Revenue management method and critical techniques of railway passenger transport

Purpose–Revenue management(RM)is a significant technique to improve revenue with limited resources.With the macro environment of dramatically increasing transit capacity and rapid railway transport development in China,it is necessary to involve the theory of RM into the operation and decision of railway passenger transport.Design/methodology/approach–This paper proposes the theory and framework of generalized RM of railway passenger transport(RMRPT),and the thoughts and methods of the main techniques in RMRPT,involving demand forecasting,line planning,inventory control,pricing strategies and information systems,are all studied and elaborated.The involved methods and techniques provide a sequential process to help with the decision-making for each stage of RMRPT.The corresponding techniques are integrated into the information system to support practical businesses in railway passenger transport.Findings–The combination of the whole techniques devotes to railway benefit improvement and transit resource utilization and has been applied into the practical operation and organization of railway passenger transport.Originality/value–The development of RMRPT would provide theoretical and technical support for the improvement of service quality as well as railway benefits and efficiency.

Time-variant fragility analysis of the bridge system considering time-varying dependence among typical component seismic demands

This paper presents a copula technique to develop time-variant seismic fragility curves for corroded bridges at the system level and considers the realistic time-varying dependence among component seismic demands. Based on material deterioration mechanisms and incremental dynamic analysis, the time-evolving seismic demands of components were obtained in the form of marginal probability distributions. The time-varying dependences among bridge components were then captured with the best fitting copula function, which was selected from the commonly used copula classes by the empirical distribution based analysis method. The system time-variant fragility curves at different damage states were developed and the effects of time-varying dependences among components on the bridge system fragility were investigated. The results indicate the time-varying dependence among components significantly affects the time-variant fragility of the bridge system. The copula technique captures the nonlinear dependence among component seismic demands accurately and easily by separating the marginal distributions and the dependence among them.

Sensitivity analysis method for increasing the passenger demands of parking and riding

Focused on finding out the relationship between passenger demands of P&R and its influencing factors, a nested-logit mode choice model was developed based on the characteristic of different modes and transfer rules. The utility functions were given respectively according to the characteristic of each alternative. Passenger demands of different modes between O-D pairs were obtained by making use of the binary logit model. Then an equilibrium model for different modes was proposed. Under this condition, the approximate relationship between passenger demands of different modes and their characteristic indexes was modeled by the sensitivity analysis method. Shift volume among different modes was achieved by utilizing this model when their characteristic indexes were changed. A case study indicates that the model and algorithm presented in this paper are effective.

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 Decade of Changes in the Transportation of Passengers： What Are the Factors That Affected the Demand？

Ten years of financial stability in Brazilian economy have gone. In this period, the regional transportation of passengers suffered exogenous impacts： economical crises, airport crises and great proportions＇ accidents, as well as impacts which were intern to the system： institutional changes （liberation of tariff promotions, many companies establishing themselves and also coming to bankruptcy）, creation of regulating institutions in the air transportation as well as the land transportation. Theoretically, it is expected that these changes have generated impacts in the demand for trips, since an environment regulated with more flexible prices and higher amounts of companies would generate a competitive environment in which the companies could struggle to attract their demand. On the other hand, the impacts which are exogenous to the system can generate responses in the sense of restoring the balance of demand. Thus, based on the theoretical experience, this article aims at analyzing empirically, through categorical variables, if there were impacts on the demand for regional trips in Brazil due to the internal or external changes. In order to perform this, monthly data from January, 1999 to December, 2009 are utilized and estimates are calculated making use of SUR （seemingly unrelated regressions）. As a result, we have the meaning of the internal and external impacts related to air and land transports, identifying that the worldwide economic crisis generated an impact at the level of the demand for transportation and also that the flexibility of tariffs allowed by ANTT （Ag^ncia Nacional de Transportes Terrestres） had an equal impact on the demand for land transportation.

