Transportation Research Part E-Logistics and Transportation Review最新文献_第7页

Operational modes and market structures selection in the on-demand ride-hailing platform considering matching-induced utility uncertainty 考虑匹配效用不确定性的网约车平台运营模式与市场结构选择

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2026-01-20 DOI: 10.1016/j.tre.2026.104682

Feng Lin , Ran Xu , Xianglong Lin , Jizhou Lu

In on-demand ride-hailing platforms, participants face matching-induced utility uncertainty, a distinct phenomenon where participants cannot predict whether they will be successfully matched with counterparts. This uncertainty creates gaps between participants’ expected and realized utility, thus significantly influencing how platforms should optimize their pricing strategies to shape participants’ choices and market outcomes. To address this challenge, this paper constructs a theoretical framework of an on-demand platform ecosystem consisting of a ride-hailing platform (RP) and its upstream contract manufacturer (CM). We investigate how both parties strategically make pricing decisions to influence market structure formation and determine the optimal operational mode selection between business-to-customer (B2C) and peer-to-peer (P2P). Our comprehensive analysis reveals several key insights: 1) Regarding market structure formation, high service investment consistently leads to balanced structures across various commission rates in both operational modes. However, as service investment declines, the two modes diverge significantly: P2P mode invariably tends towards buyer markets regardless of commission rates, while B2C mode results in buyer markets only when commission rates exceed certain thresholds 2) When the platform supply chain achieves market balance, the CM exhibits a quality-profit inflection phenomenon, benefiting from increased service investment only when it surpasses a critical threshold. Below this threshold, profits paradoxically decrease as service investment improves due to intensified pricing competition at lower quality levels. 3) The RP selects the P2P mode exclusively when both service investment and commission rates are simultaneously high, otherwise strategically shifting to the B2C mode to access multiple revenue streams and reduce dependence on commission income alone. 4) The analysis extends to incorporate participant risk preferences and endogenous service investment decisions, which not only validates the core findings but also reveals how behavioral factors and strategic quality investments interact to shape market structures.

在按需叫车平台中，参与者面临着匹配引发的效用不确定性，这是一种独特的现象，参与者无法预测他们是否会成功地与对手匹配。这种不确定性造成了参与者预期效用和实际效用之间的差距，从而显著影响了平台应该如何优化其定价策略，从而塑造参与者的选择和市场结果。为了解决这一挑战，本文构建了一个由网约车平台（RP）及其上游合同制造商（CM）组成的按需平台生态系统的理论框架。我们研究了双方如何战略性地做出定价决策来影响市场结构的形成，并确定了B2C和P2P之间的最佳运营模式选择。我们的综合分析揭示了几个关键的见解：1)在市场结构形成方面，高服务投资始终导致两种运营模式下不同佣金率的结构平衡。然而，随着服务投资的下降，两种模式出现了明显的分歧：P2P模式无论佣金率如何都必然倾向于买方市场，而B2C模式只有在佣金率超过一定阈值时才会出现买方市场。2)当平台供应链达到市场平衡时，CM表现出质量-利润的拐点现象，只有当服务投资超过一个临界阈值时，CM才会受益于服务投资的增加。低于这个门槛，利润反而会随着服务投资的增加而减少，这是由于在较低质量水平上加剧了价格竞争。3)当服务投资和佣金率同时较高时，RP只选择P2P模式，否则战略性地转向B2C模式，以获取多种收入流，减少对佣金收入的依赖。4)将参与者风险偏好和内生服务投资决策纳入分析，不仅验证了核心结论，而且揭示了行为因素和战略质量投资如何相互作用影响市场结构。

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引用次数: 0

Balancing service quality and riders’ welfare in on-demand delivery order dispatching: an integrated rolling horizon and differentiated incentive approach 按需配送订单调度中服务质量与乘客福利的平衡：综合滚动地平线与差异化激励方法

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2026-01-19 DOI: 10.1016/j.tre.2026.104685

