School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China.
PLoS One. 2022 Mar 3;17(3):e0264835. doi: 10.1371/journal.pone.0264835. eCollection 2022.
With the steady increase in passenger volume of high-speed railways in China, some high-speed railway sections have faced a difficult situation. To provide more transport services, it is necessary to add as many trains as possible in a section to increase capacity. To solve this problem, a compressed multilayer space-time network model is constructed with the maximum number of trains that can be scheduled in the train timetable as the objective. The combination of the train stop plan and speed level is represented by the layer of network where the train is located, and constraints such as train selection, train safety, train overtake and cross-line trains are considered. A method based on timing-cycle iterative optimization is designed to decompose the original problem into multiple subproblems, and the solving order of the subproblems is determined by a heuristic greedy rule. Taking the Beijing-Jinan section of the Beijing-Shanghai high-speed railway as an example, the maximum number of trains was increased by 12.5% compared with the timetable before optimization. The saturated timetables provide detailed schedules, which helps decision-makers better adjust the timetable to run more trains.
随着中国高铁客流量的稳步增长，一些高铁线路面临着困难的局面。为了提供更多的运输服务，有必要在一个路段尽可能增加列车数量以增加运力。为了解决这个问题，构建了一个以列车时刻表中可安排的最大列车数量为目标的压缩多层时空网络模型。列车停靠计划和速度水平的组合由列车所在的网络层表示，并考虑了列车选择、列车安全、列车超车和跨线列车等约束条件。设计了一种基于定时循环迭代优化的方法，将原始问题分解为多个子问题，并通过启发式贪婪规则确定子问题的求解顺序。以京沪高铁北京至济南段为例，与优化前的时刻表相比，列车数量增加了 12.5%。饱和时刻表提供了详细的时间表，有助于决策者更好地调整时间表，以运行更多的列车。
