With the fast development of automated vehicle (AV) technologies, scholars have proposed various innovative local traffic control schemes for more effective management of AV traffic, especially at intersections. However, due to computational intractability, the investigation of network-level AV control is still at the initial stage. This study proposes a space-time routing framework applicable in dedicated AV zones. To relieve the computational load, we establish a node-based conflict point network to model realistic road networks, and at each conflict point, we record the space-time occupations of AVs in continuous timelines. Then, based on the conflict point network, we develop two space-time routing algorithms for each AV once it enters the dedicated AV zone to minimize its trip travel time while maintaining the non-collision insurances; these two algorithms can trade-off between solution quality and computational load. Furthermore, to enhance the network throughput for handling heavy traffic, we develop a ”platoon strategy” that forces AVs to pass through conflict points in platoons, and we adopt Deep Q-learning (DQN) to optimize the platoon sizes at different spots dynamically. Numerical tests show that both proposed algorithms perform well in that they can execute the routing tasks with very limited computational time, and the average vehicle delay approaches zero when the traffic is relatively mild. Meanwhile, compared with the FCFS policy and the optimization-based approach, the platoon strategy can greatly reduce the average vehicle delay under congested scenarios and give a better balance between the optimality and real-time performance.

随着自动车辆（AV）技术的快速发展，学者们提出了各种创新的本地交通控制方案，以更有效地管理AV交通，尤其是在十字路口。但是，由于计算上的困难，对网络级AV控制的研究仍处于起步阶段。这项研究提出了适用于专用AV区域的时空路由框架。为了减轻计算量，我们建立了一个基于节点的冲突点网络来对现实的道路网络进行建模，并且在每个冲突点处，我们都记录了连续时间轴上AV的时空占用。然后，基于冲突点网络，一旦每个AV进入专用AV区域，我们将为其开发两种时空路由算法，以最大程度地减少其旅行时间，同时保持非冲突保险；这两种算法可以在解决方案质量和计算负载之间进行权衡。此外，为了提高网络吞吐量以处理繁忙的流量，我们制定了一种“排策略”，以强制AV通过排中的冲突点，并采用深度Q学习（DQN）来动态优化不同位置的排大小。数值测试表明，两种算法均能很好地执行路由任务，并且计算时间非常有限，并且在交通流量相对较小时，平均车辆延迟接近零。同时，与FCFS策略和基于优化的方法相比，该排策略可以大大减少拥塞情况下的平均车辆延迟，并在最优性和实时性能之间取得更好的平衡。