Transformer-Based Macroscopic Regulation for High-Speed Railway Timetable Rescheduling

Unexpected delays in train operations can cause a cascade of negative consequences in a high-speed railway system.In such cases,train timetables need to be rescheduled.However,timely and efficient train timetable rescheduling is still a challenging problem due to its modeling difficulties and low optimization efficiency.This paper presents a Transformer-based macroscopic regulation approach which consists of two stages including Transformer-based modeling and policy-based decisionmaking.Firstly,the relationship between various train schedules and operations is described by creating a macroscopic model with the Transformer,providing the better understanding of overall operation in the high-speed railway system.Then,a policy-based approach is used to solve a continuous decision problem after macro-modeling for fast convergence.Extensive experiments on various delay scenarios are conducted.The results demonstrate the effectiveness of the proposed method in comparison to other popular methods.

Last train timetable optimization for metro network to maximize the passenger accessibility over the end-of-service period

Purpose-This study aims to improve the passenger accessibility of passenger demands in the end-ofoperation period.Design/methodology/approach-A mixed integer nonlinear programming model for last train timetable optimization of the metro was proposed considering the constraints such as the maximum headway,the minimum headway and the latest end-of-operation time.The objective of the model is to maximize the number of reachable passengers in the end-of-operation period.A solution method based on a preset train service is proposed,which significantly reduces the variables of deciding train services in the original model and reformulates it into a mixed integer linear programming model.Findings-The results of the case study of Wuhan Metro show that the solution method can obtain highquality solutions in a shorter time;and the shorter the time interval of passenger flow data,the more obvious the advantage of solution speed;after optimization,the number of passengers reaching the destination among the passengers who need to take the last train during the end-of-operation period can be increased by 10%.Originality/value-Existing research results only consider the passengers who take the last train.Compared with previous research,considering the overall passenger demand during the end-of-operation period can make more passengers arrive at their destination.Appropriately delaying the end-of-operation time can increase the proportion of passengers who can reach the destination in the metro network,but due to the decrease in passenger demand,postponing the end-of-operation time has a bottleneck in increasing the proportion of passengers who can reach the destination.

A train timetable rescheduling approach based on multi-train tracking optimization of high-speed railways

Purpose–This paper aims to propose a train timetable rescheduling(TTR)approach from the perspective of multi-train tracking optimization based on the mutual spatiotemporal information in the high-speed railway signaling system.Design/methodology/approach–Firstly,a single-train trajectory optimization(STTO)model is constructed based on train dynamics and operating conditions.The train kinematics parameters,including acceleration,speed and time at each position,are calculated to predict the arrival times in the train timetable.A STTO algorithm is developed to optimize a single-train time-efficient driving strategy.Then,a TTR approach based on multi-train tracking optimization(TTR-MTTO)is proposed with mutual information.The constraints of temporary speed restriction(TSR)and end of authority are decoupled to calculate the tracking trajectory of the backward tracking train.The multi-train trajectories at each position are optimized to generate a timeefficient train timetable.Findings–The numerical experiment is performed on the Beijing-Tianjin high-speed railway line and CR400AF.The STTO algorithm predicts the train’s planned arrival time to calculate the total train delay(TTD).As for the TSR scenario,the proposed TTR-MTTO can reduce TTD by 60.60%compared with the traditional TTR approach with dispatchers’experience.Moreover,TTR-MTTO can optimize a time-efficient train timetable to help dispatchers reschedule trains more reasonably.Originality/value–With the cooperative relationship and mutual information between train rescheduling and control,the proposed TTR-MTTO approach can automatically generate a time-efficient train timetable to reduce the total train delay and the work intensity of dispatchers.

