Many existing studies on the robustness assessment of high-speed rail (HSR) networks use HSR services derived from timetable data as an alternative indicator of actual passenger flow. This alternative method has inherent limitations because of the various capacities and seat allocations among different HSR services operated between the same station pairs, leading to a biased evaluation of network robustness. Using an actual ticket sales dataset and HSR timetable data, this study integrates passenger volume into the robustness evaluation of China's HSR network. Moreover, this study proposes systematic methods to assess the robustness of HSR networks at the station and large-scale network levels, with procedure indicators. We design random failure and target attack simulation scenarios in un-weighted, travel-time-weighted, and flow-weighted networks. Simulation is con-ducted from 2014 to 2016, and with a medium-and long-term HSR network plan in China. Using the method-ological advances and empirical studies, we find that China's HSR network became less robust first, and then enhanced robustness strength during network expansion. The significance of integrating passenger flow data in the robustness assessment is confirmed, as the same attack strategy may affect the total travel time and the number of passengers delivered differently. In addition, the proposed indicators are useful for capturing the procedure failure of HSR networks and critical stations.

高速铁路网络中列车开行数量多且单列车服务的OD对数量大,给客票定价和票额分配综合优化问题的求解造成了很大困难.本文基于历史购票曲线,设计将服务相同OD对的列车划分为若干类的聚类方法,提出面向大规模客票定价和票额分配综合优化问题的分类定价策略,构建高速铁路客票分类定价和票额分配综合优化模型并设计求解算法.以包含上下行共567列车的高速铁路网络为例进行计算分析,结果表明:与既有方法相比,引入分类定价策略的高速铁路列车票价票额综合优化方法,不仅能提高运输企业客票收入,还可以节省大规模问题的求解时间.

Driven by the regional integration, intercity economic complementarity and cooperation are enhanced in the context of megalopolization. Due to the increasing interaction and intensified connection between cities, this study attempts to investigate the impact of megalopolization on intercity human mobility from the perspective of complementary employment composition. The variation of differences in the composition of employment by industry sector between cities is identified through socioeconomic statistics in the recent decade. By using passenger flow data generated from ticket sales records, this study explores how economic complementarity affects high-speed rail (HSR) volume considering spatial dependence in megalopolises. The results show that HSR volume tends to be higher between cities with more distinct employment composition, and it is consistent with the existing studies that population size and economic output are positively correlated to the HSR passenger demand. Finally, the implication of different economy planning strategies is assessed to further highlight the interdependence between regional economic patterns and transportation activities.

This paper proposes an algorithm for solving the schedule-based passenger assignment problem, with explicit consideration of continuous time-varying demand and tight capacity constraints for large-scale high-speed railway (HSR) networks. We construct the space-time travel network and formulate the assignment model by destination-based arc flow variables, then design a Dantzig-Wolfe (D-W) decomposition-based algorithm to solve the large-scale model of this problem with less memory requirement and computational complexity. The sub-problem in each iteration can be solved as a single-source (destination) minimum cost flow problem without capacity constraint by the "rooftops" method, which provides an effective technique to tackle both continuous and discrete time-varying demand in the assignment problem. It is further demonstrated that the assignment solutions for discrete time-varying demand can converge to that for continuous time-varying demand with the increase of demand discretization number. Three real case studies with different HSR network scales show that the proposed algorithm is efficient in memory and computational complexity, especially when highly accurate solutions for passenger assignment on large-scale HSR networks are needed.

Line planning is a critical issue in railway planning as a connecting link between supply and demand. This paper proposes an approach to optimize line planning problem (LPP) for the high-speed railway (HSR) network to provide higher service level for time-varying demand. A bi-level programming model based on Stackelberg game theory is constructed, incorporating passenger flow assignment into line planning to obtain a cost and customeroriented line plan which is extended by introducing the "estimated starting time" to obtain time information and evaluate the match degree between the line plan and travel demand. The model is solved under the framework of Simulated Annealing Algorithm (SAA) by a decomposition searching strategy combining the efficiency evaluation of in-line subplans and the whole network line plan to improve the stability of solutions. A case study on a partial HSR network in China is presented, and it demonstrates that the optimized line plan can not only fit the fluctuation of travel demand, but also increase the utilization ratio of the transit capacity.

