关于高铁列车客运站优化选择编制仿真研究
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摘要
高速铁路客运节点级别划分可以有效简化旅客列车停站方案优化编制问题。在旅客列车停站方案编制前,需要事先划分高速铁路客运节点的等级,以确定不同站点的停靠列车数量。高铁客运节点具有多属性、关联度高的特点,采用传统人工站点划分方法往往导致站点信息利用不足,划分结果不合理。提出了粗糙集和改进仿射传播算法相结合的客运节点级别划分方法,首先运用遗传算法采取粗糙集理论中的属性约简以排除冗余变量,接着基于约简后的客运节点属性变量指标体系,运用引入IGP指标的改进仿射传播算法对客运节点进行聚类。以2014年京沪高铁的实例研究结果表明,改进仿射传播算法能够对高速铁路客运节点进行有效划分,为高速铁路旅客停站方案优化编制提供基础。
The classification of passenger transport nodes of the high-speed railway can effectively simplify the optimization of passenger train stop schedule plan. Before organized of the plan of passenger train stops, the grade of passenger transport nodes should be divided to determine the number of stop trains at different stations. With the characteristics of multi attribute and high degree of association, the use of traditional artificial site partition method often leads to insufficient utilization of site information and unreasonable result of division. The paper proposed a classification method of passenger node level based on the combination of rough set and improved affine propagation algorithm. We first used genetic algorithm and takes the attribute reduction in rough set theory to eliminate redundant variables, and then introduced the improved affine propagation algorithm with IGP index to cluster on passenger transport nodes based on the properties variable indicators after reduction. The results of the case study of Beijing-Shanghai high-speed railway in 2014 show that the improved affine propagation algorithm can effectively divide the high-speed railway passenger nodes and provide a basis for optimizing the preparation of high-speed railway passenger stops.
The classification of passenger transport nodes of the high-speed railway can effectively simplify the optimization of passenger train stop schedule plan. Before organized of the plan of passenger train stops, the grade of passenger transport nodes should be divided to determine the number of stop trains at different stations. With the characteristics of multi attribute and high degree of association, the use of traditional artificial site partition method often leads to insufficient utilization of site information and unreasonable result of division. The paper proposed a classification method of passenger node level based on the combination of rough set and improved affine propagation algorithm. We first used genetic algorithm and takes the attribute reduction in rough set theory to eliminate redundant variables, and then introduced the improved affine propagation algorithm with IGP index to cluster on passenger transport nodes based on the properties variable indicators after reduction. The results of the case study of Beijing-Shanghai high-speed railway in 2014 show that the improved affine propagation algorithm can effectively divide the high-speed railway passenger nodes and provide a basis for optimizing the preparation of high-speed railway passenger stops.
引文
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[2] 蒲松,陈钉均,王文宪.基于改进退火算法的高速列车开行方案研究[J].计算机仿真,2015,32(7):150-153.
[3] 钟庆伦.高速铁路列车停站方案与列车运行图能力关系研究[D].西南交通大学,2016.
[4] 王文宪,吕红霞.基于近邻传播聚类的铁路客运节点类别划分[J].计算机应用研究,2016,33(10):2926-2928.
[5] 杜欣,等.基于节点重要度的客运专线旅客列车开行方案[J].北京交通大学学报,2010,34(6):5-10.
[6] Pawlak Zdzislaw.Rough Sets:Theoretical Aspects of Reasoning About Data[M].Dordrecht:Kluwer Academic Publishers,1991.
[7] 菅利荣.面向不确定性决策的杂合粗糙集方法及其应用[M].北京:科学出版社,2008,29-40.
[8] 周世兵,徐振源,唐旭清.基于近邻传播算法的最佳聚类数确定方法比较研究[J].计算机科学,2011,38(2):225-228.
[9] B J Frey,D Dueck.Clustering by passing messages between data points[J].science,2007,315(5814):972-976.
[10] 王开军,等.自适应仿射传播聚类[J].自动化学报,2007,33(12):1242-1246.