Fuyu Cao , Yichen Qin , Hongtao Hu , Xuting Sun , Kam K.H. Ng

As on-demand food delivery plays an important role in daily service, it also poses significant challenges in developing real-time optimization solutions. Overwhelming customer orders require real-time rider dispatch and route scheduling dynamically, while meal preparation delays disrupt the scheduled routing plan of riders. The resulting delivery lateness incurs penalties to riders. To balance delivery service quality and riders’ welfare, this paper proposes an order exchange mechanism which enables riders to return the delayed orders back to the platform. The platform then identifies potential riders willing to undertake the returned orders with differentiated incentives. Four mixed integer linear programming (MILP) models are formulated to capture the sequential interactive decisions of the platform and riders. A dynamic order returning and dispatching method is designed and embedded with the rolling horizon approach, managing both the returned orders and the newly arriving orders adaptively. Meanwhile, aimed at balancing riders’ individual welfare, we introduce an equity metric and establish a bi-objective order dispatching model, subsequently employing the ε-constraint approach with a linearization technique. A tailored Artificial Bee Colony (ABC) algorithm solves instances based on a new town in Shanghai Megacity, China. Numerical experiments demonstrate that the order exchange mechanism enhances scheduling flexibility and ensures welfare equity. Interestingly, incorporating equity considerations reveals that generous pricing strategies paradoxically improve the overall system efficiency and ultimately reduce the total operational costs. Policy recommendations based on these findings are provided for on-demand delivery service platforms to strategically manage their riders and delivery orders.

随需外卖在日常服务中扮演着重要的角色，同时也给实时优化解决方案的开发带来了巨大的挑战。大量的客户订单需要实时的骑手调度和动态的路线调度，而饭菜准备的延迟打乱了骑手预定的路线计划。由此导致的送货延误会对乘客造成处罚。为了平衡配送服务质量和乘客福利，本文提出了一种订单交换机制，使乘客能够将延迟的订单返回到平台。然后，该平台通过差异化的激励措施，识别愿意接受退回订单的潜在骑手。建立了四个混合整数线性规划（MILP）模型，以捕获平台和乘客的顺序交互决策。设计了一种动态订单返回与调度方法，并嵌入滚动地平线方法，对返回的订单和新到达的订单进行自适应管理。同时，以平衡乘客个人福利为目标，引入公平度量，建立双目标订单调度模型，并采用线性化的ε约束方法。一种量身定制的人工蜂群（ABC）算法解决了基于中国上海特大城市新城镇的实例。数值实验表明，订单交换机制提高了调度灵活性，保证了福利公平性。有趣的是，考虑到公平因素，慷慨的定价策略反而提高了整个系统的效率，并最终降低了总运营成本。根据这些发现，为按需配送服务平台提供了政策建议，以战略性地管理其骑手和配送订单。

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引用次数: 0

Pareto optimal regulatory strategies for coupled ridesourcing and taxi markets with impatient passengers 有不耐烦乘客的拼车和出租车市场的帕累托最优监管策略

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2026-01-19 DOI: 10.1016/j.tre.2026.104677

Xiaohan Zhou , Shaopeng Zhong , Hai Yang , Yunhai Gong , Xiantao Xiao , Yu Jiang

This study develops a multi-objective bi-level programming model to identify the Pareto optimal combined regulatory strategy that simultaneously accounts for passengers, taxi drivers, ridesourcing vehicle (RSV) drivers, and the transportation network company (TNC). The upper level determines four regulatory controls, including the RSV fleet cap, taxi fare rate, government-guided RSV fare rate, and TNC wage rate floor, while the lower level obtains the steady-state market performance, which is formulated as a fixed-point problem and approximated through iterative agent-based simulations. To solve the model, a multi-objective Bayesian optimization algorithm is developed. Based on the DiDi dataset collected from Hangzhou City in 2018, our experiments demonstrate that no regulatory strategy can simultaneously benefit all stakeholders. If the government considers maximizing vehicle utilization as a secondary criterion, then it should decrease the RSV fleet cap, impose higher fare rates, and allow the TNC to pay lower wages, compared with the benchmark scenario. Furthermore, it is recommended that the government should avoid regulations that primarily favor passengers or the TNC, as our results reveal that such policies could harm other stakeholders and reduce vehicle utilization by up to 11.6%. Finally, if passengers’ impatience is overlooked, taxi drivers may lose 23.3% of potential profits.

