Traffic4cast 2022 Competition Calls On Machine Learning Community To Investigate The Complex Dynamics Of Road Traffic Congestion

• Participants tasked to model and forecast traffic congestion across 3 global cities

• Represents unique real-world, graph-based prediction problem

• Winners to be presented at prestigious NeurIPS 2022 conference

The Institute of Advanced Research in Artificial Intelligence (IARAI), an independent global machine learning research institute, has announced the opening of its 4^th annual Traffic4cast competition in collaboration with HERE Technologies. Participants are challenged to utilize the latest in AI methods to model and predict future traffic congestion levels and vehicle speeds across London, Madrid and Melbourne.

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The IARAI Traffic4cast competition is unique in merging AI with real-world datasets and traffic research to advance the understanding of complex traffic dynamics and systems. Winners will receive prizes at NeurIPS 2022, the leading conference in AI, and a volume within the Proceedings of Machine Learning Research will highlight contributions of the Traffic4cast 2022 Competition.

The competition includes two years of real-world data provided by HERE Technologies, derived from billions of GPS points from vehicle fleets. New this year, participants will leverage data from traffic loop counters embedded in the roadways of London, Madrid and Melbourne. The aim is to reduce the barriers for using readily available, public loop counter data to predict future traffic state of entire cities.

Traffic4cast 2022

• Core challenge – participants are asked to predict the congestion level classes (red/yellow/green) for the entire road graph 15 minutes into the future from the past hour of traffic loop counter data only.
• Extended challenge – participants are invited to predict the average speeds on each road segment of the graph 15 minutes into the future from the past hour of traffic loop counter data only.

HERE will provide participants with traffic movie clips based on two years of real-world data for London, Madrid and Melbourne. The clips were created using HERE data based on more than 100 billion GPS probe points from a large fleet of vehicles. The data has been fully anonymized and transformed into high-definition movie clips that, frame by frame, depict traffic over time, including morning, evening and rush hour traffic events.

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The competition brings together researchers across the globe from several prominent areas of machine learning, including graph-based modeling, transfer learning, deep learning, and time series prediction. The competition last year focused on the time-domain shift in traffic due to COVID-19.

Sepp Hochreiter, a founding co-director of IARAI and an artificial intelligence pioneer, who invented the long short-term memory (LSTM) neural network architecture, said: “Building on the three years of success at NeurIPS 2019–2021, Traffic4cast continues to improve our understanding of complex traffic systems. This year, researchers of modern machine learning will predict traffic congestion in entire cities just from the vehicle counters available on selected points. Besides traffic congestion, advanced models are supposed to also predict the average speeds on a network of roads. Our competition will help to advance and to exploit the latest techniques in machine learning like graph neural networks or physics inspired neural networks.”

“Traffic congestion is a universal challenge that requires deep analysis to understand ‘the hidden rules’ shaping vehicle movements. I am therefore excited to see what predictive models this year’s participants can generate using this expansive new dataset from HERE and the latest advances in AI and machine learning,” said Reinhard Köhn, Head of Research at HERE Technologies.

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