PurposeThis paper aims to propose a medium-term forecast model for the daily passenger volume of High Speed Railway (HSR) systems to predict the daily the Origin-Destination (OD) daily volume for multiple consecutive days (e.g. 120 days).Design/methodology/approachBy analyzing the characteristics of the historical data on daily passenger volume of HSR systems, the date and holiday labels were designed with determined value ranges. In accordance to the autoregressive characteristics of the daily passenger volume of HSR, the Double Layer Parallel Wavelet Neural Network (DLP-WNN) model suitable for the medium-term (about 120 d) forecast of the daily passenger volume of HSR was established. The DLP-WNN model obtains the daily forecast result by weighed summation of the daily output values of the two subnets. Subnet 1 reflects the overall trend of daily passenger volumes in the recent period, and subnet 2 the daily fluctuation of the daily passenger volume to ensure the accuracy of medium-term forecast.FindingsAccording to the example application, in which the DLP-WNN model was used for the medium-term forecast of the daily passenger volumes for 120 days for typical O-D pairs at 4 different distances, the average absolute percentage error is 7%-12%, obviously lower than the results measured by the Back Propagation (BP) neural network, the ELM (extreme learning machine), the ELMAN neural network, the GRNN (generalized regression neural network) and the VMD-GA-BP. The DLP-WNN model was verified to be suitable for the medium-term forecast of the daily passenger volume of HSR.Originality/valueThis study proposed a Double Layer Parallel structure forecast model for medium-term daily passenger volume (about 120 days) of HSR systems by using the date and holiday labels and Wavelet Neural Network. The predict results are important input data for supporting the line planning, scheduling and other decisions in operation and management in HSR systems.

Pricing and seat allocation are complementary strategies of revenue management (RM) for railway industry, but these two strategies were typically considered as isolated problems in most of existing studies. To extend the literature, a nonlinear programming model is proposed for the joint optimization of pricing and seat allocation in high-speed rail (HSR) networks. Meanwhile, multistage and discriminatory pricing strategies are employed to attract more passengers and improve total revenue. Following the Davidon-Fletcher-Powell method, we design an efficient solution algorithm for large-scale joint HSR pricing and seat allocation problems. The sensitivity analysis is performed to capture the uncertain impacts of model inputs on the outputs. The advantages of joint RM strategy are demonstrated by a trade study where different RM strategies are compared. The results of a large-scale instance in the real HSR network show that the proposed model and solution algorithm can provide decision support for railway companies in their daily operation and management.

基于高速铁路高速度、高密度开行特征,将旅客对高铁出行时刻的需求描述为关于意愿出发时刻的连续时变需求,再按照等时间间隔,将其转化为离散时空需求;基于时刻表构建高铁出行时空网络,在不考虑列车拥挤的情况下,将离散客流高铁均衡分配问题等价于具有列车席位能力约束的多个O-D离散需求的最小费用流问题;构建形成基于时刻表的高铁客流分配线性规划模型,为处理模型巨量的决策变量和约束条件,设计丹齐格—沃尔夫分解算法,将模型分解成基于终点的无能力约束最小费用流问题;利用时空网络无圈特性,完成对大规模网络下高铁客流分配模型的求解,并通过海南环岛铁路东环线实例验证算法.结果 表明:对于所有2015年7月1日自三亚站出发的旅客,网络中不存在提前购票附加成本,网络能力较宽松;将网络客流量放大为原客流量的1.8倍,以琼海—海口间14:00出发的3种最优出行方案为例,得到3种方案出行费用均为184.60元,意味着网络达到用户均衡状态,证明模型与算法兼具有效性和高效性.

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.