本研究建立了一个多目标双层规划模型，以确定同时考虑乘客、出租车司机、拼车司机和运输网络公司的帕累托最优组合监管策略。上层确定四种监管控制，包括RSV车队上限、出租车收费标准、政府引导的RSV收费标准和跨国公司最低工资标准，下层获得稳态市场绩效，将其表述为一个不动点问题，并通过基于迭代智能体的模拟进行近似。为了求解该模型，提出了一种多目标贝叶斯优化算法。基于2018年杭州市的滴滴数据集，我们的实验表明，没有一种监管策略可以同时使所有利益相关者受益。如果政府考虑将车辆利用率最大化作为次要标准，那么它应该降低RSV车队上限，征收更高的票价，并允许TNC支付较低的工资，与基准情景相比。此外，建议政府应避免主要有利于乘客或跨国公司的法规，因为我们的研究结果表明，此类政策可能会损害其他利益相关者，并减少高达11.6%的车辆利用率。最后，如果忽视乘客的不耐烦，出租车司机可能会损失23.3%的潜在利润。

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引用次数: 0

Optimizing hub networks for truck platooning under uncertainty in cost savings 在成本节约不确定的情况下，优化卡车队列的枢纽网络

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2026-01-19 DOI: 10.1016/j.tre.2026.104692

Elif Zeynep Serper , Sibel A. Alumur

Platooning offers significant potential cost benefits for truckload transportation by utilizing vehicle-to-vehicle communication and automation through the formation and dissolution of platoons at hubs. This paper addresses optimization of platooning hub networks for transporting truckloads of commodities from their origins to destinations within the promised delivery times. Deterministic and stochastic optimization models are developed to design these networks with a minimum total cost, where each truckload of a commodity can be transported either directly along its shortest path from origin to destination or routed via platooning through hubs. The stochastic model incorporates uncertainty associated with the potential cost savings due to platooning. The Sample Average Approximation method is employed to solve the stochastic model. Using real-world data involving 1253 commodities across 39 U.S. cities, the computational analysis demonstrates significant cost savings and delivery performance improvements through platooning. On average, even under the highest hub operating costs, the proposed model achieves a 7.97% reduction in overall costs compared to the direct-shipment-only scenario, with the best-case improvement reaching 15.89%. Additionally, the platoon-enabled network significantly improves delivery performance, increasing the share of shipments delivered within one day by up to 42% compared to the direct-shipment-only case. Furthermore, the results demonstrate the stochastic model’s ability to adapt to cost uncertainties, making it a valuable tool for changing logistics environments.

通过在枢纽形成和解散队列，利用车对车的通信和自动化，队列为卡车运输提供了巨大的潜在成本效益。本文讨论了在承诺的交货时间内将货物从原产地运输到目的地的队列枢纽网络的优化问题。开发了确定性和随机优化模型，以最小的总成本设计这些网络，其中每辆卡车的商品可以直接沿着从起点到目的地的最短路径运输，也可以通过集线器进行队列运输。随机模型包含了不确定性与潜在的成本节约有关。采用样本平均逼近法求解随机模型。使用涉及39个 美国的1253种商品的真实数据在城市中，计算分析表明，通过队列化可以显著节省成本并提高交付性能。平均而言，即使在最高的枢纽运营成本下，与仅直接发货的情况相比，所提出的模型也能实现7.97%的总成本降低，最佳情况下的改进达到15.89%。此外，该网络显著提高了交付性能，与直接交付相比，一天内交付的货物份额提高了42%。此外，结果表明，随机模型的能力，以适应成本的不确定性，使其成为一个有价值的工具，为不断变化的物流环境。

