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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 which to optimize traffic signals. The proposed dynamic predictive control framework includes a dynamic platoon dispersion model DPDM which uses the vehicles speed data from 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 2 seconds 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 carried out a case study 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 to traditional full-actuated control.

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