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How can metro traffic be made more efficient? 

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Efficient methods to improve automation in metro traffic regulation are being investigated. An improved state space model is developed to describe the dynamics of metro loop lines, including predicted near-future states. The metro traffic regulation problem is formulated as an optimal control problem and solved using quadratic programming. Simulation results demonstrate the effectiveness of the designed controller. Efficiency in metro traffic can be achieved through the strategic design of efficient timetables and economic driving parameters. A two-level optimization method is proposed to design efficient driving commands for each interstation of a metro line. This method considers comfort restrictions, train mass uncertainty, and energy consumption. The optimization is performed using a multi-objective particle swarm optimization algorithm and a fuzzy greedy randomized adaptive search procedure. The proposed algorithms achieve a significant reduction in energy consumption compared to traditional timetable designs. Energy efficiency in metro operations can be improved by introducing energy-saving devices and optimizing train timetables. A smart control module is designed to integrate passive and active control modes in the metro power supply network. A timetable optimization model is introduced to trade off energy consumption, infrastructure investment, and travel time. The non-dominant sorting genetic algorithm is used to optimize the timetable. The proposed approach achieves significant improvements in energy efficiency from both passive and active energy-saving devices. The use of a frequency-controlled drive for regulating asynchronous electric motors on subway cars can increase energy efficiency. Scalar control is used due to its ease of implementation and adjustment. The introduction of IR compensation into the control system of the electric drive improves the motor's performance and stability. The application of IR compensation is a promising direction for improving energy efficiency in subway operations.

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The paper proposes an improved artificial bee colony algorithm for speed profile optimization and an adaptive terminal sliding mode method for speed tracking to improve energy efficiency in metro traffic.
The paper proposes a two-level optimization method to design efficient driving commands for metro lines, aiming to increase sustainability by reducing energy consumption and considering comfort restrictions and train mass uncertainty.
The paper discusses increasing the energy efficiency of subway cars by using a frequency-controlled drive for regulating asynchronous electric motors. It does not specifically address how to make metro traffic more efficient.
The paper proposes the design of metro traffic regulation controllers subjected to operational constraints to improve automation and efficiency in metro traffic regulation.
The paper discusses the optimization of metro traffic by introducing energy-saving devices and designing a smart control module for the power supply system.

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