Treffer: A Collaborative Optimization Model for Metro Passenger Flow Control Considering Train–Passenger Symmetry.

Due to the unbalanced temporal and spatial distribution of the passenger flow on metro lines during peak hours, the implementation of passenger flow control strategies effectively ensures operational safety and travel efficiency for passengers. In this study, we analyze the coupling relationship between trains and passengers, introduce train-stopping state variables, and synergistically optimize both train operation schedules and station passenger flow control. Aiming to minimize the total passenger delay time and maximize the number of boarding passengers, we consider four constraints: the train operation process, the passenger entry process, the passenger–train interaction process, and system constraints. This framework enables us to construct a cooperative passenger flow control optimization model for oversaturated metro lines. Subsequently, we propose an improved artificial bee colony algorithm to solve this model. We utilize evolutionary operators and an enhanced tabu search to create new food sources for employed bees and enhance their local search capabilities during the employed phase. Finally, Shanghai Metro Line 9 is used as a case study for the model validation. The computational results indicate that the proposed Collaborative passenger flow control strategy significantly reduces the number of stranded passengers on platforms and decreases the total passenger delay time by 36.26% compared to the existing passenger flow control strategy. The findings demonstrate that the cooperative control strategy proposed in this paper can effectively alleviate the pressure from passenger flow on oversaturated lines, balance the asymmetry between supply and demand, and markedly improve both safety and efficiency in the metro system during peak hours. [ABSTRACT FROM AUTHOR]