High-Speed Railway Train Timetable Conflict Prediction Based on Fuzzy Temporal Knowledge Reasoning

Trains are prone to delays and deviations from train operation plans during their operation because of internal or external disturbances. Delays may develop into operational conflicts between adjacent trains as a result of delay propagation, which may disturb the arrangement of the train operation plan and threaten the operational safety of trains. Therefore, reliable conflict prediction results can be valuable references for dispatchers in making more efficient train operation adjustments when conflicts occur. In contrast to the traditional approach to conflict prediction that involves introducing random disturbances, this study addresses the issue of the fuzzification of time intervals in a train timetable based on historical statistics and the modeling of a high-speed railway train timetable based on the concept of a timed Petri net. To measure conflict prediction results more comprehensively, we divided conflicts into potential conflicts and certain conflicts and defined the judgment conditions for both. Two evaluation indexes, one for the deviation of a single train and one for the possibility of conflicts between adjacent train operations, were developed using a formalized computation method. Based on the temporal fuzzy reasoning method, with some adjustment, a new conflict prediction method is proposed, and the results of a simulation example for two scenarios are presented. The results prove that conflict prediction after fuzzy processing of the time intervals of a train timetable is more reliable and practical and can provide helpful information for use in train operation adjustment, train timetable improvement, and other purposes.

COMMENTS ON THE THERAPEUTIC TIMETABLE OF ISCHEMIC APOPLEXY AND SUPER-EARLY INTERVENTION OF ACUPUNCTURE

According to the pathological process of ischemic apoplexy, which involves its onset and development, this paper expounds the great significance of adopting various active and effective measures within the therapeutic timetable for favorable prognosis and improvement of apoplexy. The author’s viewpoints differ from the conventional thinking towards the management of apoplexy, stressing super early intervention with acupuncture.

A Multiple-Neighborhood-Based Parallel Composite Local Search Algorithm for Timetable Problem

This paper presents a parallel composite local search algorithm based on multiple search neighborhoods to solve a special kind of timetable problem. The new algorithm can also effectively solve those problems that can be solved by general local search algorithms. Experimental results show that the new algorithm can generate better solutions than general local search algorithms.

Optimization Techniques in University Timetabling Problem:Constraints,Methodologies,Benchmarks,and Open Issues

University timetabling problems are a yearly challenging task and are faced repeatedly each semester.The problems are considered nonpolynomial time(NP)and combinatorial optimization problems(COP),which means that they can be solved through optimization algorithms to produce the aspired optimal timetable.Several techniques have been used to solve university timetabling problems,and most of them use optimization techniques.This paper provides a comprehensive review of the most recent studies dealing with concepts,methodologies,optimization,benchmarks,and open issues of university timetabling problems.The comprehensive review starts by presenting the essence of university timetabling as NP-COP,defining and clarifying the two formed classes of university timetabling:University Course Timetabling and University Examination Timetabling,illustrating the adopted algorithms for solving such a problem,elaborating the university timetabling constraints to be considered achieving the optimal timetable,and explaining how to analyze and measure the performance of the optimization algorithms by demonstrating the commonly used benchmark datasets for the evaluation.It is noted that meta-heuristic methodologies are widely used in the literature.Additionally,recently,multi-objective optimization has been increasingly used in solving such a problem that can identify robust university timetabling solutions.Finally,trends and future directions in university timetabling problems are provided.This paper provides good information for students,researchers,and specialists interested in this area of research.The challenges and possibilities for future research prospects are also explored.

考虑Pareto最优的列车运行图与维修天窗协调优化

列车运行图与维修天窗之间的冲突始终无法避免,且维修天窗开设时间的长短显著影响列车总运行时间。针对此问题,综合考虑维修天窗对列车运行造成的限速约束、车站到发线数量约束等,建立列车总运行时间最小,以及维修天窗实际开设时长与理想时长总偏差最小的双目标混合整数规划模型;对困难约束设置中间辅助变量将模型线性化以提高求解效率,并设计约束转换算法求解双目标模型的Pareto最优;微观化处理铁路线,将站内资源和站间资源细化为一系列行车资源单元,得到更加符合实际旅客运输需求的运行图。以某地区铁路线夜间开行列车及维修天窗开设计划为研究背景,调用商业软件求解双目标函数模型的Pareto最优,并对双目标模型的最小支配解和最优支配解进行对比分析;针对最优支配解下的列车进入、离开行车资源单元的时间、停站作业时间及维修天窗的开始时间及开设时长,绘制列车运行图。求解结果表明:模型在满足维修天窗最小开设时长的同时,能够兼顾列车运行总时间最小和维修天窗开设时长更充裕。基于最优支配解绘制的列车运行图表明:微观路网下的列车运行时刻表优化结果更符合实际旅客运输生产作业需要。研究结果可为铁路运营管理部门进一步优化列车运行图编制与维修天窗开设提供参考。

