基于再生制动能的列车发车间隔优化模型

随着低碳环保的生活理念越来越深入人心,节能已成为城市轨道交通运行的目标。着眼于再生制动能技术在列车运行过程中日益突出的效用,笔者利用再生制动能,以求得所有列车运行总能耗最低的时间间隔为目标,建立了优化多列车发车间隔的模型。该模型采用微元法将位移、速度与时间的关系表示出来,并算出列车牵引能耗,再结合再生制动能创建模型,通过改进的遗传算法求解,得到列车最优的发车间隔和运行图。     

基于编织算法的复线高速磁浮列车运行图铺画方法

根据高速磁浮线路结构和运行控制系统的特点,建立了复线高速磁浮列车运行图编制模型,设计了编织算法对模型进行求解。该算法严格按照列车在各车站的发车时间顺序,对上、下行列车运行线如同“织毛衣”般交叉铺画,在遇到列车冲突时通过更改列车路由和增加发车间隔等方法予以及时化解,逐步得到整体优化的列车运行图。算例分析表明,该方法优化速度快,能够有效地进行高速磁浮列车运行图的铺画。     

基于收敛粒子群算法的重载铁路列车运行调整方法

针对施工天窗对重载铁路行车安全限速和行车安全产生的不良影响,提出了基于收敛粒子群算法的重载铁路列车运行调整方法。首先,在分析列车追踪间隔影响因素的基础上,推导出不同编组类型重载列车之间的追踪间隔计算公式,实现了重载列车追踪间隔计算。然后,考虑施工天窗后的限速要求,以重载列车总晚点时间为优化目标,以列车区间最少运行时分、追踪间隔时分、列车停站时分、天窗时间段内禁止行车、天窗后安全限速等为约束条件,建立了施工条件下重载铁路列车运行调整模型,刻画了考虑安全限速的重载列车运行调整问题。通过优化列车到站、离站以及通过车站时刻,实现重载列车运行调整。进而,针对模型特点,在经典粒子群算法中引入收敛因子,设计了收敛粒子群算法对模型进行求解。最后,以朔黄铁路运输生产数据为基础,对建立的重载铁路运行调整模型进行实例化处理,验证了模型和算法。实验结果表明,对于求解重载铁路运行调整问题,设计的收敛粒子群算法比经典粒子群算法在计算效率上提升了5.45%,而求解精度保持不变。该方法可压缩重载列车追踪间隔,提高重载铁路运输效率,保证行车安全,提高基于安全限速的列车运行调整效率,为施工条件下重载铁路列车运行调整工作提...     

基于CTM快速路可变限速标志投资优化

针对快速路可变限速标志投资缺乏系统的定量分析、位置模糊问题,基于改进元胞传输模型构建了阶梯形限速投资模型。首先,对限速控制的改进元胞传输进行了描述;接着,为实施阶梯形限速控制,引入了0-1整数规划算法和限速约束条件;最后,利用遗传算法优化了投资额一定时可变限速标志位置以及动态限速值。算例结果发现：随着投资额增大,平均临界密度标准差、饱和度会减小,车辆运行速度会提高,但同时效益生产量会随之降低,系统平均延误会随之增大。结果分析表明,该模型能够有效解决可变限速标志投资优化问题。     

车载TBTC-CBTC系统降级场景下的CPN建模与仿真

基于轨道电路的列车控制（TBTC）-基于通信的列车控制（CBTC）双模车载系统是实现轨道交通多网融合的关键,其模式间切换具有较强的随机性和并发性,并直接影响车载信号系统的运营可用性。然而,车载信号系统故障降级导致轨道交通资源利用率降低,表现为列车追踪的时间间隔增加,而间隔增加程度取决于区间长度和TBTC列车占用检测区段长度。从不同模式下资源分配、使用和释放角度,在列车运行过程中利用有色Petri网对TBTC-CBTC双模冗余车载信号系统的列车追踪运营场景进行建模,模拟CBTC车载信号系统故障发生的随机性和系统降级对后续列车的影响,精准描述并分析列车运营受影响的情况。将城市轨道交通项目的典型配置参数代入到有色Petri网模型中进行仿真,验证在不同线路下运营间隔受模式切换影响的程度。仿真结果表明,当区间长度小于1 500 m时,列车晚点时间可控制在180 s以内,晚点时间随着区间长度的增加而延长。     

高速列车在移动闭塞条件下的运行仿真

本文构造了移动闭塞条件下的高速列车计算机运行仿真系统,介绍了系统总体结构,对列车实际运行间隔进行了分析,对各功能模块进行了描述。该系统为进一步分析研究移动闭塞条件下高速列车追踪运行间隔模型、列车的优化控制方法和列车的运行延误影响等提供了仿真平台,对系统实际运用起到了指导的作用。     

