Second Traffic4cast Competition Demonstrates Value of AI in Revolutionizing Understanding of Mobility

AI-based predictive models can optimize the planning of smarter cities and more efficient road networks

Results to be presented at the Conference on Neural Information Processing Systems (NeurIPS), the leading global machine learning eventNeurIPS, Vancouver – The Institute for Advanced Research in Artificial Intelligence (IARAI), an independent global machine learning research institute established by HERE Technologies, announced the results and winners of the second edition of its Traffic4cast competition. The goal of the competition is to predict traffic in multiple, global, major cities with varying cultural and economic backdrops, based on industrial-scale traffic data provided by HERE.

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Traffic congestion is an issue for cities worldwide and it comes with a negative social and environmental impact. Making mobility more sustainable remains one of the biggest challenges of our times. The Traffic4cast competition series aims to get to the core of this issue by challenging participants to understand complex traffic systems, with the goal of predicting their future states. Advanced traffic prediction is enabled by capturing simple, implicit rules underlying a complex system and modeling its behavior. As a result, superior AI-based predictive models can optimize the planning of smarter cities and more efficient road networks.

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“Our design for this year’s competition was heavily influenced by what we learnt from Traffic4cast 2019. This year, we provided more granular data and doubled down on the question of whether ancillary static and dynamic data, like the number of restaurants in each cell and the number road incidents we observed, would improve the results or give rise to other creative winning architectures. The results surprised us on both fronts,” said Michael Kopp, Head of Research at HERE and founding co-director of IARAI. “Through our bonus challenge we also saw approaches that can translate between the dynamics we observe in traffic and ancillary dynamics dependent on it. As a result, questions of how weather and traffic movements are related or whether we can predict the movement of different classes of road vehicles, such as two-wheelers by observing mostly four-wheelers, through this gridded spatio-temporal aggregation approach can now be tackled.”

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