前继列车晚点条件下换乘客流的接续方案优化

针对列车运行受到干扰下运行图调整问题,对前继列车晚点条件下换乘客流的接续方案进行研究,将换乘客流和在车客流影响最小化作为优化目标对列车运行图进行相应调整.在前继列车晚点条件下,假定换乘客流既可以换乘到原接续列车,也可以选择其他符合换乘条件的列车,建立所有列车上的旅客延误时间最小和换乘失败旅客数量最少的多目标模型,以期在保证换乘成功的前提下最小化列车运行计划调整.以京沪高铁和郑徐高铁上行部分区段为例验证模型的有效性.研究结果表明:当考虑列车总调整时间和换乘客流失败成本相互制约关系时,可以通过增加列车运行调整时间来提高换乘成功客流量;当考虑所有列车上的旅客延误时间和换乘旅客失败成本相互制约关系时,可以通过牺牲部分换乘旅客来减少在车旅客的总延误时间;当其他接续列车空余席位大幅减少时,接续方案倾向于调整原接续列车的到发时刻,避免换乘失败的旅客量大幅增加.

考虑动态随机客流的城市轨道交通列车时刻表仿真优化

为更加精准刻画动态随机客流与列车时刻表编制间的复杂匹配过程,综合考虑乘客动态随机到达过程、列车容量限制、车内拥挤度对乘客上下车速率影响、停站时间动态调整和列车运行安全防护等重要运营特征,构建城市轨道交通列车运行离散仿真模型。在此基础上,建立以最小化乘客平均候车时间为目标的随机非线性优化模型,并设计一种基于仿真的有限差分随机逼近算法,用于优化城市轨道交通发车方案。以国内某条线路的实际运营数据为例验证仿真模型及优化方法,结果表明所构建模型及算法具有较好的优化效果和计算效率,优化后的列车实绩运行时刻能更好地适应客流需求的动态随机性,且在不增加发车频次的前提下有效降低乘客平均候车时间,提升城市轨道交通运营管理水平。

考虑风向概率特征的航班时刻优化方法

在航班时刻表进行实际运行过程中,风向的变化对航班到达终端区共用航路点的时间造成影响,进而造成航路点的容量过载或容量浪费。因此,根据风向的统计概率对航班时刻进行调整,目的是制定在一定程度上能减少共用航路点的容量过载或容量浪费的航班时刻表。根据风向对离场航班跑道分配的影响提出基准风向的概念,并基于航季中各月份在过去5年间的机场基准风向概率预测了下一年各月的机场基准风向概率,并根据各月的基准风向概率特征进行聚类。在聚类结果的基础上,以风向变化对航路点流量的影响程度为目标函数,建立考虑风向概率特征的航班时刻优化模型,并将ε-约束法与改进粒子群优化算法(particle swarm optimization, PSO)结合提出ε-约束(ε-constraint method)-PSO组合算法实现多目标模型的求解,以北京终端区的离场航班为研究对象进行验证。结果表明:相比初始航班时刻表,共用航路点小时流量的最大值减少了12%,在不同基准风向时的共用航路点流量方差分别降低49%和56%;相比线性加权求和的方法,该方法可以实现共用航路点的溢出航班总量减少70%。结果表明:在考虑风向概率特征的条件下,该模型可以在一定程度上使共用航路点的流量更均衡,减少出现共用航路点容量过载或容量浪费的现象,减轻航路点流量受风向影响的程度。

轨道交通单线接运电动公交调度优化模型

针对乘客由轨道交通换乘接运电动公交过程中等待时间长、候车成本高等问题,提出一种面向高峰时段乘客换乘情况的轨道交通接运电动公交的时刻表优化方法。通过分析换乘过程,以换乘乘客等候时间成本、换乘失败成本、电动公交使用成本和充电成本共4项成本之和最小为目标函数,以电动公交的发车顺序、换乘乘客的等待意愿、电动公交充放电特性对行驶里程产生的影响等作为约束条件,构建混合整数非线性规划模型。在接运公交的运输需求方面,考虑了除换乘乘客外本地乘客出行需求变化对接运电动公交时刻表的影响。最后提出一种混合人工蜂群算法求解模型,通过与遗传算法、粒子群算法的对比,进行了算法的敏感性分析。结果表明:目标函数总成本为1355.32元,相比原成本降低了23.56%,其中,换乘乘客等候时间成本为298.17元,换乘失败成本为84.03元,公交公司运营成本为867.40元,电动公交充电成本为105.71元,验证了构建的模型对时刻表优化问题的有效性。