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引用次数: 0

Freeports under the lens: securing container supply chains with a risk-based inspection framework 镜头下的自由港：用基于风险的检查框架保护集装箱供应链

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2026-01-19 DOI: 10.1016/j.tre.2025.104658

Xinrui Liang , Shiqi Fan , Huanhuan Li , Floris Goerlandt , Zaili Yang

Freeports that attract illegal activities may become vulnerable without effective measures to ensure a safe and secure Maritime Container Supply Chain (MCSC) involving them. Developing a holistic risk-based inspection framework is essential to address illicit trade challenges in freeports without the need to significantly compromise operational efficiency in a globalized trading environment. However, the existing literature is limited in addressing the uncertainty in container risk levels for establishing inspection priorities and in considering the unique features of freeports. To close these gaps, this study develops a novel framework based on a hybrid two-stage decision-making approach for the optimal container inspection solution, focusing on high-risk containers within the context of Freeport-Centric Supply Chains. The first stage employs a hybrid Decision-Making Trial and Evaluation Laboratory and Bayesian Network (DEMATEL-BN) model to assess container risk levels, accounting for interdependent vulnerabilities across MCSC nodes. The findings identify the most critical risk nodes within the MCSC, including consolidation centers and loading ports. Furthermore, it measures the risk levels of containers based on both severity and likelihood metrics, which serve as key inputs for determining inspection rates in the second stage, distinguishing the approach by effectively addressing uncertainties often overlooked in existing container inspection frameworks. This approach makes new contributions to enhancing the security of freeports and various MCSCs by enabling targeted risk mitigation, optimizing inspection strategies, and balancing security with trade flow efficiency.

吸引非法活动的自由港如果没有有效的措施来确保安全可靠的海上集装箱供应链（MCSC），可能会变得脆弱。制定一个基于风险的整体检查框架对于解决自由港的非法贸易挑战至关重要，同时又不需要在全球化的贸易环境中大幅降低运营效率。然而，现有文献在解决集装箱风险水平的不确定性以确定检查优先级和考虑自由港的独特特征方面是有限的。为了缩小这些差距，本研究开发了一个基于混合两阶段决策方法的新框架，用于最佳集装箱检验解决方案，重点关注以自由港为中心的供应链背景下的高风险集装箱。第一阶段采用混合决策试验和评估实验室和贝叶斯网络（DEMATEL-BN）模型来评估容器风险水平，考虑MCSC节点之间的相互依赖漏洞。研究结果确定了MCSC内最关键的风险节点，包括整合中心和装载港口。此外，它根据严重性和可能性度量来度量容器的风险水平，这是确定第二阶段检查率的关键输入，通过有效地解决现有容器检查框架中经常被忽视的不确定性来区分方法。该方法通过有针对性地降低风险、优化检查策略、平衡安全和贸易流效率，为加强自由港和各种MCSCs的安全作出了新的贡献。

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引用次数: 0

An event-based model and hybrid genetic search algorithm for an inland multi-size container transportation problem 内陆多尺寸集装箱运输问题的事件模型和混合遗传搜索算法

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2026-01-17 DOI: 10.1016/j.tre.2025.104639

Meiyan Chi , Xiaoning Zhu , Baicheng Yan , Kris Braekers

This paper addresses a complex multi-size Container Drayage Problem (CDP) in the hinterland of a seaport, where a fleet of identical trucks is used to transport containers between customer locations, a container terminal, and a depot, and in which the repositioning of empty containers is also considered. Each truck can carry either one 40-ft container or two 20-ft containers simultaneously. The main target of the CDP is to determine the trucking schedule that satisfies all transport demands while minimizing the total cost. The problem is described using an event-based graph that considers capacity, pairing, precedence, and time-window constraints implicitly, based on which a compact Mixed-Integer Linear Programming (MILP) model is proposed. To reduce the model scale and enhance computational efficiency, we introduce tailored model enhancement methods to eliminate infeasible event nodes and arcs based on time window feasibility checks. The results of numerical experiments prove that the event-based model can solve small-scale instances effectively. For large-scale instances, we develop a Hybrid Genetic Search (HGS) algorithm that incorporates a Dynamic Programming (DP)-optimized enumeration method to handle multi-size container loading schemes and time-window constraints effeciently. Extensive computational experiments show that our proposed algorithm significantly outperforms the commercial solver CPLEX on large-scale instances, demonstrating its scalability for real-world applications.

