Recent advances in artificial intelligence and wireless communication technologies have created great potential to reduce congestion in urban networks. In this research, we develop a stochastic analytical model for optimal control of communicant autonomous vehicles (CAVs) at smart intersections. We present the automated network fundamental diagram (ANFD) as a macro-level modeling tool for urban networks with smart intersections. In the proposed cooperative control strategy, we make use of the headway between the CAV platoons in each direction for consecutive passage of the platoons in the crossing direction through non-signalized intersections with no delay. For this to happen, the arrival and departure of platoons in crossing directions need to be synchronized. To improve system robustness (synchronization success probability), we allow a marginal gap between arrival and departure of the consecutive platoons in crossing directions to make up for operational error in the synchronization process. We then develop a stochastic traffic model for the smart intersections. Our results show that the effects of increasing the platoon size and the marginal gap length on the network capacity are not always positive. In fact, the capacity can be maximized by optimizing these cooperative control variables. We analytically solve the traffic optimization problem for the platoon size and marginal gap length and derive a closed-form solution for a normal distribution of the operational error. The performance of the network with smart intersections is presented by a stochastic ANFD, derived analytically and verified numerically using the results of a simulation model. The simulation results show that optimizing the control variables increases the capacity by 138% when the error standard deviation is 0.1 s.

人工智能和无线通信技术的最新进展为减少城市网络的拥塞创造了巨大潜力。在这项研究中，我们开发了一种随机分析模型，用于对智能交叉路口的自动驾驶车辆（CAV）进行最佳控制。我们介绍了自动化网络基本图（ANFD），作为具有智能交叉口的城市网络的宏观层次建模工具。在提出的协作控制策略中，我们利用CAV排在每个方向上的间距来使排在交叉方向上连续通过无信号交叉口而没有延迟。为此，需要使排在交叉方向上的到达和离开保持同步。为了提高系统的鲁棒性（同步成功概率），我们在连续的排在交叉方向上的到达和离开之间留有一定的间隙，以弥补同步过程中的操作错误。然后，我们为智能交叉路口建立随机交通模型。我们的结果表明，增加排大小和边际间隙长度对网络容量的影响并不总是积极的。实际上，可以通过优化这些协作控制变量来最大化容量。我们从分析上解决了针对排大小和边际间隙长度的交通优化问题，并针对操作误差的正态分布得出了封闭形式的解决方案。带有智能路口的网络的性能由随机ANFD表示，通过仿真模型的结果进行分析得出并进行数值验证。