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Traffic4cast 2022 – Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from simple Road Counters

Moritz Neun · Christian Eichenberger · Michael Kopp · David Kreil · Sepp Hochreiter



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 tackle this problem in a data driven way, advancing the latest methods in modern machine learning for modelling complex spatial systems over time. This year, our dynamic road graph data combine information from road maps, 10^12 location probe data points, and car loop counters in three entire cities for two years. While loop counters are the most accurate way to capture the traffic volume they are only available in some locations. Traffic4cast 2022 explores models that have the ability to generalize loosely related temporal vertex data on just a few nodes to predict dynamic future traffic states on the edges of the entire road graph. Specifically, in our core challenge we invite participants to predict for three cities the congestion classes known from the red, yellow, or green colouring of roads on a common traffic map for the entire road graph 15min into the future. We provide car count data from spatially sparse loop counters in these three cities in 15min aggregated time bins for one hour prior to the prediction time slot. For our extended challenge participants are asked to predict the actual average speeds on each road segment in the graph 15min into the future.