Dynamic train dwell time forecasting:a hybrid approach to address the influence of passenger flow fluctuations

Train timetables and operations are defined by the train running time in sections,dwell time at stations,and headways between trains.Accurate estimation of these factors is essential to decision-making for train delay reduction,train dispatching,and station capacity estimation.In the present study,we aim to propose a train dwell time model based on an averaging mechanism and dynamic updating to address the challenges in the train dwell time prediction problem(e.g.,dynamics over time,heavy-tailed distribution of data,and spatiotemporal relationships of factors)for real-time train dispatching.The averaging mechanism in the present study is based on multiple state-of-the-art base predictors,enabling the proposed model to integrate the advantages of the base predictors in addressing the challenges in terms of data attributes and data distributions.Then,considering the influence of passenger flow on train dwell time,we use a dynamic updating method based on exponential smoothing to improve the performance of the proposed method by considering the real-time passenger amount fluctuations(e.g.,passenger soars in peak hours or passenger plunges during regular periods).We conduct experiments with the train operation data and passenger flow data from the Chinese high-speed railway line.The results show that due to the advantages over the base predictors,the averaging mechanism can more accurately predict the dwell time at stations than its counterparts for different prediction horizons regarding predictive errors and variances.Further,the experimental results show that dynamic smoothing can significantly improve the accuracy of the proposed model during passenger amount changes,i.e.,15.4%and 15.5%corresponding to the mean absolute error and root mean square error,respectively.Based on the proposed predictor,a feature importance analysis shows that the planned dwell time and arrival delay are the two most important factors to dwell time.However,planned time has positive influences,whereas arrival delay has negative influences.

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.

Modeling and simulation of high-speed passenger train movements in the rail line

In this paper, we propose a new formula of the real-time minimum safety headway based on the relative velocity of consecutive trains and present a dynamic model of high-speed passenger train movements in the rail line based on the proposed formula of the minimum safety headway. Moreover, we provide the control strategies of the high-speed passenger train operations based on the proposed formula of the real-time minimum safety headway and the dynamic model of highspeed passenger train movements. The simulation results demonstrate that the proposed control strategies of the passenger train operations can greatly reduce the delay propagation in the high-speed rail line when a random delay occurs.

A simulation model for estimating train and passenger delays in large-scale rail transit networks

A simulation model was proposed to investigate the relationship between train delays and passenger delays and to predict the dynamic passenger distribution in a large-scale rail transit network. It was assumed that the time varying original-destination demand and passenger path choice probability were given. Passengers were assumed not to change their destinations and travel paths after delay occurs. CapaciW constraints of train and queue rules of alighting and boarding were taken into account. By using the time-driven simulation, the states of passengers, trains and other facilities in the network were updated every time step. The proposed methodology was also tested in a real network, for demonstration. The results reveal that short train delay does not necessarily result in passenger delays, while, on the contrary, some passengers may get benefits from the short delay. However, large initial train delay may result in not only knock-on train and passenger delays along the same line, but also the passenger delays across the entire rail transit network.

A Distributionally Robust Optimization Method for Passenger Flow Control Strategy and Train Scheduling on an Urban Rail Transit Line

Regular coronavirus disease 2019(COVID-19)epidemic prevention and control have raised new require-ments that necessitate operation-strategy innovation in urban rail transit.To alleviate increasingly seri-ous congestion and further reduce the risk of cross-infection,a novel two-stage distributionally robust optimization(DRO)model is explicitly constructed,in which the probability distribution of stochastic scenarios is only partially known in advance.In the proposed model,the mean-conditional value-at-risk(CVaR)criterion is employed to obtain a tradeoff between the expected number of waiting passen-gers and the risk of congestion on an urban rail transit line.The relationship between the proposed DRO model and the traditional two-stage stochastic programming(SP)model is also depicted.Furthermore,to overcome the obstacle of model solvability resulting from imprecise probability distributions,a discrepancy-based ambiguity set is used to transform the robust counterpart into its computationally tractable form.A hybrid algorithm that combines a local search algorithm with a mixed-integer linear programming(MILP)solver is developed to improve the computational efficiency of large-scale instances.Finally,a series of numerical examples with real-world operation data are executed to validate the pro-posed approaches.

