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Trajectory optimization for heavy-haul trains considering cyclic braking under complex operating conditions 复杂运行条件下考虑循环制动的重载列车轨迹优化
IF 2.6 Q3 TRANSPORTATION Pub Date : 2024-07-25 DOI: 10.1016/j.jrtpm.2024.100462
Min Zhou , Yuesong Liu , Hongwei Wang , Hairong Dong

Heavy-haul railways (HHRs) pose significant challenges due to their substantial traction weight, extended train length, and complex operational environments. Heavy-haul trains (HHTs), equipped with traditional pneumatic control braking systems, must adopt cycle braking strategies on long downhill slopes. The varying traction masses of HHTs on these railways lead to diverse maneuvering characteristics, presenting challenges for drivers and dispatchers in unforeseen circumstances. To enhance transportation efficiency and mitigate operational complexities, a trajectory optimization method is formulated for determining the optimal trajectory of HHTs with different traction masses under complex conditions, including long downhill slopes, temporary speed limit sections, and regular sections. It considers the dynamics of train traction, braking, and coasting at each phase, optimizing objectives such as train operation efficiency, energy consumption, and pneumatic braking times. A linear weight search algorithm ensures punctuality, and the model is linearized into a mixed-integer linear programming (MILP) form using segmented and stepwise functions to align with operational realities. Simulation experiments utilizing real data and various HHT configurations validate the efficacy of the proposed approach against alternative methods. This method offers precise trajectory optimization under complex conditions, providing valuable guidance for dispatchers and drivers in the heavy-haul railway sector.

重载铁路(HHR)因其牵引重量大、列车长度长和运行环境复杂而面临巨大挑战。配备传统气动控制制动系统的重载列车(HHT)必须在长下坡时采用循环制动策略。在这些铁路上,重载列车的牵引质量各不相同,导致操纵特性也各不相同,在不可预见的情况下给驾驶员和调度员带来了挑战。为了提高运输效率并降低运营复杂性,本文提出了一种轨迹优化方法,用于确定不同牵引质量的高速列车在长下坡、临时限速路段和常规路段等复杂条件下的最优轨迹。它考虑了列车在每个阶段的牵引、制动和滑行动态,优化了列车运行效率、能耗和气动制动时间等目标。线性权重搜索算法可确保正点率,模型线性化为混合整数线性规划(MILP)形式,使用分段函数和逐步函数,以符合运行实际情况。利用真实数据和各种 HHT 配置进行的模拟实验验证了所提方法与其他方法相比的有效性。该方法可在复杂条件下提供精确的轨迹优化,为铁路重载运输部门的调度员和司机提供有价值的指导。
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引用次数: 0
Comprehensive examination of regional railway passenger behavior and dwell time components: Insights from video-based observations in Victoria, Australia 全面考察地区铁路乘客行为和停留时间构成:澳大利亚维多利亚州视频观察的启示
IF 2.6 Q3 TRANSPORTATION Pub Date : 2024-07-11 DOI: 10.1016/j.jrtpm.2024.100464
Kenneth Ng, Nirajan Shiwakoti, Peter Stansinopoulos

In understudied regional railway operations, this study explores passenger boarding and alighting patterns and how station design impacts them, particularly dwell times. Despite extensive metropolitan and suburban train research, regional railways have been overlooked. This study investigates regional rail passenger flow and dwell time to bridge this gap. This article studies dwell times and passenger boarding and alighting at two regional stations in Victoria, Australia, using CCTV data. The objective is to identify insights that might improve regional railway services' efficiency and user experience and advocate for sector-specific solutions. Analysis indicates distinct station boarding and alighting features, highlighting the discovery of the ‘blinded phenomenon’ for train conductors particularly in the afternoon peak (PMP). The results of the study showed that PMP services, which prioritise alighting passengers, had higher dwell times than the morning peak (AMP) services, which emphasise boarding passengers. Obstructed views make it difficult for train conductors to monitor passenger alighting, prolonging dwell times. Better human resource strategies, artificial intelligence for crowd surveillance, and strategic CCTV system deployment to streamline operations and improve passenger experience on regional railways are proposed in the paper, laying the groundwork for future research and operational changes in this vital transportation sector.

