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：深度学习（DL）模型在时空交通流预测中，常采用卷积或图卷积滤波器，并结合循环神经网络来捕捉交通数据中的时空依赖性。这类模型（如CNN-LSTM）利用相邻检测站的交通流预测目标位置的流量。然而，这类模型在捕捉交通系统更宏观的动态特性方面存在局限性，因为它们主要学习特定于检测器配置和目标位置交通特征的信息。因此，这些模型难以迁移至不同地点，尤其是当新地点缺乏训练数据时。为解决该局限，我们提出一种基于交通流物理特性的特征变换方法，用于时空深度学习模型。该变换引入Newell理论中目标位置的非拥堵与拥堵状态交通流估计量，使模型能够学习系统更广泛的动态特性。我们利用两个不同地点的交通数据对方法进行了实证验证。结果表明，所提出的特征变换在不同预测时域内均能提升模型预测交通流的表现，其拟合优度统计量更优。该方法的一个重要优势是其可迁移至缺乏数据的新地点——通过基于站点距离及多种交通参数合理考虑空间依赖性实现。相比之下，常规深度学习模型因输入固定而难以迁移。需指出的是，受数据限制，我们未能进行空间敏感性分析，需未来利用仿真数据开展进一步研究。