Traffic speed forecasting plays an increasingly essential role in successful intelligent transportation systems. However, this still remains a challenging task when the accuracy requirement is demanding. To improve the prediction accuracy and achieve a timely performance, the capture of the intrinsically spatio-temporal dependencies and the creation of a parallel model architecture are required. Accordingly, we propose a novel end-to-end deep learning framework named Graph Attention Temporal Convolutional Network (GATCN). The proposed model employs the graph attention network to mine the complex spatial correlations within the traffic network and temporal convolution operation to capture temporal dependencies. In addition, the multi-head self-attention mechanism is incorporated into the model to extract the spatio-temporal coupling effects. Experiments show that the proposed model consistently outperforms other state-of-the-art baselines for various prediction intervals on two real-world datasets. Moreover, we reveal that the proposed model can effectively distinguish the sophisticated traffic patterns of ramps on expressways by analyzing the graph attention heatmap.

Leveraging the accuracy and consistency of vehicle motion control enabled by the connected and automated vehicle technology, we propose the rhythmic control (RC) scheme that allows vehicles to pass through an intersection in a conflict-free manner with a preset rhythm. The rhythm enables vehicles to proceed at a constant speed without any stop. The RC is capable of breaking the limitation that right-of-way can only be allocated to nonconflicting movements at a time. It significantly improves the performance of intersection control for automated traffic. Moreover, the RC with a predetermined rhythm does not require intensive computational efforts to dynamically control vehicles, which may possibly lead to frequent accelerations or decelerations. Assuming stationary vehicle arrivals, we conduct a theoretical investigation to show that RC can considerably increase intersection capacity and reduce vehicle delay. Finally, the performance of RC is tested in the simulations with both stationary and nonstationary vehicle arrivals at both symmetric and asymmetric intersections.