在研究不足的地区铁路运营中,本研究探讨了乘客上下车模式以及车站设计如何影响乘客上下车,尤其是停留时间。尽管对大都市和郊区列车进行了广泛研究,但区域铁路一直被忽视。本研究调查了区域铁路的客流和停留时间,以弥补这一空白。本文利用闭路电视数据研究了澳大利亚维多利亚州两个地区车站的停留时间和乘客上下车情况。目的是找出可提高地区铁路服务效率和用户体验的见解,并倡导针对具体行业的解决方案。分析表明了车站上下车的显著特点,重点发现了列车长的 "失明现象",尤其是在下午高峰时段(PMP)。研究结果表明,优先考虑下车乘客的 PMP 服务的停留时间高于优先考虑上车乘客的早高峰 (AMP) 服务。由于视线受阻,列车长难以监控乘客下车情况,从而延长了停留时间。本文提出了更好的人力资源战略、用于人群监控的人工智能以及战略性闭路电视系统部署,以简化区域铁路的运营并改善乘客体验,为这一重要交通部门的未来研究和运营变革奠定了基础。
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引用次数: 0
Modeling of impact of operations and maintenance on safety, availability, capacity, and cost of Railways-A System dynamics approach 运营和维护对铁路安全、可用性、运力和成本影响的建模--系统动力学方法
IF 2.6 Q3 TRANSPORTATION Pub Date : 2024-07-08 DOI: 10.1016/j.jrtpm.2024.100463
Katleho R.M. Mafokosi , Jan-Harm C. Pretorius , Gopinath Chattopadhyay

the transport infrastructure, particularly the railway infrastructure plays a vital role in the delivery of freight and the transportation of people. The ability and reliability of the railway infrastructure to deliver goods and transport people are challenged by train derailments and collisions caused by infrastructure breakdowns. Lack of maintenance has been identified as one of the causes of infrastructure breakdowns leading to accidents. The current paper proposes that if the railway infrastructure safety, availability, capacity, and cost are modeled using system dynamics, the impact of infrastructure operation and maintenance on safety can be predicted more accurately. The paper follows systems thinking approach that aims to understand the railway infrastructure as a system, by defining the system structure, system component relationships, and system behavior. The impact on railway infrastructure is modeled using system dynamics by developing causal loop diagrams and stock and flow diagrams which define the system structure, and system component relationships, and models the system behavior of safety, availability, capacity, and cost.

运输基础设施,特别是铁路基础设施在货物运输和人员运输方面发挥着至关重要的作用。铁路基础设施运送货物和运送人员的能力和可靠性受到基础设施故障造成的列车脱轨和碰撞事故的挑战。缺乏维护被认为是基础设施故障导致事故的原因之一。本文提出,如果利用系统动力学对铁路基础设施的安全性、可用性、容量和成本进行建模,就能更准确地预测基础设施运营和维护对安全的影响。本文采用系统思维方法,旨在通过定义系统结构、系统组件关系和系统行为,将铁路基础设施理解为一个系统。通过绘制因果循环图、存量图和流量图来定义系统结构和系统组件关系,并对安全性、可用性、容量和成本等系统行为进行建模,从而利用系统动力学来模拟对铁路基础设施的影响。
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引用次数: 0
A mathematical model for a two-service skip-stop policy with demand-dependent dwell times 根据需求确定停留时间的双服务跳站政策数学模型
IF 2.6 Q3 TRANSPORTATION Pub Date : 2024-07-05 DOI: 10.1016/j.jrtpm.2024.100461
Rodolphe Farrando , Nadir Farhi , Zoi Christoforou , Alain Urban

