As a state-of-the-art train control technology, virtual coupling provides a flexible and efficient operation scheme for urban rail transits. In this paper, an integrated train scheduling method is proposed to obtain the optimal coupling strategy and detailed train scheduling with the consideration of passenger flow dynamics. Different from existing train scheduling methods, we choose the coupling strategies, the selection of operating level, departure time, and arrival time for trains as decision variables, and a joint state-space model is constructed based on train movement and the evolution of passenger flow. We propose a multi-objective optimization model to collaboratively optimize the deviation of the train schedule under perturbations, the number of stranded passengers, and the energy consumption. A mixed-integer nonlinear programming (MINLP) model is established and further transformed into a linear model to facilitate problem-solving. The numerical results show that the proposed method can improve schedule punctuality and reduce the number of stranded passengers more effectively.
An Integrated Train Scheduling Optimization Approach for Virtual Coupling Trains
2022-10-08
534670 byte
Conference paper
Electronic Resource
English
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