Observability and controllability are two critical requirements for a partially observable transportation system. This paper proposes a stepwise signal optimization framework with connected vehicle (CV) data as input to solve both challenges. First, a Bayesian deduction method based on low-penetration CV data is established to estimate the traffic volume. Thereafter, an offline signal optimization model is constructed to simultaneously optimize the flexible lane settings and signal timings, which are set as the prior information for the third step. In the third step, an online deep recurrent Q-learning (DRQN) signal optimization model dynamically adjusts signal settings based on real-time traffic information. Numerical experiments demonstrate that the model outperforms the actuated control, the online DQRN model without offline filter, and the back-pressure model by 9%–66% and 7%–29% in two networks. This study innovatively combines traffic state estimation and traffic signal control as an integrated process. It contributes to an improved understanding of traffic control in a CV environment and lays a solid foundation for future traffic control strategies.

中文翻译：

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部分可观测交通系统的交通信号优化及车联网普及率低

可观察性和可控性是部分可观察交通系统的两个关键要求。本文提出了一个以联网车辆 (CV) 数据作为输入的逐步信号优化框架，以解决这两个挑战。首先，建立了一种基于低渗透CV数据的贝叶斯推导方法来估计交通量。此后，构建离线信号优化模型，同时优化灵活车道设置和信号配时，将其设置为第三步的先验信息。第三步，在线深度循环 Q 学习 (DRQN) 信号优化模型根据实时交通信息动态调整信号设置。数值实验表明该模型优于驱动控制，没有离线过滤器的在线 DQRN 模型，背压模型在两个网络中分别降低了 9%–66% 和 7%–29%。本研究创新地将交通状态估计和交通信号控制结合为一个集成过程。它有助于提高对 CV 环境中交通控制的理解，并为未来的交通控制策略奠定坚实的基础。