本文解决了海港腹地复杂的多尺寸集装箱拖运问题（CDP），其中使用相同的卡车车队在客户位置，集装箱码头和仓库之间运输集装箱，并且还考虑了空集装箱的重新定位。每辆卡车可以同时装载一个40英尺的集装箱或两个20英尺的集装箱。CDP的主要目标是确定满足所有运输需求的卡车运输时间表，同时将总成本降至最低。利用隐式考虑容量、配对、优先级和时间窗约束的事件图来描述问题，在此基础上提出了紧凑的混合整数线性规划（MILP）模型。为了减小模型规模，提高计算效率，我们引入了基于时间窗可行性检查的模型增强方法，以消除不可行的事件节点和弧线。数值实验结果表明，基于事件的模型可以有效地求解小尺度实例。对于大规模实例，我们开发了一种混合遗传搜索（HGS）算法，该算法结合了动态规划（DP）优化的枚举方法来有效地处理多尺寸集装箱装载方案和时间窗约束。大量的计算实验表明，我们提出的算法在大规模实例上显著优于商业求解器CPLEX，证明了其在实际应用中的可扩展性。

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引用次数: 0

A multi-channel retail store location model considering customer retry purchasing patterns 考虑顾客重试购买模式的多渠道零售商店选址模型

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2026-01-17 DOI: 10.1016/j.tre.2026.104674

Lifen Yun , Runfeng Yu , Hongqiang Fan , Yuanjie Tang , Xun Weng

The rapid growth of e-commerce has driven retailers to establish multiple retail channels to enhance service quality for their customers. Despite retailers’ efforts to serve customers, services may not always be available due to various reasons. In such cases, customers often retry their purchases through their preferred channel. This behavior, along with the complexities of multi-channel retailing, complicates both the structure and costs of last-mile network design. To optimize store locations and costs, this paper proposes a mixed-integer programming (MIP) model for tactical store location planning, considering customer retry purchasing patterns and three channels: ship from store (SFS), buy online and pick up in store (BOPS), and offline shopping (OS). Given that the problem is NP-hard in the strong sense, we develop an iterative two-phase Lagrangian relaxation and granular tabu search heuristic (LR-GTS) to tackle large-scale instances. In each iteration, the LR operator decomposes the model and produces high-quality location schemes, while the GTS operator improves the vehicle routing in the SFS channel. Numerical results demonstrate that our heuristic exhibits strong performance in solving large-scale problems involving 600 customers. Additionally, we apply our model to real-world cases, offering valuable managerial insights derived from the sensitivity analysis results.

电子商务的快速发展促使零售商建立多种零售渠道，以提高对客户的服务质量。尽管零售商努力为顾客服务，但由于各种原因，服务可能并不总是可用的。在这种情况下，顾客通常会通过他们喜欢的渠道重新购买。这种行为，加上多渠道零售的复杂性，使最后一英里网络设计的结构和成本都变得复杂。为了优化商店位置和成本，本文提出了一种混合整数规划（MIP）模型，用于战术商店位置规划，考虑了客户重试购买模式和三个渠道：从商店发货（SFS），在线购买和在商店取货（BOPS）和离线购物（OS）。考虑到该问题在强意义上是np困难的，我们开发了一种迭代的两阶段拉格朗日松弛和颗粒禁忌搜索启发式（LR-GTS）来处理大规模实例。在每次迭代中，LR算子分解模型并生成高质量的定位方案，而GTS算子则改进SFS通道中的车辆路线。数值结果表明，我们的启发式算法在解决涉及600个客户的大规模问题时表现出很强的性能。此外，我们将我们的模型应用于实际案例，从敏感性分析结果中提供有价值的管理见解。

