This paper presents a novel methodology using classification for day-ahead traffic prediction. It addresses the research question whether traffic state can be forecasted based on meteorological conditions, seasonality, and time intervals, as well as COVID-19 related restrictions. We propose reliable models utilizing smaller data partitions. Apart from feature selection, we incorporate new features related to movement restrictions due to COVID-19, forming a novel data model. Our methodology explores the desired training subset. Results showed that various models can be developed, with varying levels of success. The best outcome was achieved when factoring in all relevant features and training on a proposed subset. Accuracy improved significantly compared to previously published work.

中文翻译：

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使用分类进行交通预测的方法：以 COVID-19 的影响为特色

本文提出了一种使用分类进行日前交通预测的新方法。它解决了是否可以根据气象条件、季节性和时间间隔以及 COVID-19 相关限制预测交通状况的研究问题。我们提出了使用较小数据分区的可靠模型。除了特征选择之外，我们还结合了与 COVID-19 引起的运动限制相关的新特征，形成了一个新的数据模型。我们的方法探索了所需的训练子集。结果表明，可以开发各种模型，并取得不同程度的成功。在考虑所有相关特征并对提议的子集进行训练时，取得了最佳结果。与以前发表的工作相比，准确性显着提高。