A Fundamental Model with Stable Interpretability for Traffic Forecasting

Deep learning models have been widely applied in traffic prediction and analysis. Notably, attention-based models like Graph Attention Network (GAT) have brought significant insights and decisionmaking capabilities to traffic managers through their interpretability. However, attacks on the sensor networks that traffic prediction relies on can introduce severe disturbances and uncertainties in the interpretability of models, leading to erroneous judgments by managers. To address the issue, we propose a definition of fundamental models with stable interpretability. In the paper, we first showcase existing attention-based interpretable models in traffic prediction and analysis. Subsequently, we introduce and define the conditions that this fundamental model should meet in terms of accuracy, interpretability, and stability of interpretability. Finally, we discuss the opportunities and potential development directions in traffic forecasting and analysis. It is promised that the model will establish a solid foundation for ensuring the safety of deploying and applying interpretable models in real-world transportation management systems.