Optimization method of urban rail train operational plan based on O-D time-varying demand

Purpose–Under the constraints of given passenger service level and coupling travel demand with train departure time,this study optimizes the train operational plan in an urban rail corridor to minimize the numbers of train trips and rolling stocks considering the time-varying demand of urban rail passenger flow.Design/methodology/approach–The authors optimize the train operational plan in a special network layout,i.e.an urban rail corridor with dead-end terminal yard,by decomposing it into two sub-problems:train timetable optimization and rolling stock circulation optimization.As for train timetable optimization,the authors propose a schedule-based passenger flow assignment method,construct the corresponding timetabling optimization model and design the bi-directional coordinated sequential optimization algorithm.For the optimization of rolling stock circulation,the authors construct the corresponding optimization assignment model and adopt the Hungary algorithm for solving the model.Findings–The case study shows that the train operational plan developed by the study’s approach meets requirements on the passenger service quality and reduces the operational cost to the maximum by minimizing the numbers of train trips and rolling stocks.Originality/value–The example verifies the efficiency of the model and algorithm.

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.

Combined heuristics for determining order quantity under time-varying demands

The time-varying demands for a certain period are often assumed to be less than the basic economic order quantity （EOQ） so that total replenishment quantity rather than economic order quantity is normally considered by most of the heuristics. This acticle focuses on a combined heuristics method for determining order quantity under generalized time-varying demands. The independent policy （IP）, abnormal independent policy （AIP） and dependent policies are studied and compared. Using the concepts of normal/abnormal periods and the properties of dependent policies, a dependent policy-based heuristics （DPH） is proposed for solving the order quantity problems with a kind of time-varying demands pattern under which the first period is normal. By merging the Silver-Meal （S-M） heuristics and the dependent policy-based heuristics （DPH）, a combined heuristics （DPH/S-M） is developed for solving order quantity problems with generalized time-varying demands. The experimentation shows that （1） for the problem with one normal period, no matter which position the normal period stands, the DPH/S-M could not guarantee better than the S-M heuristics, however it is superior to the S-M heuristics in the case that the demands in the abnormal periods are in descending order, and （2） The DPH/S-M is superior to the S-M heuristics for problems with more than one normal period, and the more the number of normal periods, the greater the improvements.

基于在线评论的乘用车需求趋势分析方法

提出一种基于在线产品评论的竞争情报挖掘方法,将网络评论文本中的关键信息应用于乘用车设计开发中。结合LDA和TF-IDF算法,从主题特征和情感态度特征获取不同车型评论主题情感倾向性估值,并对应2009—2019年的乘用车销量,构建用户偏好分析的面板数据模型。结果显示:该方法能够有效地提取网络评论情报信息;通过与年份的交互作用分析产品需求趋势,过去11年间消费者只对油耗的关注度降低了8.64%,对动力、空间、外观、内饰和舒适性的关注度分别上升了3.16%,6.80%,89.69%,13.38%和18.47%。其中,与生活品质更加密切的外观、内饰和舒适性等指标的增量远高于传统关注的动力和空间指标。这表明燃油经济性越来越不受关注,已不再成为消费者购买乘用车的决定性因素。

考虑需求不确定的响应式接驳公交自适应发车策略优化

传统公交系统融合网约出行的模式之一是需求响应式接驳公交,它定位于连接枢纽站点(如地铁车站、铁路车站或机场),服务客流的集散,其需求响应服务主要指灵活线路的门到门接送,但在发车策略方面仍主要沿用传统的基于时刻表或频率的运营方案。在乘客到达或需求订单不确定的情形下,固定发车间隔可能导致系统过饱和或低饱和等问题,为此,本文提出自适应发车策略,根据乘客累计到达量动态确定发车决策。具体地,引入载客量目标作为发车阈值,称为固定发车阈值和时变发车阈值,前者的发车阈值固定不变,而后者的发车阈值则随时间逐渐降低,对此,构建以期望广义系统成本最小化为目标的数学规划问题,并利用网格搜索方法获得最优解。数值分析结果表明,时变发车阈值模型相比于传统的等间隔发车模型和固定发车阈值模型在需求均值较低或变异系数较高的场景下优势显著,其广义系统成本节约百分比最高可达18%。灵敏度分析表明上述模型的性能也受到乘客的时间价值影响。自适应发车模型具有较广的应用潜力,适当修改后可用于其他形式的公共交通系统。