Technical Feasibility Study of Passenger Rail Service along the West Route between Las Vegas and Los Angeles

The study evaluates the feasibility of running passenger train service from Las Vegas, NV on the Union Pacific Railroad (UPRR), to Barstow, on the Burlington Northern Santa Fe (BNSF) track, to Mojave on UPRR track again, and to Lancaster connecting Metrolink to their destinations in Southern California. In this study, the railroad infrastructure was inventoried and issues related to running the passenger service were identified. Passenger train operation was evaluated based on the Rail Traffic Controller (RTC) simulation model. The performance measures of passenger trains including travel time, overall delay and average speed are analyzed. The uncertainty in freight flow and its impact on providing the passenger service is addressed by conducting a sensitivity analysis. The conclusion is that the existing railroad infrastructure is sufficient to provide a passenger train service from Las Vegas to Los Angeles. From an operational perspective, the passenger train is not expected to influence freight trains’ performance on the existing railroads. When freight train flows are increased to 50%, the influence of passenger train service on the freight operation is still minimal. This study recommends restoring a platform at the Las Vegas Station. At the Mojave Station, special care should be given on running the passenger trains where there is no direct railroad connection from BNSF to UPRR. Platforms and walkways require construction at the Lancaster Station for transferring passengers between the Metrolink trains and X-Train. Transferring the passenger train at this station involves stopping the train on mainline and coordinating the operations between different railroads.

高速铁路列车乘客上下车策略分析及仿真模拟

为获得高速铁路列车乘客最优的上下车策略,选取CRH380AL型高速列车二等座车厢,构建乘客上下车策略仿真模型。针对不同的上下车策略,分析人员携带行李比例、上车人数的落座时间和过道密度,量化乘客上下车的效率和舒适度。结果表明:低密度时,乘客落座时间在双车门行策略和双车门倒金字塔策略下变化差异不明显。随着乘客人数和携带行李比例的增加,双车门倒金字塔策略的乘客落座时间最短。30人上车场景时,双车门倒金字塔策略车厢过道区域的最大密度和平均密度均为最低。在上下车人数为50人、携带行李比例为40%时,双车门倒金字塔策略的过道密度相较于双车门行策略和双车门斯蒂芬法策略较低,此时乘客舒适度较好。

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。

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.

基于乘客服务水平和安全的城市轨道交通扣车方案评价

[目的]扣车是城市轨道交通发生长时间初始延误条件下经常采用的列车运行方案。为了评价该方案的合理性,需构建基于乘客服务和安全的扣车方案评价模型,并对扣车方案进行评价。[方法]考虑到乘客出行规律的复杂性和特殊性,制定了城市轨道交通扣车方案。构建了基于最大发车间隔、列车总延误时间、列车均衡性、终到延误列车数、滞留乘客数及乘客总旅行时间的扣车方案评价模型。选取上海某条轨道交通线路的4个区间,以早高峰小时的实际OD(起讫点)客流量为例进行计算。[结果及结论]计算表明,方案6下的综合评价指标最小,为最优扣车调整方案。该方案可以有效提高列车运行图的均衡性,减少滞留乘客的数量,降低车站的安全隐患。