基于人工蜂群算法的轨道交通列车行车间隔优化

轨道交通运营组织作为轨道交通运营企业管理的核心,在降低企业运营成本、提升服务水平和旅客出行效率方面起着非常重要的作用。提出一种基于人工蜂群（ABC）优化算法的列车行车间隔优化策略,在考虑运营企业和旅客各自利益的基础上,以列车发车间隔为决策变量,建立旅客平均候车时间最小和列车等候时间最大的双目标非线性规划模型。采用ABC算法对模型进行优化求解,结合京津城际铁路某日不同时段客流基础数据进行仿真,实例验证了所提算法和模型的有效性。     

平行公交系统中的计算实验问题研究

研究了在平行公交系统中如何进行计算实验的问题. 平行公交系统中的计算实验主要通过以下三步完成: 1)基于简化的数学模型的求解确定初始的公交发车时间间隔; 2)在一个较小范围内不断调整公交车发车间隔, 在平行公交系统中运行观察其运行效果, 形成不同的发车间隔方案; 3)建立方案评价体系, 对得到的发车间隔方案进行评估, 确定最优发车方案. 在本文的计算实验实例中, 共进行了6次计算实验, 得到了一个最优的发车方案, 并基于结果数据说明了论文所提方法的其合理性.     

基于区段划分的快速公交实时行车控制研究

针对快速公交系统（BRT）车辆出现串车和大间隔对车辆延误和乘客候车满意度的影响较大的情况,提出了一种基于区段划分的实时行车控制算法。并在区段划分的基础上,给出了理想最小间隔的计算方法以及道路使用情况等级量化划分方法。综合考虑信号灯配时方案、站台延误时间以及区段的历史平均速度,提出了一种较为精确的车辆位置预测方法。     

具有极大时间约束的轨道交通系统的周期分析

运用极大–加代数方法研究具有极大时间约束的轨道交通系统的周期运行规律.分别建立具有两个车站的双回路城际轨道交通系统和具有n个车站的单回路城市轨道交通系统的极大–加线性模型.对于前者,运用系统状态矩阵的周期性,证明各个车站第k次与第(k+2)次的发车时间间隔相同;对于后者,运用状态变量的线性替换,证明在任何初始状态下,系统经过一次循环便可进入周期稳态运行,即列车连续两次到达同一车站的时间间隔相同.周期时间分析有利于轨道交通系统列车时刻表的编排和周期运行方案的设计.为验证本文结果的实用性和有效性,给出周期时间分析在列车调度和线路规划中的应用例子.     

A decomposition based hybrid heuristic algorithm for the joint passenger and freight train scheduling problem

We study the joint problem of scheduling passenger and freight trains for complex railway networks, where the objective is to minimize the tardiness of passenger trains at station stops and the delay of freight trains. We model the problem as a mixed integer program and propose a two-step decomposition heuristic to solve the problem. The heuristic first vertically decomposes the train schedules into a passenger train scheduling phase and then a freight train scheduling phase. In the freight train scheduling phase, we use a train-based decomposition to iteratively schedule each freight train. Experimental results show the efficiency and quality of the proposed heuristic algorithm on real world size problems.     

基于换乘导向的大型客运枢纽高铁列车接续优化

针对高速铁路成网条件下的客运枢纽高铁列车接续优化问题,分析了枢纽内的旅客换乘过程,提出了中长途客流的换乘满意度概念;以平均换乘满意度和枢纽车站列车到发均衡性为优化目标,以大站合理发车时间、合理终到时间、车站作业间隔时间、旅客换乘时间、车站到发线能力等为约束条件,建立了基于换乘协同的大型客运枢纽高速列车接续优化模型。设计了改进染色体编码方式和选择策略的遗传算法对算例进行了求解。改进后的遗传算法同基本遗传算法、基本模拟退火算法相比,目标函数中所求的平均换乘满意度分别增加了5.10%、2.93%,枢纽车站列车到发均衡性分别提高了0.27%、2.31%,算例结果验证了改进遗传算法的有效性和稳定性,表明所提方法可以有效地提高大型枢纽高铁列车的接续质量。     