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引用次数: 0

Platelet inventory management in hospital networks: A reinforcement learning approach 医院网络血小板库存管理：一种强化学习方法

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2026-01-17 DOI: 10.1016/j.tre.2025.104629

Shahrzad Valizadeh , Babak Abbasi , Su Nguyen , Zahra Hosseinifard

This study proposes a reinforcement learning (RL)-based framework incorporating the Proximal Policy Optimization (PPO) algorithm to improve platelet inventory management. The proposed approach considers an inventory system with varying ordering intervals, incorporating ABO-Rh substitution decisions and hospital collaborations through transshipment. In this framework, transshipment is modeled as a fixed policy, reflecting real-world practices where blood units nearing expiration are proactively transferred from smaller local hospitals to larger hospitals, where they are more likely to be used in time. We extend our analysis by exploring several RL models, including Trust Region Policy Optimization (TRPO) and Soft Actor-Critic (SAC). The results show that PPO-Complete outperforms the other RL models, and all considered RL approaches outperform the base-stock strategy, which is commonly used in hospital platelet inventory management. The analyses indicate that lower transshipment costs, when coupled with effective substitution decisions, lead to a reduction in total cost and enable larger order sizes, thereby mitigating shortages.

本研究提出了一种基于强化学习（RL）的框架，结合近端策略优化（PPO）算法来改善血小板库存管理。提出的方法考虑了具有不同订货间隔的库存系统，包括ABO-Rh替代决策和通过转运的医院合作。在这个框架中，转运被建模为一种固定的政策，反映了现实世界的做法，即即将到期的血液单位主动从较小的当地医院转移到更有可能及时使用的大医院。我们通过探索几个RL模型来扩展我们的分析，包括信任域策略优化（TRPO）和软行为者批评家（SAC）。结果表明，PPO-Complete模型优于其他RL模型，并且所有RL方法都优于医院血小板库存管理中常用的基础库存策略。分析表明，较低的转运成本，加上有效的替代决策，导致总成本的降低，并使更大的订单规模，从而缓解短缺。

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引用次数: 0

How does supply chain finance impact supply chain resilience − based on three levels of supply and demand decision making 供应链金融如何影响供应链弹性-基于三个层次的供需决策

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2026-01-17 DOI: 10.1016/j.tre.2026.104686

Limeng Chai , Longyu Zong , Kee-hung Lai

Enhancing supply chain resilience has emerged as a key focus for industries worldwide. Based on the theory of resource orchestration, this paper uses annual reports of Chinese companies to analyze how supply chain finance improves supply chain resilience in three dimensions, including matching supply and demand, fostering supply–demand relationships, and ensuring supply quality. The results indicate that supply chain finance contributes positively to improving resilience across these dimensions. Furthermore, the moderating analysis examines the positive moderating effects of artificial intelligence, merchant guild culture and the level of vertical integration on supply chain finance and supply chain resilience. Finally, this paper also explores the threshold effect of company risk-taking and the heterogeneity of industry characteristics. The results show that company risk-taking level has a certain threshold impact, and the supply chain finance in the manufacturing industry has a more significant effect on enhancing supply chain resilience, which emphasizes that companies should attach importance to aspects such as technology, risk management and cultural maintenance, when they carry out supply chain finance business. This research provides a new perspective for optimizing supply chain resilience.