This paper presents a discrete-event model for a mass-transit line operated with a two-service skip-stop policy while allowing for train dwell times to vary according to passengers’ demand volumes. The model is formulated by two mathematical constraints on the train’s travel and safe separation times that govern the train dynamics on the line. In addition, the model takes into account trains’ dwell times, which are affected by both the services offered by the operator and passenger demand. The model is written in the max-plus algebra, a mathematical framework that allows us to derive interesting analytical results, including the fundamental diagram of the line, which describes the relationship between the average train time headway (or frequency), the number of trains running on the line and the passenger travel demand. The paper also derives indicators that are capable of quantifying and, thus, assessing the impact of a skip-stop policy on passengers’ travel. Finally, the paper compares two different passenger demand profiles. Results show that long-distance passengers mainly benefit from skip-stop policies, while short-distance travelers may experience an increase in their travel time. For long-distance passengers, the increase in the waiting time is counterbalanced by the decrease in the in-vehicle time, leading to an overall decrease in total passenger travel time.

本文提出了一个离散事件模型,该模型适用于一条采用双班次跳站政策运营的公共交通线路,同时允许列车停留时间随乘客需求量的变化而变化。该模型由列车行驶时间和安全分离时间两个数学约束条件组成,这两个约束条件控制着线路上的列车动态。此外,该模型还考虑了列车的停留时间,而列车的停留时间受运营商提供的服务和乘客需求的影响。该模型是用 max-plus 代数编写的,这一数学框架使我们能够得出有趣的分析结果,包括线路的基本图,它描述了列车平均间隔时间(或频率)、线路上运行的列车数量和乘客出行需求之间的关系。本文还得出了一些指标,这些指标能够量化跳站政策对乘客出行的影响,从而对其进行评估。最后,本文比较了两种不同的乘客需求状况。结果显示,长途乘客主要受益于跳站政策,而短途乘客的旅行时间可能会增加。对于长途乘客来说,等待时间的增加被车内时间的减少所抵消,从而导致乘客总旅行时间的总体减少。
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引用次数: 0
Formulation of train routing selection problem for different real-time traffic management objectives 针对不同实时交通管理目标的列车路由选择问题表述
IF 3.7 Q3 TRANSPORTATION Pub Date : 2024-06-14 DOI: 10.1016/j.jrtpm.2024.100460
B. Pascariu , M. Samà , P. Pellegrini , A. D’Ariano , J. Rodriguez , D. Pacciarelli

The train routing selection problem (TRSP) addresses the optimized selection of alternative routes as a preliminary step for real-time railway traffic management problem (rtRTMP). In the TRSP, route selection relies on estimating potential delays resulting from scheduling decisions. The selected routes are then exclusively applied in the rtRTMP. While prior research established the mathematical model and solution algorithms for the TRSP, its practical application in real-time rail traffic management remains limited. The existing TRSP model focuses on a single objective function for the rtRTMP. However, in practice, various stakeholders may prioritize different objectives, leading to diverse objective functions employed in the rtRTMP. This paper extends the TRSP model by considering a range of suitable objectives for the rtRTMP. We formulate the TRSP for each objective function and enhance the cost estimation model to evaluate the correspondence between the TRSP and rtRTMP objective functions. We then assess the overall effectiveness of the TRSP for the rtRTMP through an evaluation that takes into account several configurations of the model and the rtRTMP solution approach used. Our purpose is to enlarge the applicability of the TRSP and enhance the efficiency of the rtRTMP for real-world systems. The paper includes an in-depth computational analysis of two French case studies to investigate the performance of the TRSP across different rtRTMP configurations.