Locomotive assignment and freight train scheduling using genetic algorithms

This study considers the movement of freight trains through a passenger rail network, a common occurrence in many developing countries. Passenger trains run according to a fixed schedule while freight trains need to be accommodated and run on the same track, ensuring that they do not interfere with passenger train movements. Operationally, this requires the assignment of a locomotive to a freight rake and then creating a workable schedule. Accordingly, we propose to solve the problem in two phases. In the first phase, we assign locomotives with partial scheduling with the objectives of minimizing total deadheading time and total coupling delay. We use a genetic algorithm to find non‐dominant locomotive assignment solutions and propose a method for evaluating its performance. The solutions are then ranked using two approaches, based on the decision maker's preferences. In the second phase, we select a locomotive assignment solution based on the ranking and find the lower bound on the arrival time of freight trains at their destinations. We use a genetic algorithm again to schedule the freight trains in the passenger rail network, with prescribed locomotive assignment precedence constraints with the objective of minimizing total tardiness. Computational results confirm the efficacy of the proposed method.     

Train Timetable Problem on a Single-Line Railway With Fuzzy Passenger Demand

The aim of the train timetable problem is to determine arrival and departure times at each station so that no collisions will happen between different trains and the resources can be utilized effectively. Due to uncertainty of real systems, train timetables have to be made under an uncertain environment under most circumstances. This paper mainly investigates a passenger train timetable problem with fuzzy passenger demand on a single-line railway in which two objectives, i.e., fuzzy total passengers' time and total delay time, are considered. As a result, an expected value goal-programming model is constructed for the problem. A branch-and-bound algorithm based on the fuzzy simulation is designed in order to obtain an optimal solution. Finally, some numerical experiments are given to show applications of the model and the algorithm.      

客运专线的追踪间隔控制模型与计算

在分析列车追踪间隔时间类型以及动车组追踪运行的基础上，建立了客运专线信号系统下的列车区间追踪间隔和列车车站追踪间隔模型，给出了相应的列车追踪间隔时间的计算方法。应用Matlab软件进行了计算，并对计算结果进行了分析。     

Optimization using simulation and response surface methodology with an application on subway train scheduling

This paper presents a technique based on discrete‐event simulation and response surface methodology to model and then optimize the schedule of subway train travels. The aim of this study is to find appropriate headways—time intervals between the travels of two consecutive trains—at different hours in order to optimize average passenger travel time and rate of carriage fullness. For physical reasons and the observance of safety standards, an increase in the train speed in order to decrease average passenger travel time may not exceed some specified limits. One of the ways to decrease this average is to appropriately adjust headways of trains in the schedule. For this purpose, a metamodel of multinomial type is fitted to the data obtained from simulation tests to describe the relation between input variables (headways) and output variables (average passenger travel time in system and carriage fullness rate), and then optimal combinations of input variables are obtained using a weighed metric method and sequential quadratic programming.     

移动闭塞条件下基于双曲列控策略的发车间隔时间计算

列车间隔时间关乎铁路行车安全和效率。对未来铁路移动闭塞系统发车间隔时间的科学计算进行探讨,引入了能够反映经验丰富司机驾车列车优化行为的基于双曲函数的列车行为控制模型;然后,讨论了列车分段运动方程和车站发车作业、进路解锁等环节,以及各种误差因素,并将它们与基于双曲函数的列车行为控制模型有机结合起来,建立了计算移动闭塞条件下车站发车间隔时间的数学模型,给出了两种不同情形的车站发车间隔时间计算方法,数值仿真试验验证了算法的有效性和可行性。对移动闭塞条件下车站发车环节的列车行为控制和行车组织有较大参考价值。     

基于物联网技术的移动闭塞分区设计

随着哈大高铁等一批高速客运专线的商业运营,我国真正地进入了高铁时代。在高铁时代,列车最高时速保持在350公里左右。这样,行车安全就显得尤为重要了。在保证行车安全的基础上,采用"无形的"移动闭塞方式既可以缩短列车间隔又可以提高列车运行效率。为此提出了一套基于ZigBee技术的无线物联网络系统,在列车与轨旁设备之间构成移动闭塞分区,确保列车安全、高速、高效运行。     

A feasible timetable generator simulation modelling framework for train scheduling problem

An important problem in management of railway systems is the train scheduling/timetabling problem. This is the problem of determining a timetable for a set of trains that do not violate track capacities and satisfy some operational constraints. In this study, a feasible timetable generator framework for stochastic simulation modelling is developed. The objective is to obtain a feasible train timetable for all trains in the system. The feasible train timetable includes train arrival and departure times at all visited stations and calculated average train travel time. Although this study focuses on train scheduling/timetabling problem, the developed simulation framework can also be used for train rescheduling/dispatching problem if this framework can be fed by real time data. The developed simulation model includes stochastic events, and can easily cope with the disturbances that occur in the railway system.