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Traffic4cast competition reveals novel way to predict traffic flow using AINeurIPS, Vancouver – The Institute for Advanced Research in Artificial Intelligence (IARAI), an independent global machine-learning research institute established by HERE Technologies, today announced the results and winners of its traffic prediction competition, which aimed to solve mobility challenges using artificial intelligence (AI). Traffic4cast, a unique competition merging movie-prediction machine learning with traffic research, challenged competitors to understand complex traffic systems and make predictions about how they would flow in the future. The Traffic4cast results show that neural networks were the most effective method used at predicting traffic and came closest to simulating the exact traffic flow. All the top entrants used neural networks instead of “non-black box” solutions, such as suppor vector machines, Bayesian networks and other fixed algorithms. Winners from South Korea, Oxford/Zurich and Toronto were among more than 40 teams from around the world who submitted over 4,000 entries. Working with HERE, IARAI provided participants with traffic movie clips based on a year’s worth of industrial-scale, real-world data for three diverse cities: Berlin, Istanbul and Moscow. The clips were created using data based on an unprecedented number of over 100 billion probe points from positions reported by a large fleet of probe vehicles. They captured morning, evening and rush-hour traffic. Each movie frame summarized GPS trajectories mapped to spatio-temporal cells. The movies showed multiple color channels characterizing traffic volume, speed and direction. “This competition is special alone because of the sheer scope and size of the data,” said Sepp Hochreiter, a founding co-director of IARAI and an artificial intelligence pioneer (he invented the long short-term memory (LSTM) neural network framework). Entrants had to forecast the traffic by completing the next part of each movie clip for all three cities. Contestants were given 285 full training days (full movie for the entire day) and 72 testing days (containing five blocks of 12 consecutive images with at least 30 frames between each such block); the rest were marked out validation sets. Each contestant then had to produce the three consecutive images following each given block of 12 images in each movie file for each day in the test set for each city. “This competition brought together diverse groups to tackle a fundamental problem—predicting geospatial processes—that lies at the heart of sustainable mass mobility,” said Michael Kopp, head of research at HERE and founding co-director of IARAI. “Guiding the AI revolution to this problem using an interdisciplinary approach via billions of real-life data points is both novel and a paradigm shift that will be reflected in many applied scientific disciplines. The results seem to prove that ‘black box’ machine learning is most effective at solving predictive problems. This gives us a jumping-off point for further research into how AI learns.” Media Contacts Amy Stupavsky
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