Accurate traffic flow forecasting is crucial for intelligent transportation services such as navigation and ride-hailing. In such applications, uncertainty estimation in forecasting is important because it helps evaluate traffic risk levels, assess forecast reliability, and provide timely warnings. As a result, probabilistic traffic flow forecasting (PTFF) has gained significant attention, as it produces both point forecasts and uncertainty estimates. However, existing PTFF approaches still face two key challenges: (1) how to uncover and model the causes of traffic flow uncertainty for reliable forecasting, and (2) how to capture the spatiotemporal correlations of uncertainty for accurate prediction. To address these challenges, we propose RIPCN, a Road Impedance Principal Component Network that integrates domain-specific transportation theory with spatiotemporal principal component learning for PTFF. RIPCN introduces a dynamic impedance evolution network that captures directional traffic transfer patterns driven by road congestion level and flow variability, revealing the direct causes of uncertainty and enhancing both reliability and interpretability. In addition, a principal component network is designed to forecast the dominant eigenvectors of future flow covariance, enabling the model to capture spatiotemporal uncertainty correlations. This design allows for accurate and efficient uncertainty estimation while also improving point prediction performance. Experimental results on real-world datasets show that our approach outperforms existing probabilistic forecasting methods.

翻译：准确的交通流预测对于导航和网约车等智能交通服务至关重要。在此类应用中，预测中的不确定性估计具有重要意义，因为它有助于评估交通风险水平、衡量预测可靠性并提供及时预警。因此，概率交通流预测（PTFF）因其能同时提供点预测和不确定性估计而受到广泛关注。然而，现有PTFF方法仍面临两个关键挑战：（1）如何揭示并建模交通流不确定性的成因以实现可靠预测；（2）如何捕捉不确定性的时空相关性以进行准确预测。为解决这些挑战，我们提出RIPCN——一种融合领域特定交通理论与时空主成分学习的概率交通流预测网络。RIPCN引入动态阻抗演化网络，该网络能捕捉由道路拥堵水平和流量波动驱动的定向交通转移模式，从而揭示不确定性的直接成因，并同时提升预测的可靠性与可解释性。此外，网络设计了主成分预测模块，用于预测未来流量协方差矩阵的主导特征向量，使模型能够捕捉时空不确定性相关性。该设计在实现准确高效不确定性估计的同时，也提升了点预测性能。在真实数据集上的实验结果表明，本方法优于现有概率预测方法。