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.

该研究提出了一种基于贝叶斯循环神经网络和谱归一化的交通预测模型框架，能够提供不确定性估计和更好地适应不同数据集的环境，从而在交通数据预测任务中提高了准确性。