A Dynamic Predictive Traffic Signal Control Framework in a Cross-Sectional Vehicle Infrastructure Integration Environment

With the development of modern wireless communication technology, especially the vehicle infrastructure integration (VII) technology, vehicles' information such as identification, location, and speed can be readily obtained at upstream cross-section. This information can be used to support traffic signal timing optimization in real time. A dynamic predictive traffic signal control framework for isolated intersections is proposed in a cross-sectional VII environment, which has the ability to predict vehicle arrivals and use this to optimize traffic signals. The proposed dynamic predictive control framework includes a dynamic platoon dispersion model (DPDM) which uses the vehicles' speed data from the cross-sectional VII environment, as opposed to traditional vehicle passing/existing data, to predict the arriving flow distribution at the downstream stop-line. Then, a dynamic programming algorithm based on the exhaustive optimization of phases (EOP) is proposed working in rolling optimization (RO) scheme with a 2s time horizon. The signal timings are continuously optimized by regarding the minimization of intersection delay as the optimization objective, and setting the green time duration of each phase as a constraint. In the end, the proposed dynamic predictive control framework is tested in a simulated cross-sectional VII environment and a case study carried out based on a real road network. The results show that the proposed framework can reduce the average delay and queue length by up to 33% and 35%, respectively, compared with the traditional full-actuated control.