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.

本研究针对现有交通预测模型偏向于周期性事件的问题，提出了DualCast模型框架，旨在提高对非周期性事件（如交通事故）的预测能力。该模型通过双分支架构分离交通信号，结合跨时间注意机制，有效捕捉到高阶的空间-时间关系，实验中显示在多个真实数据集上的预测误差最多降低了9.6%。

DualCast：利用双分支模型从交通系列中解耦非周期性事件