基于加线技术的轨道交通临客开行计划优化模型

基于运行图加线技术构建适于计划性临客开行的运行线铺画方法.以运力与客流匹配度最大化、对原运行图调整最小化为优化目标,考虑行车安全、临客加线位置、开行时间域等约束,构建临客运行线铺画非线性整数规划模型,并对其线性转换以提高求解效率.以北京地铁8号线某大型活动散场大客流为场景进行案例分析,结果表明:所构建方法能够在既有运行图上有效铺画临客运行线,满足不同行车密度及停站模式下的临客开行需求.建议在较高密度行车条件下不采用“空车直达”停站模式.

城市轨道交通多编组列车时刻表和车底周转综合优化研究

为缓解城市轨道交通高峰客流拥挤,减少平峰运力浪费,本文研究多编组列车混跑场景下的列车时刻表和车底周转综合优化问题。基于动态变化的OD客流需求和多类型的行车资源,以最小化乘客等待时间和列车运行费用为优化目标,以开行列车总数、列车时刻表、编组类型、列车出入段情况和车底接续关系为决策变量,考虑时刻表、车底衔接、车底资源、折返线占用和列车能力等约束,构建城市轨道交通多编组列车时刻表和车底周转综合优化模型。根据开行列车总数可变的特点,设计变长度非支配排序遗传算法Ⅱ求解双目标的帕累托集合。以某地铁线路为例进行案例分析,结果表明:相比于单一编组方案,优化的多编组方案可同时降低26.16%和25.75%的乘客总等待时间和列车运行费用;优化后的列车平均满载率增加了1.3%~9.6%,进一步提高了运输能力和客流需求的匹配度。

高速铁路动卧开行日列车运行图与维修天窗协调优化

高速铁路夜间开行的动卧列车会与综合维修天窗在时间和空间上产生冲突。协调行车和天窗对线路通过能力的占用,能够优化铁路运力资源配置,提升列车服务质量和天窗作业效率。对动卧列车等线开行模式与分段矩形天窗的配合进行研究。考虑到车站夜间例行安排的维修施工,将天窗单元分类为“车站天窗”与“区间天窗”,二者组合构成维修调度命令中的天窗分段。采用整数规划方法构建动卧开行日列车运行图和维修天窗协调优化模型,最小化相邻天窗错位时间和列车总旅行时间,以减少天窗分段数量,更利于调度命令传达,作业灵活且安全地实施,同时保证动卧列车开行质量。运用图着色问题推论添加车站到发线能力约束以满足列车可用到发线数量的限制。代入京广高铁动卧列车案例,使用Gurobi实现求解。综合优化的运行图上,车站天窗和区间天窗构成11个天窗分段,比实际少13个,各维修工区管辖区段内的天窗计划更加简化;7对动卧列车的车站到发、区间运行均不与车站天窗、区间天窗冲突;列车的总旅行时间减少了173 min,列车停站次数由82次减少到66次。多组案例讨论了车站天窗、天窗错位、到发线数量等因素对列车等线位置和时间的影响。研究结果为进一步优化高铁动卧列车运输组织和维修天窗开设提供参考建议。

