  Connected and automated vehicle (CAV) technology is providing urban
transportation managers tremendous opportunities for better operation of urban
mobility systems. However, there are significant challenges in real-time
implementation, as the computational time of the corresponding operations
optimization model increases exponentially with increasing vehicle numbers.
Following the companion paper (Chen et al., 2020) which proposes a novel
automated traffic control scheme for isolated intersections, this study
proposes a network-level real-time traffic control framework for CAVs on grid
networks. The proposed framework integrates a rhythmic control (RC) method with
an online routing algorithm to realize collisionfree control of all CAVs on a
network and achieve superior performance in average vehicle delay, network
traffic throughput, and computational scalability. Specifically, we construct a
preset network rhythm that all CAVs can follow to move on the network and avoid
collisions at all intersections. Based on the network rhythm, we then formulate
online routing for the CAVs as a mixed integer linear program, which optimizes
the entry times of CAVs at all entrances of the network and their time-space
routings in real time. We provide a sufficient condition that the linear
programming relaxation of the online routing model yields an optimal integer
solution. Extensive numerical tests are conducted to show the performance of
the proposed operations management framework under various scenarios. It is
illustrated that the framework is capable of achieving negligible delays and
increased network throughput. Furthermore, the computational time results are
also promising. The CPU time for solving a collision-free control optimization
problem with 2,000 vehicles is only 0.3 s on an ordinary personal computer.
