Deep-learning models for traffic data prediction can have superior performance in modeling complex functions using a multi-layer architecture. However, a major drawback of these approaches is that most of these approaches do not offer forecasts with uncertainty estimates, which are essential for traffic operations and control. Without uncertainty estimates, it is difficult to place any level of trust to the model predictions, and operational strategies relying on overconfident predictions can lead to worsening traffic conditions. In this study, we propose a Bayesian recurrent neural network framework for uncertainty quantification in traffic prediction with higher generalizability by introducing spectral normalization to its hidden layers. In our paper, we have shown that normalization alters the training process of deep neural networks by controlling the model's complexity and reducing the risk of overfitting to the training data. This, in turn, helps improve the generalization performance of the model on out-of-distribution datasets. Results demonstrate that spectral normalization improves uncertainty estimates and significantly outperforms both the layer normalization and model without normalization in single-step prediction horizons. This improved performance can be attributed to the ability of spectral normalization to better localize the feature space of the data under perturbations. Our findings are especially relevant to traffic management applications, where predicting traffic conditions across multiple locations is the goal, but the availability of training data from multiple locations is limited. Spectral normalization, therefore, provides a more generalizable approach that can effectively capture the underlying patterns in traffic data without requiring location-specific models.

翻译：用于交通数据预测的深度学习模型凭借其多层架构在复杂函数建模中表现出色。然而，这类方法的主要缺陷在于多数模型无法提供伴随不确定性估计的预测结果，而这对交通运行与控制至关重要。缺乏不确定性估计将导致模型预测难以建立信任基础，基于过度自信预测的运营策略可能反而加剧交通拥堵。本研究提出一种贝叶斯循环神经网络框架，通过在其隐藏层引入谱归一化技术，实现交通预测中的不确定性量化并提升模型泛化能力。实验表明，归一化通过控制模型复杂度并降低对训练数据的过拟合风险，能够改变深度神经网络的训练过程，进而提升模型在分布外数据集上的泛化性能。结果显示，谱归一化可优化不确定性估计，且在单步预测任务中显著优于层归一化及无归一化模型。这种性能提升归因于谱归一化能更有效地定位扰动下数据的特征空间。本研究对多地点交通状态预测场景具有特殊价值——尽管多地点训练数据有限，谱归一化仍可提供更强的泛化能力，无需针对不同地点建立独立模型即可有效捕捉交通数据的潜在规律。