针对高速铁路日常客运量预测问题,提出消除节假日因素影响的数据替补修正法和融合变分模态分解(VMD)、遗传算法(GA)和BP神经网络的日常客运量VMD-GA-BP预测方法.数据替补修正法是根据日常客运量超常波动判定阈值识别节假日延续期,采用VMD-GA-BP预测方法得到预测值,用该预测值替换节假日延续期内的客运量.VMD-GA-BP预测方法首先采用VMD对被替换数据之前的数据序列进行分解,得到不同频率的模态分量;其次通过GA优化初始权值和阈值的BP神经网络对各模态分量分别预测;然后重构各模态分量的预测值,用预测值替换节假日延续期内的客运量,得到修正数据序列,据此预测得到高速铁路日常客运量.实例应用表明,VMD-GA-BP的预测误差远低于BP,EMD-GA-BP,SVR,EMD-BP等方法,且基于修正数据序列的预测误差明显低于基于原始数据序列.可见,VMD-GA-BP预测方法精度较高.

不同需求日的出行需求在结构和总量上都存在差异,不仅需要提供与各需求日相匹配的运输能力,而且不同需求日的列车开行方案应有很大相似性,使高铁运输组织能平稳过渡.为权衡铁路运营成本和运输组织衔接,在考虑高铁运输组织平稳过渡前提下,谋求铁路运输成本和旅客出行费用最小,对不同需求日列车开行方案进行协同优化.由各需求日各OD对需求生成最大包络需求,以最大包络需求列车开行方案为备选列车集,产生各个需求日的列车开行方案.建立不同需求日开行方案协同优化双层规划模型,设计求解模型的遗传算法.算例分析表明,在公共列车集比例限制下,协同优化产生的开行方案具有较好评价指标,算法收敛性较好,体现了模型和算法的有效性.

This study investigates the circuity of China's high-speed-rail (HSR) network from 2014 to 2016 and analyzes the network performance. The concept of circuity has been redefined in terms of travel time so that various speed levels of HSR lines can be measured systematically. In this study, circuity is redefined as the ratio of actual travel time to ideal travel time. By using actual HSR trip records, the influence of passenger demand and the circuity of transfer trips have been examined. At the node level, we find that the circuity of principal stations has significantly decreased overall. For stations with lower circuity, transfer trips from/to them tend to be more circuitous. Although stations along the intercity rail lines show higher circuity, they contribute to regional coverage and connectivity. Finally, we find that circuity tends to increase with a decreasing passenger flow for OD pairs within a certain distance range, and the passenger flow may decline as the OD distance increases.

Passenger demand plays an important role in railway operation and organization, and this paper aims to estimate passenger time-varying demand by simulating the ticket-booking process for High Speed Rail (HSR) system. The ticket-booking process of each OD pair can be partition into discrete booking phases by the times when the tickets of any itinerary had sold out. The ticket booking volume of each itinerary is reversely assigned to its corresponding expected departure intervals to obtain the time-varying demand in each booking phase using the rooftop model, and the total time-varying demand are estimated by summing the time-varying demand distributions in all booking phases. Only with the data about the itinerary flow, the precedence relationship is introduced to constrain the ticket sold-out order of all itineraries for each OD pair. Based on the precedence relationships of itineraries, two typical situations are proposed, in which the Single Booking Phase Reverse Assignment (SBPRA) algorithm and the Multiple Booking Phases Reverse Assignment (MBPRA) algorithm are proposed to estimate the time-varying demand respectively. Case analysis on OD pair Beijing-Shanghai are presented, and the validity analysis demonstrates that the error rates of SBPRA algorithm and MBPRA algorithm are 8.64% and 6.37%, respectively.

To get the precise transfer demand, deeply analysis for the passenger data is needed. The algorithm based on transfer direction has low efficiency and takes longer time when dealing with big quantity data. This paper proposed an algorithm based on OD shortest distance. The experiment indicated that this algorithm can improve the performance hundreds times with relative error lower than 3%. All the transfer information for each train trip are preserved during the calculation, these data can be used to support deeply analysis for traveler's transfer demand characteristics.