列车路线选择问题(TRSP)解决了作为实时铁路交通管理问题(rtRTMP)第一步的备选路线优化选择问题。在 TRSP 中,路线选择依赖于对调度决策导致的潜在延误进行估计。然后,选定的路线将专门应用于 rtRTMP。虽然先前的研究建立了 TRSP 的数学模型和求解算法,但其在实时轨道交通管理中的实际应用仍然有限。现有的 TRSP 模型重点关注 rtRTMP 的单一目标函数。然而,在实际应用中,各利益相关方可能会优先考虑不同的目标,从而导致轨道交通实时管理计划采用不同的目标函数。本文扩展了 TRSP 模型,为 rtRTMP 考虑了一系列合适的目标。我们为每个目标函数制定了 TRSP,并改进了成本估算模型,以评估 TRSP 与 rtRTMP 目标函数之间的对应关系。然后,我们通过考虑模型的几种配置和所使用的 rtRTMP 解决方法,评估 TRSP 对 rtRTMP 的整体有效性。我们的目的是扩大 TRSP 的适用性,提高 rtRTMP 在实际系统中的效率。本文包括对两个法国案例研究的深入计算分析,以研究 TRSP 在不同 rtRTMP 配置下的性能。
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引用次数: 0
Deep reinforcement learning with predictive auxiliary task for autonomous train collision avoidance 带预测性辅助任务的深度强化学习用于自动列车防撞
IF 3.7 Q3 TRANSPORTATION Pub Date : 2024-06-10 DOI: 10.1016/j.jrtpm.2024.100453
Antoine Plissonneau , Luca Jourdan , Damien Trentesaux , Lotfi Abdi , Mohamed Sallak , Abdelghani Bekrar , Benjamin Quost , Walter Schön

The contribution of this paper consists of a deep reinforcement learning (DRL) based method for autonomous train collision avoidance. While DRL applied to autonomous vehicles’ collision avoidance has shown interesting results compared to traditional methods, train-like vehicles are not currently covered. In addition, DRL applied to collision avoidance suffers from sparse rewards, which can lead to poor convergence and long training time. To overcome these limitations, this paper proposes a method for training a reinforcement learning (RL) agent for collision avoidance using local obstacle information mapped into occupancy grids. This method also integrates a network architecture containing a predictive auxiliary task consisting in future state prediction and encouraging the intermediate representation to be predictive of obstacle trajectories. A comparison study conducted on multiple simulated scenarios demonstrates that the trained policy outperforms other deep-learning-based policies as well as human driving in terms of both safety and efficiency. As a first step toward the certification of a DRL based method, this paper proposes to approximate the policy learned by the RL agent with an interpretable decision tree. Although this approximation results in a loss of performance, it enables a safety analysis of the learned function and thus paves the way to use the strengths of RL in certifiable algorithms. As this work is pioneering the use of RL for collision avoidance of rail-guided vehicles, and to facilitate future work by other engineers and researchers, a RL-ready simulator is provided with this paper.

本文的贡献在于提出了一种基于深度强化学习(DRL)的自动列车防撞方法。与传统方法相比,将 DRL 应用于自动驾驶汽车的防撞已经取得了令人感兴趣的结果,但目前还没有涉及类似火车的车辆。此外,应用于防撞的 DRL 还存在奖励稀疏的问题,这可能导致收敛性差和训练时间长。为了克服这些局限性,本文提出了一种利用映射到占位网格中的局部障碍物信息来训练避撞强化学习(RL)代理的方法。该方法还整合了一个网络架构,其中包含一个预测性辅助任务,包括未来状态预测,并鼓励中间表征对障碍物轨迹进行预测。在多个模拟场景中进行的对比研究表明,经过训练的策略在安全性和效率方面都优于其他基于深度学习的策略以及人类驾驶。作为基于 DRL 方法认证的第一步,本文建议用可解释的决策树来近似 RL 代理学习到的策略。虽然这种近似会导致性能损失,但却能对所学功能进行安全分析,从而为在可认证算法中利用 RL 的优势铺平道路。由于这项工作是将 RL 用于轨道制导车辆防撞的先驱,为方便其他工程师和研究人员今后开展工作,本文提供了一个 RL 就绪模拟器。
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引用次数: 0
Clustering railway passenger demand patterns from large-scale origin–destination data 从大规模始发站数据中归类铁路旅客需求模式
IF 2.6 Q3 TRANSPORTATION Pub Date : 2024-05-22 DOI: 10.1016/j.jrtpm.2024.100452

