more.


Prof. Leonid Sigal, University of British Columbia, Canada

Leonid Sigal is an Associate Professor in the Department of Computer Science at the University of British Columbia. He is also a Canada Research Chair (CRC II) in Computer Vision and Machine Learning and a remote Faculty Member of the Vector Institute for AI in Toronto. He received his Ph.D. in Computer Science at Brown University in 2008. His research focuses on problems of visual understanding and reasoning, including object recognition, scene understanding, articulated motion capture, motion modeling, action recognition, motion perception, manifold learning, transfer learning, character and cloth animation, and a number of other directions on the intersection of computer vision, machine learning, and computer graphics.

Eleni Vlahogianni

Prof. Eleni I. Vlahogianni, National Technical University of Athens, Greece

Eleni Vlahogianni is an Associate Professor at the Department of Transportation Planning and Engineering of the National Technical University of Athens. She received her Ph.D. in Traffic Operations at National Technical University of Athens in 2005. Her primary research field is traffic flow analysis and forecasting. Other research fields include mobility modeling, driving analytics, ICT applications to transportation, intelligent transportation systems, traffic management, advanced technologies for monitoring transportation infrastructures.


Sungbin Choi

Score: 0.0011628615462311


10 000 USD award

12 month Research Fellowship at IARAI (covering stipend and expenses)


Score:  0.0011667194914317

Team members

Fanyou Wu(1), Yang Liu(2), Zhiyuan Liu(2), Xiaobo Qu(3), Rado Gazo(4), Eva Haviarova(4)


1 Purdue University, West Lafayette, US
2 Southeast Univeristy, Nanjing, China
3 Chalmers University of Technology, Gothenburg, Sweden
4 Purdue University, West Lafayette, US


5 000 USD award 

12 month Research Fellowship at IARAI (covering stipend and expenses)

LDS Group

Score:  0.0011686813831425

Team members

Jingwei Xu(1), Jianjin Zhang(2), Zhiyu Yao(3), Yunbo Wang(1)


1 Shanghai Jiao Tong University, Shanghai, China
2 Microsoft, Beijing, China
3 Tsinghua University, Beijing, China


2 000 USD award

Complimentary registration for NeurIPS 2021


Traffic4cast is chosen again this year for the prestigious Neural Information Processing Systems (NeurIPS) conference, the leading event in machine learning.

NeurIPS 2020 is a virtual-only event. It runs from Sunday, December 6 through Saturday, December 12. 

Conference program

Traffic4cast session at NeurIPS 2020 competition track

Friday, December 11, 2020

16:00 PST (1:00 December 12 CET)

This session features a summary of the Traffic4cast 2020 core competition. Each of the winners of the competition presents a highlight talk. Two additional highlight talks are selected by the Scientific Committee from the submitted abstracts for their exceptional scientific input.

An extended in-depth discussion of the competition results will be presented at the Traffic4cast 2020 Special Session on Friday, December 11 in two parts at 9:00 CET (0:00 PST) and 17:00 CET (8:00 PST).

Traffic Map Movies - An Introduction to the Traffic4cast ChallengeSepp HochreiterIARAI, Johannes Kepler University5 min
The Traffic4cast Competition Design and DataMichael KoppIARAI, HERE Technologies5 min
The Best Traffic4Cast SubmissionsDavid KreilIARAI2 min
1st prize: Utilizing UNet for the Future Traffic Map Prediction - highlight talkSungbin Choi 5 min
2nd prize: TLab: Traffic Map Movie Forecasting Based on HR-NET - highlight talkFanyou WuPurdue University5 min
3rd prize: Towards Good Practices of U-Net for Traffic Forecasting - highlight talkJingwei XuShanghai Jiao Tong University5 min
Graph Ensemble Net and the Importance of Feature & Loss Function Design for Traffic Prediction - highlight talkQi Qi 5 min
Uncertainty Intervals for Graph-based Spatio-Temporal Traffic Prediction - highlight talkTijs Maas University of Amsterdam5 min
Traffic4cast Award Ceremony, Outlook, and Follow Up ChallengesDavid KreilIARAI8 min


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