Traffic signal optimization for partially observable traffic system and low penetration rate of connected vehicles

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.