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Accurate traffic forecasting is of the utmost importance for optimal travelplanning and for efficient city mobility. IARAI (The Institute of AdvancedResearch in Artificial Intelligence) organizes Traffic4cast, a yearly trafficprediction competition based on real-life data[https://www.iarai.ac.at/traffic4cast/], aiming to leverage artificialintelligence advances for producing accurate traffic estimates. We present oursolution to the IARAI Traffic4cast 2022 competition, in which the goal is todevelop algorithms for predicting road graph edge congestion classes andsupersegment-level travel times. In contrast to the previous years, this year'scompetition focuses on modelling graph edge level behaviour, rather than morecoarse aggregated grid-based traffic movies. Due to this, we leverage a methodfamiliar from tabular data modelling -- gradient-boosted decision treeensembles. We reduce the dimensionality of the input data representing trafficcounters with the help of the classic PCA method and feed it as input to aLightGBM model. This simple, fast, and scalable technique allowed us to winsecond place in the core competition. The source code and references to trainedmodel files and submissions are available at https://github.com/skandium/t4c22 .

Large scale traffic forecasting with gradient boosting, Traffic4cast 2022 challenge