地铁潮汐客流下变编组列车不成对运行图优化

方向不均衡分布的潮汐客流在大城市的地铁系统中较为常见,新兴的变编组技术为应对潮汐客流提供了新方法。为解决潮汐客流下客流需求与运力难以匹配的问题,针对布置双端车辆段的双向线路,考虑车底在线路两端折返线进行在线解编联挂作业,协调列车时刻表、编组方案与车底周转计划,编制变编组下列车不成对运行图。从列车到发时刻、客流、车底周转和折返线占用4个方面设置约束,以最小化乘客和企业总成本与出入段作业接续占比加权和为目标,构建变编组下的不成对运行图优化模型。综合运用变邻域搜索算法和贪心算法对模型进行求解,前者更新运行图中的车次到发时刻和车底编组,后者求解对应的车底周转计划。为验证模型有效性,对具有潮汐客流特征的北京地铁八通线一期工程线路进行案例分析。研究结果表明:早高峰时段列车解编、联挂作业分别主要发生在大、小客流方向终点站;变编组较单一大编组和多编组分别降低了8.28%和4.82%的乘客和企业双方总成本,在保证服务质量的同时节约了走行车公里和运用车数;变编组较多编组减少了26.02%的出入段作业接续占比和20.51%的车底出入段作业次数,通过在线解编联挂作业有效减少了编组类型对车底接续的影响,提高了车辆利用率。研究成果可为列车编组方案的选择和变编组的运营组织提供依据。

考虑高平峰差异的城市轨道交通节能运行图优化研究

降低能耗是轨道交通绿色低碳、可持续发展的关键。基于“高峰快跑保运力、平峰慢跑降能耗”的思想,本文提出在高平峰时段设置差异化标尺的节能运行图优化方法。首先,根据区间运行时间与列车牵引能耗负相关关系,结合客运服务需求特征,以列车周转牵引能耗最小化为目标构建列车运行标尺优化模型;其次,针对差异化标尺导致的过渡时段列车服务不均匀问题,以列车到站间隔标准差最小化为目标构建列车运行线调整模型;最后,选取福州地铁1号线开展实证分析。结果表明:高平峰差异化标尺节能运行图能够在不改变运力供给、车底运用数量的前提下有效降低全日列车牵引能耗12.56%。所构建方法具有良好的应用价值和可操作性,能够为运营管理人员编制节能运行图提供方法支持。

考虑跨线列车的运行图与天窗一体化模型与算法

列车运行图与维修天窗之间以及本线列车与跨线列车之间存在相互影响、相互制约的耦合关系。为了在编制运行图和安排维修天窗时充分考虑跨线列车的影响,用跨线列车的到达时刻与理想到达时刻的偏差衡量其时刻与理想时刻偏离程度。以列车总旅行时间最小、跨线列车理想时刻偏差最小和天窗开设时间最长为目标,建立考虑跨线列车的高速铁路运行图与综合维修天窗一体优化的多目标整数规划模型。根据问题特点构建基于二维排序编码的NSGA-II算法进行求解,对随机选取一行的顺序交叉方式难以满足越行规则和停站方案的情况采用“多列顺序交叉+未交叉部分重新生成+随机越行”的交叉策略,并设计了运行线冲突疏解方法。结合模拟高速铁路线路算例进行有效性验算,得到Pareto最优解。与初始运行图对比分析结果显示,优化后的运行图可以使列车总旅行时间减少194 min(下降6.0%);跨线列车理想时刻偏差为3447 min;天窗开设时间增加1655 min(上升19.3%)。算例结果证明,所建模型和设计的求解算法对优化列车运行图和维修天窗均有效。提出的考虑跨线列车的运行图与天窗一体化模型和方法,可以缩短列车旅行时间,增加天窗开设时间以预留铁路通过能力,同时使跨线列车时刻在一个满意范围内,可为铁路运营决策者编制列车运行图和设置维修天窗提供有价值的决策支持。

基于Dueling DQN算法的列车运行图节能优化研究

通过优化地铁时刻表可有效降低地铁牵引能耗。为解决客流波动和车辆延误对实际节能率影响的问题,提出列车牵引和供电系统实时潮流计算分析模型和基于Dueling Deep Q Network(Dueling DQN)深度强化学习算法相结合的运行图节能优化方法,建立基于区间动态客流概率统计的时刻表迭代优化模型,降低动态客流变化对节能率的影响。对预测Q网络和目标Q网络分别选取自适应时刻估计和均方根反向传播方法,提高模型收敛快速性,同时以时刻表优化前、后总运行时间不变、乘客换乘时间和等待时间最小为优化目标,实现节能时刻表无感切换。以苏州轨道交通4号线为例验证方法的有效性,节能对比试验结果表明:在到达换乘站时刻偏差不超过2 s和列车全周转运行时间不变的前提下,列车牵引节能率达5.27%,车公里能耗下降4.99%。