Spatiotemporal graph neural networks have achieved state-of-the-art performance in traffic forecasting. However, they often struggle to forecast congestion accurately due to the limitations of traditional loss functions. While accurate forecasting of regular traffic conditions is crucial, a reliable AI system must also accurately forecast congestion scenarios to maintain safe and efficient transportation. In this paper, we explore various loss functions inspired by heavy tail analysis and imbalanced classification problems to address this issue. We evaluate the efficacy of these loss functions in forecasting traffic speed, with an emphasis on congestion scenarios. Through extensive experiments on real-world traffic datasets, we discovered that when optimizing for Mean Absolute Error (MAE), the MAE-Focal Loss function stands out as the most effective. When optimizing Mean Squared Error (MSE), Gumbel Loss proves to be the superior choice. These choices effectively forecast traffic congestion events without compromising the accuracy of regular traffic speed forecasts. This research enhances deep learning models' capabilities in forecasting sudden speed changes due to congestion and underscores the need for more research in this direction. By elevating the accuracy of congestion forecasting, we advocate for AI systems that are reliable, secure, and resilient in practical traffic management scenarios.

翻译：时空图神经网络在交通预测中已达到最先进的性能。然而，由于传统损失函数的局限性，这些模型在准确预测拥堵时往往存在困难。虽然准确预测常规交通状况至关重要，但一个可靠的人工智能系统还必须能够准确预测拥堵场景，以维持安全高效的交通运行。在本文中，我们探索了受重尾分布分析和非均衡分类问题启发的多种损失函数来解决这一问题。我们评估了这些损失函数在预测交通速度方面的有效性，特别关注拥堵场景。通过在真实世界交通数据集上的广泛实验，我们发现，在优化平均绝对误差（MAE）时，MAE-Focal损失函数最为有效；而在优化均方误差（MSE）时，Gumbel损失函数则是更优选择。这些选择能够在不影响常规交通速度预测准确性的前提下，有效预测交通拥堵事件。本研究提升了深度学习模型在预测因拥堵引起的速度突变方面的能力，并强调了在这一方向上开展更多研究的必要性。通过提高拥堵预测的准确性，我们倡导在现实交通管理场景中建立可靠、安全且具有韧性的人工智能系统。