基于可预知大客流的城市轨道交通列车加开计划调整模型与算法

[目的]节假日、大型文体活动等事件会导致城市轨道交通线路产生可预知大客流,需在确保列车运行安全的前提下,根据客流预测情况在已有列车运行图中加开列车,以期在运营成本、运营组织复杂度和乘客服务水平之间取得平衡。[方法]采用事件-活动网络,对列车运行过程进行了形式化描述,建立了3个目标函数及相关的约束条件,进而构建了独立运用策略下列车加开计划调整模型、套跑混合运用策略下列车加开计划调整模型。在此基础上,提出了基于混合遗传禁忌的搜索算法。结合深圳地铁11号线的实际运营数据,在高峰时段和非高峰时段分别建立了不同的算例场景,以验证所建模型和算法的有效性。[结果及结论]所建模型支持不同运营时段、不同运行交路、不同车底接续策略下列车加开需求,在可预知大客流的运营调整上具有适用性。在车辆资源相对有限的情况下,为有效缓解高峰时段的客流压力,可灵活采用大小交路套跑混合运用策略,使线路的运输能力最大化。

场景理论视域下地铁列车旅客界面需求研究

目的 为了提高乘客在地铁车厢内的乘车体验,优化地铁列车旅客界面的合理性与便捷性。方法 以场景理论为基础,采用问卷调查和用户访谈等方法,收集现有地铁乘客的用户需求,提炼客观场景。运用Kano模型明确地铁旅客界面设计的用户需求权重,基于对用户需求权重的考量及QFD质量屋展开,功能需求被转化为产品特征,用以分析用户需求与产品特征之间的关系。结果 归纳了典型地铁乘车场景,构建地铁列车乘客界面的用户需求与产品特征之间的关联矩阵,并以此为基础分别从车厢布局、信号灯提示、扶握系统和导视系统四个方面,提出了地铁列车旅客界面的配置策略。结论 从场景分析的角度结合多重定量、定性分析方法,验证场景理论对挖掘地铁乘车需求具有一定指导作用,并提出地铁列车旅客界面在提升旅客乘车体验方面的初步建议。

面向车次取消的票额分配与停站方案组合优化研究

当列车因严重干扰而取消时,大量旅客将在车站滞留,同时铁路公司收益也会受到一定影响,合理的票额分配是解决此类问题的有效方法。在差异化设置换乘旅客优先权基础上,构建了以铁路公司收益最大化和偏离原时刻表最小化为目标的票额分配和停站方案组合优化模型,并通过增加不等式组约束条件将其线性化;利用求解器GUROBI对上述模型进行求解并分析算例。研究结果表明:当列车因干扰而取消时,与不采用任何措施相比,灵活停站策略能有效地减少滞留旅客数量19.7%以上;铁路公司收益增加21.8%以上。

基于多层次客流需求的高铁列车谱系结构优化设计

高速铁路的高质量服务特性激发了大量不同类型旅客的多样化出行需求。当前铁路旅客列车开行数量日益增多,但提供的客运产品分类较少,列车谱系化特征不明显,难以适应旅客的多样性需求。设计具有显性差异特征的铁路客运产品,满足不同类型旅客在经济、快捷、方便、舒适等方面的异质性出行需求,实现铁路客流分类与产品分类的高效匹配,对新时代铁路旅客运输发展具有重要意义。结合当前我国高速铁路网列车开行结构现状,并考虑路网中各线路具有不同的资源、能力、客流特征,基于铁路客运产品谱系化思想和发展趋势,从列车停站模式、旅速、运距、频率几个维度将既有列车种类进一步拓展划分为11类谱系,分析多层次旅客对谱系列车的选择,运用整数规划并结合客流分配技术,构建决策各线路区段上从11类谱系中选择列车开行种类、范围和数量的列车谱系结构优化模型,达到客流类型与列车谱系结构之间的有效匹配。以我国部分高铁路网为例开展案例研究,研究结果表明:优化后的列车开行结构明显改善,各高铁线路上列车谱系结构差异化分布特征明显;不同层次客流旅速、列车谱系和客流层次对应、列车客座率、“车-流”运距比等指标均表现出较好的“车-流”匹配效果。在高铁列车大规模成网运营条件下,研究结果可为设计具有谱系特征的列车产品提供方法和技术指导。

