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 tackles this problem in a data-driven way, advancing the latest methods in machine learning for modeling complex spatial systems over time. In this edition, our dynamic road graph data combine information from road maps, $10^{12}$ probe data points, and stationary vehicle detectors in three cities over the span of two years. While stationary vehicle detectors are the most accurate way to capture traffic volume, they are only available in few 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. In the core challenge, participants are invited to predict the likelihoods of three congestion classes derived from the speed levels in the GPS data for the entire road graph in three cities 15 min into the future. We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task. The data are aggregated in 15 min time bins for one hour prior to the prediction time. For the extended challenge, participants are tasked to predict the average travel times on super-segments 15 min into the future - super-segments are longer sequences of road segments in the graph. The competition results provide an important advance in the prediction of complex city-wide traffic states just from publicly available sparse vehicle data and without the need for large amounts of real-time floating vehicle data.

翻译：城市化与个人出行便利性的全球趋势迫使我们重新思考生活方式和城市空间利用。Traffic4cast竞赛系列以数据驱动方式应对这一挑战，推动机器学习方法在复杂时空系统建模中的前沿发展。本届竞赛中，我们的动态路网图数据融合了道路地图、$10^{12}$个探测数据点以及三个城市两年间固定车辆检测器的信息。尽管固定车辆检测器是捕捉交通流量的最精确方式，但其部署位置有限。Traffic4cast 2022探索模型能力——利用仅分布在少数节点的松散时序顶点数据，预测整个路网图边上的未来动态交通状态。核心挑战环节要求参赛者基于GPS数据中的速度等级，预测三个城市整个路网图在未来15分钟内三类拥堵概率（源自速度等级）。任务仅提供这三个城市空间稀疏分布的固定车辆检测器采集的车辆计数数据作为模型输入，数据按预测时间前1小时内的15分钟时间窗口进行聚合。扩展挑战环节则要求参赛者预测未来15分钟超路段（图中由连续道路段构成的长序列）的平均通行时间。竞赛结果表明，仅利用公开的稀疏车辆数据，无需大量实时浮动车数据即可实现复杂城市级交通状态预测，这标志着该领域的重要进展。