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：面向交通数据预测的深度学习模型，通过采用多层架构在复杂函数建模方面展现出卓越性能。然而，这类方法的主要缺陷在于，大多数方法未能提供包含不确定性估计的预测结果——而这一特性对于交通运行与控制至关重要。缺乏不确定性估计会使模型预测难以获得任何程度的信任，依赖过度自信预测的运行策略反而可能导致交通状况恶化。本研究提出一种贝叶斯循环神经网络框架，通过引入谱归一化至其隐藏层，实现交通预测中兼具更高泛化能力的不确定性量化。研究表明，归一化通过控制模型复杂度并降低训练数据过拟合风险，能够改变深度神经网络的训练过程，进而帮助提升模型在分布外数据集上的泛化性能。实验结果表明，谱归一化能够改善不确定性估计，在单步预测场景中显著优于层归一化及无归一化模型。这种性能提升可归因于谱归一化在数据扰动条件下，对特征空间进行更优局域化的能力。本研究发现对交通管理应用具有特殊价值——当预测目标是多地点交通状况但多地点训练数据有限时，谱归一化提供了一种无需位置特异性模型即可有效捕捉交通数据潜在模式的更通用方案。