考虑乘客旅行时间价值的市域轨道交通列车开行方案

[目的]鉴于市域铁路的功能定位,以及当前经济社会发展下乘客对出行快捷性、舒适性和经济性需求不一致的特性,需对考虑乘客旅行时间价值的市域铁路列车开行方案进行研究。[方法]构建了考虑乘客旅行时间价值的列车开行方案模型。该模型以乘客出行成本和列车开行成本为目标函数,以列车定员、站间客流及列车开行数量等为主要约束条件,同时采用帕累托模型对不同乘客的时间价值和不同编组列车的开行成本进行标定。以上海市域轨道交通机场联络线为研究背景,考虑了4节与8节两种列车编组形式,对虹桥站至上海东站站单方向高峰小时的列车开行方案进行了计算分析及评价。[结果及结论]考虑乘客旅行时间价值的列车开行方案能最大限度地减少乘客出行成本,更好地满足不同乘客的出行时间需求;增加乘客旅行时间价值较低的乘客旅行时间,缩短乘客旅行时间价值较高的乘客旅行时间,能更好地发挥列车开行方案的经济效益。

基于“互联互通”的嘉兴至枫南市域铁路列车开行方案研究

嘉兴至枫南市域铁路是浙江省嘉兴市第一条市域铁路,是长三角地区多层次轨道交通网络中重要线路之一,与嘉善至西塘市域铁路互联互通。“互联互通”条件下市域铁路网络跨线运营是未来的发展趋势,列车开行方案设计是制定市域铁路运输组织方案的前提,做好“互联互通”条件下列车开行方案的制定是运营期市域铁路运营管理的一项主要工作,也是市域铁路规划设计阶段需要重点考虑的问题。在研究嘉兴至枫南市域铁路概况的基础上,对其进行数学建模。在分析市域铁路不同列车种类的基础上,以所有乘客总体出行时间效益最大化为优化目标,应用数学规划、多目标优化的方法构建了一套“互联互通”条件下列车开行方案优化模型,包括市域铁路直达列车数量计算模型、大站快车停站选择模型和大站快车、站站停列车数量计算模型。在研究嘉兴至枫南市域铁路线路互联互通的基础上,依据初期早高峰时段客流预测结果,计算得到初期早高峰时段嘉兴南站—南枫线段采用快慢车混跑方式,大站快车停车站为科技城站、嘉善站,开行直达列车1对/h,大站快车3对/h,站站停列车5对/h;嘉兴南站—示范区线段采用快慢车混跑方式,大站快车停车站为科技城站、嘉善站、西塘站,开行大站快车2对/h,站站停列车6对/h,进而确定嘉兴至枫南市域铁路列车开行方案。

基于可解释机器学习框架的列车乘车区段客流分布预测方法

为解释客运产品特征对列车乘车区段客流分布预测的影响,本文提出一种基于可解释机器学习框架的高速铁路列车乘车区段客流分布预测方法。首先,提出基于梯度提升树模型的高速铁路列车乘车区段客流分布预测框架,构建不同梯度提升树模型(GBDT、XGBoost、LightGBM及CatBoost)的高速铁路列车乘车区段客流分布预测模型;其次,计算特征贡献重要度,基于SHAP(SHapley Additive exPlanations)方法实现特征变量优化,揭示单一特征和交互特征与列车乘车区段客流分布预测的非线性关系。北京南—上海虹桥间列车客流分布预测结果表明:4种模型可精准预测客流分布结果,GBDT,XGBoost,LightGBM及CatBoost在测试集的决定系数分别为0.9664,0.9601,0.9680及0.9715;特征优化后,按贡献重要度排序依次为标杆车,票价,旅行时间,日期,星期,车次及出发时间;特征优化后,CatBoost-7模型在验证集中的决定系数为0.9458;日期和标杆车对客流分布预测呈现非线性正相关,旅行时间对客流分布预测呈现非线性负相关,低旅行时间、高票价及出发时间整点的标杆车对客流分布预测产生正向影响。本文研究结果能够为高速铁路客运产品设计提供一定参考价值。

