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首页> 外文期刊>Intelligent Transportation Systems, IEEE Transactions on >Design of Robust and Energy-Efficient ATO Speed Profiles of Metropolitan Lines Considering Train Load Variations and Delays
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Design of Robust and Energy-Efficient ATO Speed Profiles of Metropolitan Lines Considering Train Load Variations and Delays

机译:考虑列车负荷变化和时延的鲁棒高效节能ATO速度曲线设计

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摘要

Metropolitan railway operators' strategic plans include nowadays actions to reduce energy consumption. The application of ecodriving initiatives in lines equipped with automatic train operation (ATO) systems can provide important savings with low investments. Previous studies carried out under the ATO framework have not considered the main uncertainties in the traffic operation: the train load and delays in the line. This paper proposes a method to design robust and efficient speed profiles to be programmed in the ATO equipment of a metro line. First, an optimal Pareto front for ATO speed profiles that are robust to changes in train load is constructed. There are two objectives: running time and energy consumption. A robust optimization technique and an alternative method based on the conservation of the shape of the speed profiles (pattern robustness) are compared. Both procedures make use of a multi objective particle swarm optimization algorithm. Then, the set of speed profiles to be programmed in the ATO equipment is selected from the robust Pareto front by means of an optimization model. This model is a particle swarm optimization algorithm (PSO) to minimize the total energy consumption considering the statistical information about delays in the line. This procedure has been applied to a case study. The results showed that the pattern robustness is more restrictive and meaningful than the robust optimization technique as it provides information about shapes that are more comfortable for passengers. In addition, the use of statistical information about delays provides additional energy savings between 3% and 14%.
机译:都市铁路运营商的战略计划包括当今减少能耗的行动。在配备有自动列车运行(ATO)系统的生产线中采用节能驾驶计划可以节省大量投资,而且投资少。在ATO框架下进行的先前研究并未考虑交通运营中的主要不确定因素:火车负载和线路延误。本文提出了一种方法,用于设计要在地铁ATO设备中编程的鲁棒,高效的速度曲线。首先,构建了一个ATO速度曲线的最优Pareto前沿,该曲线对列车负载的变化具有鲁棒性。有两个目标:运行时间和能耗。比较了鲁棒性优化技术和基于速度曲线形状守恒性(模式鲁棒性)的替代方法。这两个过程都使用了多目标粒子群优化算法。然后,通过优化模型从鲁棒的帕累托前端中选择要在ATO设备中编程的一组速度曲线。该模型是一种粒子群优化算法(PSO),它考虑了有关线路延迟的统计信息,从而将总能耗降至最低。此过程已应用于案例研究。结果表明,模式鲁棒性比鲁棒性优化技术更具限制性和意义,因为它提供了有关乘客更舒适的形状信息。此外,使用有关延迟的统计信息还可以节省3%到14%的能源。

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