航空旅客出行需求强度异质性研究

为了研究旅客出行需求强度问题,采用问卷调查的方式采集数据,对回收的问卷进行数据整理,构建了潜在类别模型,并用软件mplus对出行人群的出行需求强度进行分类,得到结果如下:可通过旅客属性特征调查,将旅客分为4类人群,分别是高需求人群、中高需求人群、中低需求人群、低需求人群。对分类结果采用多元logistic回归分析,结果表示需求强度人群划分影响因素主要为职业、出行目的、票价提高是否出行等。根据回归结果分析可以将4类需求强度异质性做具体的分析。

低客流公交运营模式优化方法

为解决低客流传统公交线路运营效率低的问题,考虑低客流公交线路客流时空特性,提出了采用传统公交与需求响应公交(demand response transit,DRT)相结合的组合运营模式:全时段传统公交模式(M1)、分时段传统公交+DRT模式(M2)全时段DRT模式(M3)的优化思路。分析了不同运营模式的要素,考虑乘客空间、时间特征,构建基于三维时空聚类的站点规划模型;考虑车辆行驶成本、乘客时间成本,以总班次运营成本最小为目标,构建了带有时间窗的灵活型DRT线路与调度规划模型,运用遗传算法进行求解。以北京市某公交线路进行仿真实证,仿真结果表明:M2相比M1、M3分别降低了9.0%、23.0%的运营成本。研究可针对低客流公共交通服务的运营方案制定与优化提供技术参考。

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。

四网融合条件下以客流需求为导向的市域铁路运输组织优化建议

[目的]区域一体化建设对区域多层次轨道交通网络的融合发展提出了更高的要求。在四网融合趋势下,应充分考虑客流需求,以客流需求为导向进行市域铁路的运输组织优化。[方法]从长江三角洲等区域轨道交通四网融合背景和市域铁路在其中的功能定位出发,明确了轨道交通互联互通的内涵。从客运需求总量、市域铁路客流分担率、运力供给和客运需求的匹配性3个方面阐述了当前市域铁路面临的运输组织困境。以客流需求为导向,从战略层、策略层及运营层3个层面提出了市域铁路运输组织的优化建议。[结果及结论]当前市域铁路运输组织的3大制约因素分别为线路规划建设与客运需求的差异、市域铁路与其他运输方式竞争优势不明显、缺少客流导向的列车开行方案调整机制及方法。从战略层角度应强化顶层设计,形成一体化运输组织体系;从策略层角度应化竞为合,发挥多模式轨道交通网络化效应;从运营层角度应以人为本,实行多样化行车组织模式。

基于虚拟编组的城轨列车时刻表优化方法

针对高峰时段城市轨道交通客流与运力不匹配问题,考虑城市轨道交通客流时空分布特征、交通过饱和状态和车场车底数量限制,提出了基于虚拟编组的城轨列车时刻表优化方法。构造动态客流累计需求函数,预测不同时段客流量;考虑乘客需求、发车间隔、运行时间、车底数量、车底接续等约束条件,以列车在首站的发车时刻和各车次的编组方案为决策变量,以乘客平均等待时间与列车走行里程最小化为优化目标,建立了基于虚拟编组的城轨列车时刻表优化模型。针对原问题包含大量耦合约束条件,利用拉格朗日松弛算法将耦合性约束吸收至目标函数,将原问题分解为2个独立路径的子问题,降低问题的复杂度;再利用商业求解器求解子问题的下界解,并设计启发式算法求解子问题的上界可行解,得到原问题解的上下界。以上海地铁某线路为算例进行验证,结果表明:在高峰时段,所提动态客流累计需求函数与客流实际到达规律拟合度较高;固定编组模式下,非均匀发车时刻表相比于均匀发车时刻表,可降低24.15%的乘客平均等待时间和51.73%的滞留乘客等待时间;而所提虚拟编组列车时刻表相比于固定编组模式下非均匀发车时刻表,不仅可减少0.33%的列车运行里程,还可进一步减少16.95%的乘客平均等待时间和6.03%的滞留乘客等待时间。