Train passenger demand fluctuates throughout the day. In order to let train services, such as the line plan and timetable, match this fluctuating demand, insights are needed into how the demand is changing and for which periods the demand is relatively stable. Hierarchical clustering on both regular and normalized origin–destination (OD) data is used to determine for each workday continuous time-of-day periods in which the passenger demand is homogeneous. The periods found for each workday are subsequently used as input in a clustering algorithm to look for similarities and differences between workdays. The methods for finding homogeneous periods during the day and week are applied to a case study covering a large part of the railway network in the Netherlands. We find large differences between the periods based on regular OD matrices and those based on normalized OD matrices. The periods based on regular OD matrices are more compact in terms of passenger volumes and average kms travelled and therefore more suitable to use as input for designing a service plan. Comparison of different workdays shows that mainly the peak periods on Friday are far away from Monday to Thursday, and hence could benefit from an altered service plan.

火车乘客的需求全天都在波动。为了使列车服务(如线路计划和时刻表)与这种波动的需求相匹配,需要深入了解需求是如何变化的,以及哪些时段的需求相对稳定。对常规数据和归一化的始发站数据进行分层聚类,可确定每个工作日中乘客需求均匀的连续时段。为每个工作日找到的时段随后被用作聚类算法的输入,以寻找工作日之间的异同。我们将寻找日间和周间同质时段的方法应用于一项案例研究,研究范围涵盖荷兰大部分铁路网。我们发现,基于常规 OD 矩阵的时段与基于归一化 OD 矩阵的时段之间存在很大差异。基于常规 OD 矩阵的时段在乘客量和平均行驶公里数方面更为紧凑,因此更适合用作设计服务计划的输入。不同工作日的比较显示,主要是周五的高峰期与周一至周四的高峰期相距较远,因此可以从改变服务计划中获益。
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引用次数: 0
Train separation at cruising speed, how it can improve current railway operations 列车以巡航速度分离,如何改善目前的铁路运行状况
IF 3.7 Q3 TRANSPORTATION Pub Date : 2024-05-17 DOI: 10.1016/j.jrtpm.2024.100451
Michael Nold, Francesco Corman

This paper systematically reviewed the slipping operation, which is a train separation at cruising speed. For this, we describe the historical and operational background of the operation scenario practiced for over 100 years. Based on the concept of slipping, we discuss the holistic potential to improve current railway operations, considering travel time saving, energy saving, the increase of capacity utilization, station topology, driver requirements, and vehicle usage. Finally, a simulation of a theoretical urban railway line with several scenarios quantifies the magnitudes of the improvements. Based on the slipping test cases, one parameter can improve enormously, e.g., up to −65 % energy saving, −33 % capacity usage, and travel time reductions. Otherwise, slipping can slightly improve several parameters simultaneously.

本文系统地回顾了滑动运行,即列车在巡航速度下的分离。为此,我们介绍了这种运行方式 100 多年来的历史和运行背景。在滑行概念的基础上,我们从节省旅行时间、节约能源、提高运力利用率、车站拓扑结构、司机要求和车辆使用等方面,讨论了改善目前铁路运营的整体潜力。最后,我们对理论上的城市铁路线进行了模拟,对几种情况下的改善幅度进行了量化。根据滑行测试案例,一个参数就能带来巨大的改善,例如,节能可达-65%,运力利用率可达-33%,旅行时间也能减少。否则,滑行可同时轻微改善多个参数。
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引用次数: 0
Analyzing the impact of timetable elements on railway line capacity 分析时刻表要素对铁路线路能力的影响
IF 3.7 Q3 TRANSPORTATION Pub Date : 2024-05-11 DOI: 10.1016/j.jrtpm.2024.100450
Qinglun Zhong , Libin Zang , Rudong Yang , Zhao Sheng , Ruihua Xu

