High-speed train tracking running curve optimization setting method

一种高速列车追踪运行曲线优化设定方法

Abstract

The invention discloses a high-speed train tracking running curve optimization setting method. According to the characteristic of 'movable and dynamic length' of a tracking section of a high-speed train under movable blocking condition, the method establishes a high-speed train echo state network speed prediction model, a movable blocking based tracking running model, a line network and a tracking running curve multi-target setting model adopting innovative evaluation indexes based on line and high-speed train running data acquired in a site; an efficient multi-target particle swarm optimization is adopted to use an algorithm convergence condition as one of model setting constraints, and high-speed train tracking running curve optimization setting is performed based on the real-time data; finally, section operation efficiency and stability are used as the evaluation indexes of the setting method, and a group of optimal running curves are screened out, so that the high-speed train running process is safe and efficient, and meanwhile the high-speed train section operating efficiency and stability under the movable blocking condition are improved.
本发明公开了一种高速列车追踪运行曲线优化设定方法,针对移动闭塞下高速列车追踪间隔“移动、动态长度”的特点,所述方法基于现场采集的线路和高速列车运行数据,建立了高速列车回声状态网络速度预测模型、基于移动闭塞的追踪运行模型、线路特征模型,以及采用了创新性评价指标的追踪运行曲线多目标设定模型。再采用高效的多目标粒子群算法,将算法收敛条件作为设定模型的约束之一,基于以上实时数据进行高速列车追踪运行曲线优化设定。最后以区间运营效率和稳定性为设定方法的评估指标,筛选出一组最优的运行曲线,使得高速列车运行过程安全、高效,同时提高移动闭塞下的高速铁路区间运营效率和稳定性。

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