Deep learning (DL) models for spatio-temporal traffic flow forecasting employ convolutional or graph-convolutional filters along with recurrent neural networks to capture spatial and temporal dependencies in traffic data. These models, such as CNN-LSTM, utilize traffic flows from neighboring detector stations to predict flows at a specific location of interest. However, these models are limited in their ability to capture the broader dynamics of the traffic system, as they primarily learn features specific to the detector configuration and traffic characteristics at the target location. Hence, the transferability of these models to different locations becomes challenging, particularly when data is unavailable at the new location for model training. To address this limitation, we propose a traffic flow physics-based feature transformation for spatio-temporal DL models. This transformation incorporates Newell's uncongested and congested-state estimators of traffic flows at the target locations, enabling the models to learn broader dynamics of the system. Our methodology is empirically validated using traffic data from two different locations. The results demonstrate that the proposed feature transformation improves the models' performance in predicting traffic flows over different prediction horizons, as indicated by better goodness-of-fit statistics. An important advantage of our framework is its ability to be transferred to new locations where data is unavailable. This is achieved by appropriately accounting for spatial dependencies based on station distances and various traffic parameters. In contrast, regular DL models are not easily transferable as their inputs remain fixed. It should be noted that due to data limitations, we were unable to perform spatial sensitivity analysis, which calls for further research using simulated data.

翻译：针对时空交通流预测的深度学习模型，通常采用卷积或图卷积滤波器结合循环神经网络，以捕获交通数据中的时空依赖关系。这类模型（如CNN-LSTM）利用邻近检测站的交通流数据，预测目标位置特定检测点的流量。然而，由于模型主要学习与检测器配置及目标位置交通特性相关的特征，其捕获交通系统全局动态的能力有限，导致模型难以迁移至不同位置，尤其是在新位置缺乏训练数据的情况下。为解决这一局限，本文提出一种基于交通流物理机制的特征变换方法，用于时空深度学习模型。该变换融合了Newell理论中针对目标位置交通流的不拥堵与拥堵状态估计量，使模型能够学习交通系统的全局动态。我们采用来自两个不同地点的交通数据对方法进行实证验证。结果表明，所提特征变换在不同预测时域上均能提升模型的交通流预测性能，拟合优度统计量显著改善。本框架的关键优势在于其可迁移至缺乏数据的新位置，这通过基于检测站距离及多种交通参数的空间依赖关系建模实现。相比之下，常规深度学习模型因输入固定而难以直接迁移。需指出的是，受数据条件限制，本研究未能开展空间敏感性分析，后续需借助仿真数据进一步探索。