论文标题
大规模交通预测，梯度提升，流量4cast 2022挑战
Large scale traffic forecasting with gradient boosting, Traffic4cast 2022 challenge
论文作者
论文摘要
准确的交通预测对于最佳旅行计划和有效的城市流动性至关重要。 IARAI（人工智能高级研究所）组织了流量4Cast，这是一项基于现实数据[https://www.iarai.ac.at/traffic4cast/]的年度交通预测竞赛，旨在利用人工智能进步来产生准确的交通估算。我们将解决方案介绍给IARAI Acrivic4cast 2022竞赛，其中的目标是开发用于预测Road Graph Edge Edge拥塞课程和超级级别旅行时间的算法。与前几年相反，今年的竞争重点是建模图边缘级别的行为，而不是基于更粗的基于网格的交通电影。因此，我们利用一种熟悉表格数据建模的方法 - 提高梯度的决策树组合。我们在经典PCA方法的帮助下降低了代表流量计数器的输入数据的维度，并将其作为输入为LightGBM模型。这种简单，快速和可扩展的技术使我们能够在核心竞争中赢得第二名。源代码和对训练有素的模型文件和提交的引用可在https://github.com/skandium/t4c22上找到。
Accurate traffic forecasting is of the utmost importance for optimal travel planning and for efficient city mobility. IARAI (The Institute of Advanced Research in Artificial Intelligence) organizes Traffic4cast, a yearly traffic prediction competition based on real-life data [https://www.iarai.ac.at/traffic4cast/], aiming to leverage artificial intelligence advances for producing accurate traffic estimates. We present our solution to the IARAI Traffic4cast 2022 competition, in which the goal is to develop algorithms for predicting road graph edge congestion classes and supersegment-level travel times. In contrast to the previous years, this year's competition focuses on modelling graph edge level behaviour, rather than more coarse aggregated grid-based traffic movies. Due to this, we leverage a method familiar from tabular data modelling -- gradient-boosted decision tree ensembles. We reduce the dimensionality of the input data representing traffic counters with the help of the classic PCA method and feed it as input to a LightGBM model. This simple, fast, and scalable technique allowed us to win second place in the core competition. The source code and references to trained model files and submissions are available at https://github.com/skandium/t4c22 .