增强供应链弹性已成为全球各行业关注的焦点。本文以资源编排理论为基础，以中国企业年报为样本，从供需匹配、供需关系培育、供给质量保障三个维度分析供应链金融如何提高供应链弹性。结果表明，供应链金融对提高这些维度的弹性有积极的贡献。此外，调节分析还考察了人工智能、商会文化和垂直整合水平对供应链金融和供应链弹性的正向调节作用。最后，本文还探讨了公司风险承担的门槛效应和行业特征的异质性。研究结果表明，企业风险承担水平具有一定的阈值影响，制造业供应链金融对提升供应链弹性的作用更为显著，这强调了企业在开展供应链金融业务时应重视技术、风险管理和文化维护等方面。本研究为供应链弹性优化提供了一个新的视角。

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引用次数: 0

Two-stage virtual coupling high-speed railway train operation control method considering train operation curve optimization 考虑列车运行曲线优化的两段虚拟耦合高速铁路列车运行控制方法

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2026-01-16 DOI: 10.1016/j.tre.2026.104687

Wencheng Huang , Baiquan Tai

In order to maintain the train tracking accuracy of the virtual coupled train formation (VCTF) while reducing energy consumption during operation, in this paper, we propose a two-stage virtual coupling high-speed train operation control method that considers optimizing the train operation curve. Based on train dynamics, we take minimizing energy consumption and operation time deviation as the main objectives for the train operation curve optimization in Stage I, establish a dynamic programming model with time discretization, and design an algorithm to satisfy the requirements of precise parking and on-time operation of the train, in which a variable time step solving algorithm is considered to improve the solving efficiency. The optimal energy-saving train operation curve obtained in Stage I is used as the expected operation curve of the leader train in the VCTF. In Stage II, we adopt the Distributed Model Predictive Control (DMPC) to establish the virtual coupling high-speed railway train operation control model. With the goal of minimizing the position error and speed error of the VCTF, and the train safety interval, train speed, train control performance and passenger comfort constraints, the designed DMPC ensures the stability and safety of the operation of VCTF. Finally, simulation analysis is conducted on three different scenarios, the research results show that the methodology proposed in this paper can effectively reduce the energy consumption of trains during operation while improving the tracking accuracy of trains in VCTF. In terms of energy saving, the proposed method is superior to previous heuristic algorithms and reinforcement learning methods, with improvements of 16.12 % and 2.52 %, respectively. In terms of tracking accuracy, the peak distance and speed errors of the VC trains do not exceed 2 m and 0.24 m/s. In terms of solving efficiency, the variable time step dynamic programming method can improve the solving speed by nearly 30 % while ensuring the solving accuracy. Moreover, the energy consumption of Train 0 and Train 1 increased by 1.7 % and 0.66 % respectively compared to the expected operation curve. Simulation results with different expected intervals show that setting an appropriate expected tracking interval in actual operation requires a balance between train operation safety and energy efficiency.

为了保持虚拟耦合列车编队（VCTF）的列车跟踪精度，同时降低运行过程中的能耗，本文提出了一种考虑优化列车运行曲线的两阶段虚拟耦合高速列车运行控制方法。基于列车动力学，以最小化能耗和运行时间偏差为第一阶段列车运行曲线优化的主要目标，建立了时间离散化的动态规划模型，设计了满足列车精确停车和准点运行要求的算法，其中考虑了变时间步长求解算法，提高了求解效率。将第一阶段得到的最优列车节能运行曲线作为VCTF中领头列车的期望运行曲线。第二阶段，采用分布式模型预测控制（DMPC）建立虚拟耦合高速铁路列车运行控制模型。设计的DMPC以VCTF的位置误差和速度误差最小为目标，同时考虑列车安全间隔、列车速度、列车控制性能和乘客舒适度约束，保证VCTF运行的稳定性和安全性。最后，对三种不同的场景进行了仿真分析，研究结果表明，本文提出的方法可以有效降低列车运行时的能耗，同时提高列车在VCTF中的跟踪精度。在节能方面，该方法优于以往的启发式算法和强化学习方法，分别提高了16.12%和2.52%。在跟踪精度方面，VC列车的峰值距离误差不超过2 m，速度误差不超过0.24 m/s。在求解效率方面，变时间步长动态规划法在保证求解精度的同时，求解速度提高了近30%。与预期运行曲线相比，0号列车和1号列车的能耗分别提高了1.7%和0.66%。不同期望区间的仿真结果表明，在实际运行中设置合适的期望跟踪区间需要在列车运行安全和节能之间取得平衡。

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