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Megacities' uneven metro line needs can result in long passenger wait times and a significant underutilization of vehicle capacity. Because of the erratic delays and unknown passenger flow, the issue is challenging to solve. On metro terminals, a metro fleet can be dynamically disassembled and assembled in similar modules (or carriages) thanks to our proposed modular transit system (MTS). A nonlinear programming (NLP) model that takes into account train power, greenhouse gas emissions, wind resistance, and operational economics provides a formal description of this problem. The NLP is then linearized, which speeds up the solution even further. We demonstrate the mathematical model's viability and validate the model's usefulness in terms of the economic, low-carbon, and ecological aspects by doing numerical experiments based on Shenzhen Metro data