Train timetabling poses inherent challenges, prompting the need to enhance existing schedules by extracting valuable insights from the current timetable structure. By such philosophy, this paper studies the impact of timetable elements on the consumed capacity. These elements primarily encompass train operation parameters, including running times and stop plans. Their impact is defined as the deviation of consumed capacity relative to their variations. We initially establish a link between consumed capacity and timetable elements. This relationship is articulated as the signed sum of timetable elements along a designated ”critical path”. Then, the limited impact of any timetable element is clarified, namely changing an element can impact the consumed capacity with its neighbor trains in a combinatorial way. With this knowledge, we analyze the impact of a single element, using stop plans for example. This result is then generalized into analyzing the impact of several dependent and independent stop plans. The findings on capacity calculation and impact analysis of a single element are tested through real-world numerical computations and then extended to analyzing various capacity factors, such as average speed and heterogeneity.

列车时刻表编制工作面临着固有的挑战,因此需要通过从当前的时刻表结构中提取有价值的见解来改进现有的时刻表。根据这一理念,本文研究了时间表要素对消耗运力的影响。这些要素主要包括列车运行参数,包括运行时间和停车计划。它们的影响被定义为消耗运力相对于其变化的偏差。我们首先在已消耗运力和时刻表要素之间建立联系。这种关系被表述为沿指定 "关键路径 "的时间表要素的符号总和。然后,我们明确了任何时刻表元素的有限影响,即改变一个元素都会以组合方式影响其相邻列车的消耗能力。有了这些知识,我们就能以停站计划为例,分析单个要素的影响。然后,我们将这一结果推广到分析多个从属和独立停车计划的影响。我们通过实际数值计算检验了单个元素的容量计算和影响分析结果,然后将其扩展到分析各种容量因素,如平均速度和异质性。
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引用次数: 0
Railway signals passed at danger: A bibliometric analysis 在危险中通过的铁路信号:文献计量分析
IF 3.7 Q3 TRANSPORTATION Pub Date : 2024-05-09 DOI: 10.1016/j.jrtpm.2024.100449
Satish Ajabrao Ambhore , Valentino Sangiorgio , Richard van der Weide

Signal passed at danger (SPAD) is the most frequent cause of rail accidents as confirmed by relevant investigation in the field. The purpose of this paper was to identify the trends in SPAD research. Consequently, we aim to inspire and motivate future researchers, particularly academicians, to delve into this critical issue. Currently, the majority of researchers working on this topic are from the railway industry. For this reason, conducting a comprehensive review of existing research would yield significant value to both the academic and technical communities. This research examines the developments and accomplishments in this field concerning SPAD using the bibliometric library of R software from 1966 to the end of 2022. By bridging the gap in the existing literature, this research facilitates the global exchange of knowledge among railway experts, ultimately contributing to the reduction of safety risks and associated economic costs.

经相关实地调查证实,危险信号通过(SPAD)是导致铁路事故的最常见原因。本文旨在确定 SPAD 的研究趋势。因此,我们旨在启发和激励未来的研究人员,尤其是学术界人士,深入研究这一关键问题。目前,大多数研究人员都来自铁路行业。因此,对现有研究进行全面回顾将对学术界和技术界产生重大价值。本研究利用 R 软件的文献计量库,对 1966 年至 2022 年底该领域有关 SPAD 的发展和成就进行了研究。本研究填补了现有文献的空白,促进了全球铁路专家之间的知识交流,最终有助于降低安全风险和相关经济成本。
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引用次数: 0
期刊
Journal of Rail Transport Planning & Management
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