Traffic forecasting is an important problem in the operation and optimisation of transportation systems. State-of-the-art solutions train machine learning models by minimising the mean forecasting errors on the training data. The trained models often favour periodic events instead of aperiodic ones in their prediction results, as periodic events often prevail in the training data. While offering critical optimisation opportunities, aperiodic events such as traffic incidents may be missed by the existing models. To address this issue, we propose DualCast -- a model framework to enhance the learning capability of traffic forecasting models, especially for aperiodic events. DualCast takes a dual-branch architecture, to disentangle traffic signals into two types, one reflecting intrinsic {spatial-temporal} patterns and the other reflecting external environment contexts including aperiodic events. We further propose a cross-time attention mechanism, to capture high-order spatial-temporal relationships from both periodic and aperiodic patterns. DualCast is versatile. We integrate it with recent traffic forecasting models, consistently reducing their forecasting errors by up to 9.6% on multiple real datasets.

翻译：交通预测是交通系统运营与优化中的一个重要问题。现有先进解决方案通过最小化训练数据上的平均预测误差来训练机器学习模型。由于周期性事件在训练数据中通常占据主导地位，训练后的模型在预测结果中往往更倾向于捕捉周期性事件而非非周期性事件。尽管非周期性事件（如交通事故）能提供关键的优化机会，但现有模型可能会将其遗漏。为解决这一问题，我们提出了DualCast——一个旨在增强交通预测模型学习能力（尤其是对非周期性事件的学习能力）的模型框架。DualCast采用双分支架构，将交通信号解耦为两种类型：一类反映内在的{时空}模式，另一类反映包括非周期性事件在内的外部环境背景。我们进一步提出了一种跨时间注意力机制，以从周期性和非周期性模式中捕获高阶时空关系。DualCast具有通用性。我们将其与近期先进的交通预测模型结合，在多个真实数据集上持续将预测误差降低了最高达9.6%。