铁路客车非金属材料VOCs释放规律试验研究

为了研究温度对铁路客车典型非金属材料中挥发性有机化合物释放量的影响,基于多气固比法和数据拟合法,对典型非金属材料(重防腐涂料、地板布、玻璃纤维增强塑料)的挥发性有机化合物释放规律进行了试验研究.首先,分别测量了4种不同气固比条件下的挥发性有机化合物浓度,得到挥发性有机化合物释放关键参数(初始可散发浓度和分配系数);然后,结合车辆工艺和运用场景,研究不同温度对挥发性有机化合物释放规律的影响.研究结果表明:材质的物理化学性能和温度是影响挥发性有机化合物释放特性的重要因素;温度从16℃升高至55℃,苯系物和醛类散发浓度均呈减小的趋势,重防腐涂料的初始可散发浓度减小为1.8%,其分配系数减小,地板布和玻璃钢的初始可散发浓度减小至0.3%以下,地板布的分配系数增大,玻璃钢的分配系数减小;苯系物是挥发性有机化合物的主要成分,其中以苯乙烯占比最大,甲苯、乙苯以及二甲苯占比排序规律不明显,并且未检测出苯;在铁道车辆烘焙法环保净化处理时,建议重防腐涂料的烘焙温度不小于55℃,地板布和玻璃钢的烘焙温度不小于45℃.

铁路集装箱客运化班列停站方案与运行图协同优化研究

铁路集装箱客运化系统对于改善我国铁路集装箱运输效率和提升铁路在高附加值货物运输市场的竞争力具有重要意义。在客运化系统中,集装箱班列的停站方案与运行图是班列运输组织计划的核心内容,前者确定各班列的停站模式,后者确定班列在各车站的出发与到达时间,二者之间相互影响,共同决定服务质量。因此,为提升运输组织计划编制效率,有必要在基于铁路通道的运营环境下,研究考虑不同运行区段的集装箱客运化班列停站方案与运行图协同优化问题。在满足箱流需求与符合列车运力的前提下,考虑具体的集装箱至列车的分配,将列车停站方案约束嵌入到列车运行图编制过程中,一方面使得停站方案与箱流分配方案相协调,另一方面保证列车在站停留时间能满足集装箱装卸作业的要求。以总列车停站时间最小为目标,综合考虑货物运到期限、车站到发线资源的分配、列车追踪间隔时间以及列车停站时间等约束,构建混合整数线性规划模型,并借助优化求解器CPLEX对模型进行求解。以实际中欧班列通道为案例进行实证分析,验证提出方法的有效性。实验结果表明,客运化系统能够充分发挥网络运输的优势,在仅牺牲部分列车能力利用率的前提下,具有列车开行频率高、运输集装箱数量多以及运到期限短的优点。

市域车客室空调通风系统仿真与优化研究

以某市域车客室空调通风系统为研究对象,提出了将客室、送风系统、回风系统、废气排风系统和新风系统整体耦合的新仿真模型,使仿真边界条件设置贴合实际工程。利用ANSYS Fluent软件,对市域车空调通风系统的气流组织和传热状况进行了仿真计算与优化。结果表明:优化前2台空调机组4个回风口的回风量分别为0.24、0.26、0.35、0.19 kg/s,回风量分布不均匀;废气排风道过于窄小,导致废气排风系统阻力高达629 Pa。鉴于此,对客室回风结构与废气排风结构进行了优化研究,优化后最大回风量与最小回风量仅相差0.05 kg/s,客室到废气排风出口流动阻力降低至120 Pa。客室内大部分区域温度范围为24~26℃,客室内部人员主要活动范围处于微风速,乘坐舒适性提升显著。本研究可以为市域车空调通风系统的优化设计提供依据。