Spatiotemporal Cross-Attention Neural Networks for Predicting Transportation Level of Service

The level of service (LOS) serves as a crucial metric in assessing the efficiency of intelligent transportation systems (ITSs). Precise forecasting of LOS is vital to improving the service quality provided by ITSs. However, within a transportation network, the LOS at each node typically exhibits spatial and temporal correlations due to the complex dynamics of traffic patterns. This poses a significant difficulty in accurately predicting traffic LOS. In this article, we propose a deep learning model, namely spatiotemporal cross-attention neural network (STCANN), to predict the LOS of a transportation network. On the one hand, the spatiotemporal dependencies between network nodes are captured through a set of stacked spatial and temporal cross-attention modules. On the other hand, the nonlinear interactions between traffic flow and speed are also resolved using a deliberate cross-attention mechanism. To evaluate the performance of the proposed model, we conduct experiments on two public datasets available from California highways. The results demonstrate that the proposed STCANN model outperforms the state-of-the-art methods in terms of prediction accuracy. The findings show that the incorporation of the cross-attention mechanism into the spatiotemporal neural network architecture shows great promise in improving the accuracy and reliability of LOS prediction.