Obtaining future traffic state evolution information is critical to traffic control algorithms design and further to intelligent transportation systems. However, accurately predicting traffic state evolution is not an easy task, although the traffic prediction-related study attracted a lot of attention. This study develops a macroscopic traffic flow model-integrated deep learning framework ( $\mathbf{MTFD}$ ) for the high-resolution temporal-spatial traffic state dynamic propagation on a road network, integrating temporal-spatial traffic dependency, traffic flow theory, and data analysis techniques. First, traffic state propagation on every road section is mathematically described by the $\mathbf{CTM}$ model given traffic initial and boundary conditions. Next, a temporal-spatial traffic dependency attention ( $\mathbf{TSTD}$ ) recurrent neural network is developed to predict boundary conditions factoring the traffic temporal-spatial dependency. Also, this paper develops a graph theory-based method to capture the temporal-spatial traffic dependency among the traffic on neighboring road sections. Last, the extended Kalman Filter ( $\mathbf{EKF}$ ) is introduced to adjust the predicted traffic state at an intersection to satisfy the conservation law. The numerical experiments illustrate that the proposed method predicts the traffic state evolution in a freeway network within 30 minutes with accuracy varying from 75%-95%. It has a better performance compared to the tested baseline models (APTN, Graph CNN-LSTM, and so on). The experimental results also illustrate that factoring traffic dependency and integrating data assimilation techniques can improve prediction accuracy.

This paper develops distributed optimization-based, platoon-centered connected and autonomous vehicle (CAV) car-following schemes, motivated by the recent interest in CAV platooning technologies. Various distributed optimization or control schemes have been developed for CAV platooning. However, most existing distributed schemes for platoon centered CAV control require either centralized data processing or centralized computation in at least one step of their schemes, referred to as partially distributed schemes. In this paper, we develop fully distributed optimization based, platoon centered CAV platooning control under the linear vehicle dynamics via the model predictive control approach with a general prediction horizon. These fully distributed schemes do not require centralized data processing or centralized computation through the entire schemes. To develop these schemes, we propose a new formulation of an objective function and a decomposition method that decomposes a densely coupled central objective function into the sum of multiple locally coupled functions whose coupling satisfies the network topology constraint. We then exploit locally coupled optimization and operator splitting methods to develop fully distributed schemes. Control design and stability analysis is carried out to achieve desired traffic transient performance and asymptotic stability. Numerical tests demonstrate the effectiveness of the proposed fully distributed schemes and CAV platooning control.