基于需求弹性的多模式铁路客运票价优化模型及算法

为增强铁路客运市场竞争力、提高其运营收入,对多模式铁路客运票价的多目标系统优化问题进行了研究。采用变分不等式模型对不同铁路客运产品客运需求之间的平衡关系进行描述,采用灵敏度分析的方法给出了多模式铁路客运产品需求弹性的计算方法,并构建了多模式铁路客运的市场需求函数;在此基础上,综合考虑了铁路客运企业的市场需求、客运收入、利润以及旅客出行费用等多个优化目标,提出了多目标双层规划模型来描述多模式铁路客运票价的系统优化问题。最后,采用北京至天津的真实铁路客运数据对提出的模型进行验证。结果表明本文提出的票价优化方法可以有效兼顾客运需求、客运收入以及利润等多个指标,为铁路客运部门在不同市场竞争阶段制定科学合理的铁路客运票价体系提供参考和支撑。

适应客流波动的高铁运力动态调整策略与实践

高速铁路网的拓展和动车组运载工具数量的增加,使我国高铁客流呈现愈加复杂的变化规律。为了灵活、快速、精准地响应不同时期动态变化的客流需求,基于现阶段运力配置模式,首先揭示平峰时期以及包含季节性假期和国家法定节假日的高峰时期客流波动规律;其次剖析客运生产环节中涉及运力调整的原则、流程、资源和措施;最后根据不同时期、不同场景的客流波动规律,针对性地提出可适应平峰时期客流需求的“三批次”运力动态调整策略与高峰时期的“梯次投放”运力动态调整策略。该高铁运力动态调整策略,可满足市场化背景下客流需求的波动性与复杂性,为进一步完善复合场景下铁路运力精准配置策略提供参考。

潮汐客流需求驱动的地铁列车不成对运行图节能优化方法

针对城市轨道交通客流潮汐现象引起的大客流方向乘客滞留和小客流方向运力虚靡且牵引能耗高的问题,本文提出一种结合快慢车和变编组的不成对运输组织策略,并结合变编组技术提升运力供给灵活度的同时减少运能浪费,此外,开行部分跨站快车加速车底周转。基于此,构建开行方案、时刻表与车底运用计划协同优化模型,决策列车的双向开行频率、编组类型、停站方案、首站发车时刻及车底周转计划,从而最小化乘客的总旅行时间和全线总牵引能耗成本;并设计定制的变邻域搜索算法求解混合整数非线性规划模型。广州地铁14号线案例结果表明:与实际的成对运输方案相比,本文不成对运行方案可以将乘客总旅行时间减少6.52%,全线总牵引能耗降低34.20%,总目标函数减少11.40%;快车的开行加速了车底周转,减少了6列上线列车单元数量,可以更好地实现不成对运输组织;变编组技术可以进一步提升不成对运输计划的灵活性,大幅缩减上线列车单元数量,使线路的平均满载率提升约20%,线路总牵引能耗优化率提升约33%。结合快慢车和变编组技术的不成对运输策略实现运力精准灵活配置的同时节约列车运行能耗成本,达到运营降本增效的目的。

客运需求局部缩减条件下列车开行方案调整研究

因疫情或其他特定事件导致局部客运需求缩减时,铁路各OD之间客流规模均发生相应变化。如何保证铁路运输生产连续性来调整列车开行方案是铁路运营企业面临的难题。归纳了应对不同客运需求缩减场景的列车开行方案调整策略,包括列车停运、列车编组调整、列车运行区段调整、列车停站办理客运作业调整及关联售票调整。以此为基础,构建列车开行方案调整模型,对列车停运、列车停站不办理客运作业和列车编组调整综合决策,其中列车停运考虑整交路以实现决策在实际中的适用性。通过算例证明了模型的有效性,在实际线路案例中的结果显示,通过优化列车开行方案运力配置,调整后结果在供需匹配的基础上可以控制运营成本,减少需求缩减带来的损失。

齐河路/华绣路人行天桥方案研究

介绍齐河路/华绣路人行天桥工程概况,其主要特点是在道路建成后新建天桥,受紧邻居民区,学校,管线限制,修建造价高、难度大,通过Z型方案满足主要交通需求的情况下降低造价,详细阐述了设计方案,有关经验为今后同类型的工程提供借鉴参考。