Predicting traffic conditions is tremendously challenging since every road is highly dependent on each other, both spatially and temporally. Recently, to capture this spatial and temporal dependency, specially designed architectures such as graph convolutional networks and temporal convolutional networks have been introduced. While there has been remarkable progress in traffic forecasting, we found that deep-learning-based traffic forecasting models still fail in certain patterns, mainly in event situations (e.g., rapid speed drops). Although it is commonly accepted that these failures are due to unpredictable noise, we found that these failures can be corrected by considering previous failures. Specifically, we observe autocorrelated errors in these failures, which indicates that some predictable information remains. In this study, to capture the correlation of errors, we introduce ResCAL, a residual estimation module for traffic forecasting, as a widely applicable add-on module to existing traffic forecasting models. Our ResCAL calibrates the prediction of the existing models in real time by estimating future errors using previous errors and graph signals. Extensive experiments on METR-LA and PEMS-BAY demonstrate that our ResCAL can correctly capture the correlation of errors and correct the failures of various traffic forecasting models in event situations.

翻译：预测交通条件具有巨大的挑战性,因为每一条道路在空间和时间上都高度依赖对方。最近,为了捕捉这种空间和时间依赖性,引入了特别设计的建筑,如图集网络和时变网络。虽然在交通预测方面取得了显著进展,但我们发现,在某些模式中,基于深学习的交通预测模型仍然失败,主要是在各种情况中(例如快速下降),虽然人们普遍认为,这些失败是由于不可预测的噪音造成的,但我们发现,这些失败可以通过考虑以前的失败来纠正。具体地说,我们观察到这些失败中与自动有关的错误,表明一些可预测的信息仍然存在。在这项研究中,为了捕捉错误的关联性,我们引入了ResCAL,即交通预报的残余估计模块,作为现有交通预测模型的一个广泛适用的附加模块。我们的ResCAL通过使用以前的错误和图形信号来实时估计现有模型的预测。我们对METR-LA和PEMS-BAY进行的广泛实验表明,我们的ResCAL能够正确捕捉到错误的关联性,并纠正各种交通预测模式的失误。