| Summary: | This paper improves the level of urban traffic control by creasing the dimension of control variables. It focuses on roads rather than vehicles. A new space-time resource scheduling model and a bi-level optimization control method for urban intersections are developed in this study. In traditional concept, the properties of lane are fixed. Nowadays, it changes with the development of new technologies, which increase the dimension of the control variables in the control model and expand the control capability. To this end, the space-time resource scheduling model for intersections includes spatial variables (lane genes, phases, and phase sequences) and time variables (green light time of phases). Then, a new bi-level optimization control method is developed, in which there are an upper layer for lane control based on reinforcement learning and a lower layer is a two-layer optimal control method of phase control based on the model predictive control idea. Finally, the proposed method is proved more efficient than traditional methods after comprehensive experiments.
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