在传统列车开行方案基础上引入列车始发时间,形成高速铁路列车开行方案的新概念.针对一些关键O-D对,提出服务列车数下限要求;针对车站需求稀疏时段,提出发车时间间隔上限要求.基于旅客出行的时变需求,建立面向旅客服务水平的高速铁路列车开行方案优化的双层规划模型,其中上层规划为铁路企业优化列车开行方案的决策;下层规划描述旅客的乘车选择行为,即列车网络上进行客流分配.为缩小优化搜索空间,列车运行区段仅限于备选集中产生,并将关键O-D对服务列车数约束转化为各车站的停站列车数约束.设计列车停站方案的确定方法、初始开行方案的生成方法和邻域解的搜索方法,并以此为核心设计求解模型的模拟退火算法.算例表明,采用该模型和算法求解的列车开行方案在时空区域上较好地满足了旅客出行时变需求,服务水平达到规定下限,模型和算法具有良好的优化效率和实用性.

An optimization model of train timetabling for high-speed rail network is built with the aim of minimizing the total travel time of trains considering time interval constraints of trains operating on both same and various rail lines. And then a linear programming model was constructed to optimize train arrival and departure times with a fixed order based on the extension of digraph of train timetable from rail line to rail network. The solving algorithm was designed combining moving operation times, exchanging operation order and changing stop plan based on the choice strategies of the first defused conflict and its handling technique. Finally, a numerical example was applied to analyze the efficiency of the proposed method and algorithm.

In order to evaluate whether the high-speed train diagram is in accordance with the time-dependent travel demand of passengers, the passenger flow can be assigned according to the time-dependent travel demand and the departure time deviation can be used to evaluate. However, if the distribution of the time-dependent travel demand is unknown, this evaluation method cannot be used. This paper studied the utilization of high-speed railway system operation data, to evaluate whether the high-speed train diagram is consistent with the time-dependent travel demand. By defining the smaller space-time units, the transport capacity, passenger flow and load factor in the space-time units, the train density, travel demand intensity and their relationship were described. Based on the numerical analysis of the Beijing-Guangzhou high-speed railway system operating data, it was found that the average transport capacity is lager in the space-time quadrant with higher demand in the reasonable train diagram, and the average load factor is higher in the space-time quadrant with larger transport capacity. Although the actual traffic volume data was used in the numerical analysis, the feature indicates that the train diagram corresponds to the travel demand. This feature can be used to evaluate the train diagram and apply to large-scale high-speed railway network.

高速铁路客流分配过程的本质是旅客购票过程,影响旅客购票过程的主要因素包括铁路售票策略和旅客购票时序.本文将铁路售票策略和旅客购票时序嵌入到客流分配过程中,设计了针对不同售票策略的换乘网络,根据不同OD行程的购票特征构造购票强度函数;利用若干最小费用换乘方案构建屋顶模型,将时变需求的连续出行时间离散化,获得每一个最小费用方案的离散出行客流量,设计了考虑售票策略的高速铁路客流分配方法.通过对京广深高速铁路客流分配实例分析表明,客流分配结果与旅客实际购票过程相吻合,运算时间短,证实了配流方法的有效性,具有解决大规模高速铁路网络客流分配能力,为列车运行图和售票策略提供评价手段.

This paper formulates and examines the passenger flow assignment (itinerary choice) problem in high-speed railway (HSR) systems with multiple-class users and multiple-class seats, given the train schedules and time-varying travel demand. In particular, we take into account advance booking cost of travelers in the itinerary choice problem. Rather than a direct approach to model advance booking cost with an explicit cost function, we consider advance booking cost endogenously, which is determined as a part of the passenger choice equilibrium. We show that this equilibrium problem can be formulated as a linear programming (LP) model based on a three-dimension network representation of time, space, and seat class. At the equilibrium solution, a set of Lagrange multipliers for the LP model are obtained, which are associated with the rigid in-train passenger capacity constraints (limited numbers of seats). We found that the sum of the Lagrange multipliers along a path in the three-dimension network reflects the advance booking cost of tickets (due to advance/early booking to guarantee availability) perceived by the passengers. Numerical examples are presented to demonstrate and illustrate the proposed model for the passenger assignment problem.