A subjective capacity evaluation model for single-track railway system with delta-balanced traffic and lambda-tolerance level
A subjective capacity evaluation model for single-track railway system with delta-balanced traffic and lambda-tolerance level
复制标题
具有Delta平衡交通和lambda容忍水平的单线铁路系统主观容量评估模型
DOI：
10.1016/j.trb.2017.08.020
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Yang Lixing
中科院分区：
文献类型：
--
作者：
Li Feng;Gao Ziyou;Wang David Z W;Liu Ronghui;Tang Tao;Wu Jianjun;Yang Lixing
在本文中，我们提出了一种方法来测量单线铁路走廊在给定的双向交通负荷平衡程度和允许的总体延误水平下的通行能力。引入δ-平衡度和λ-容差度的概念，反映铁路管理者对运力评价的主观度量。将列车初始发车时间选择的列车平衡调度问题嵌入到铁路运力测度中。将组合调度和容量评价方法表述为一个0-1混合整数规划模型，并采用一种简单的基于二分类的启发式方法求解。提出了一种基于压缩模式概念的高效启发式算法来求解列车平衡调度问题，数值结果表明，该方法得到的解接近于CPLEX求解器的最优解。详细分析了单线铁路走廊在不同容差水平和平衡度下的双向交通承载能力。通过对交通负荷能力过渡区域的识别，为铁路管理者在处理干扰或中断情况下的列车重新调度请求提供了有用的决策支持工具。
In this paper, we propose a method to measure the capacity of single-track railway corridors subject to a given degree of balance between the two directional traffic loads and a permitted overall delay level. We introduce the concepts of δ-balance degree and λ-tolerance level to reflect the subjective measures of the railway administrator for capacity evaluation. A train balance scheduling problem with initial departure time choice of trains is embedded into the measure of railway capacity. The combined scheduling and capacity evaluation method is formulated as a 0-1 mixed integer programming model, and solved using a simple dichotomization-based heuristic method. A highly efficient heuristic procedure based on the concept of compaction pattern is developed to solve the train balance scheduling problem, and the numerical results demonstrate that the method yields high-quality solutions close to the optimal ones using the CPLEX solver. The two-way traffic loading capacity of a single-track railway corridor is analyzed in detail under different tolerance levels and balance degrees. The transition regions of traffic loading capacity are identified, and provide a useful decision support tool for the railway administrators in dealing with train rescheduling requests under disturbance or disruption scenarios.