Journal of Rail Transport Planning & Management最新文献

英文中文

Analysis of freight train passing a stop signal using machine learning: Application of XGBoost and SHAP 货运列车通过停车信号的机器学习分析：XGBoost和SHAP的应用

IF 2.6 Q3 TRANSPORTATION

Journal of Rail Transport Planning & Management

Pub Date : 2025-06-24 DOI: 10.1016/j.jrtpm.2025.100532

Mona Ahmadi Rad , Lianne M. Lefsrud , Michael T. Hendry , Asdrubal Cheng Cen , Sara Soltaninejad

Passing a Stop Signal (PASS) is a critical safety concern in railway operations, with the potential to cause serious accidents. This study investigates non-human contributing factors to PASS events in Canadian mainline freight operations using machine learning. We analyze incident narratives from the Rail Occurrence Database System (RODS) through text mining and enrich them with geospatial and weather data. We develop a binary classification model using XGBoost and interpret feature importance and interactions with SHAP (SHapley Additive exPlanations). To address class imbalance and improve model performance, we apply a custom sampling method, combined with hyperparameter tuning and data standardization. Key contributors to PASS events include sharp track curvature near signals, downhill grades, low atmospheric pressure, high relative humidity, non-clear weather, and heavy traffic—placing Rocky Mountain subdivisions among the highest-risk areas. The model also reveals that combinations of environmental conditions, such as low temperature, low pressure, and high humidity, increase the likelihood of PASS events by reducing visibility and braking effectiveness. This study offers methodological and empirical contributions by modelling complex operational contexts, incorporating underexplored environmental factors, and producing region-specific insights. The proposed framework informs proactive safety strategies and supports risk analysis in other linear infrastructure systems.

通过停车信号（PASS）是铁路运营中一个关键的安全问题，有可能导致严重事故。本研究使用机器学习调查了加拿大干线货运业务中PASS事件的非人为影响因素。我们通过文本挖掘分析铁路事故数据库系统（RODS）中的事件叙述，并用地理空间和天气数据丰富它们。我们使用XGBoost开发了一个二元分类模型，并解释了特征重要性和与SHapley Additive explanation （SHapley Additive exPlanations）的相互作用。为了解决类失衡和提高模型性能，我们采用自定义采样方法，结合超参数调优和数据标准化。PASS事件的主要因素包括信号附近的急剧轨道曲率，下坡坡度，低气压，高相对湿度，不晴朗的天气和繁忙的交通，使落基山细分地区成为风险最高的地区。该模型还显示，低温、低压和高湿等环境条件的组合会降低能见度和制动效果，从而增加发生超车事件的可能性。本研究通过模拟复杂的操作背景、纳入未充分开发的环境因素和产生特定区域的见解，提供了方法和经验贡献。拟议的框架为主动安全策略提供信息，并支持其他线性基础设施系统的风险分析。

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引用次数: 0

System analysis of train operations to improve freight flow on high gradient rail bottleneck section with mixed traffic 混合交通高梯度轨道瓶颈段列车运行改善货流的系统分析

IF 2.6 Q3 TRANSPORTATION

Journal of Rail Transport Planning & Management

Pub Date : 2025-06-04 DOI: 10.1016/j.jrtpm.2025.100522

Kritika Karwasra , Narayan Rangaraj , Karuna Nidhi Singh

This paper analyzes the operations of freight trains on high-gradient rail sections, which are common on India’s western and eastern coasts (known as a ghat section). Operations in these sections differ from regular rail operations. Resource usage differs for passenger trains and freight trains and depends on whether the train is ascending or descending the graded section. This creates an imbalance in resource usage and adds complexity to these operations. Priority for passenger trains adds to this complication, making freight train scheduling challenging. This study analyzes different techniques to improve the freight throughput of the graded sections, considering the case study of the Lonavala-Karjat section. Freight trains on this section experience extended delays, leading to a network capacity bottleneck. Identifying the root cause of these delays is not straightforward, as operations are interrelated. Therefore, the study employs a systems thinking approach, beginning with identifying bottleneck resources. A discrete event simulation model is employed due to its ability to replicate complex real-world scenarios and assess the impact of different variables on system performance. Based on the findings, policies are proposed to optimize freight flow on the graded rail section.

本文分析了货运列车在高坡度铁路路段的运行情况，这在印度西部和东部海岸（被称为ghat段）很常见。这些路段的运营不同于普通铁路运营。旅客列车和货运列车的资源使用情况不同，这取决于列车是上升还是下降。这造成了资源使用的不平衡，并增加了这些操作的复杂性。客运列车的优先级增加了这种复杂性，使货运列车调度具有挑战性。本研究以Lonavala-Karjat路段为例，分析不同的改善分级路段货物吞吐量的方法。这一路段的货运列车延误时间延长，导致网络容量瓶颈。确定这些延迟的根本原因并不简单，因为操作是相互关联的。因此，本研究采用系统思维方法，从识别瓶颈资源入手。采用离散事件仿真模型是因为它能够复制复杂的现实世界场景并评估不同变量对系统性能的影响。在此基础上，提出了分级段货流优化政策。

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引用次数: 0

Designing an optimized fueling infrastructure for a hydrogen railway system 为氢能铁路系统设计优化的加氢基础设施

IF 2.6 Q3 TRANSPORTATION

Journal of Rail Transport Planning & Management

Pub Date : 2025-06-02 DOI: 10.1016/j.jrtpm.2025.100524

Alessio Trivella, Amina Balha, Daniela Guericke

Hydrogen use is increasing in transportation, including within the railway sector. In collaboration with a governmental institution in the Netherlands, we study how to design an efficient hydrogen fueling infrastructure for a railway system. The problem involves selecting yards in a network for hydrogen fueling, assigning trains to these yards, locating hydrogen storage and fueling stations, and connecting them via pipelines. This key planning phase must avoid oversizing costly fueling infrastructure while accounting for track availability at yards and costs due to fueling operations. We formulate this novel problem, which has the structure of a nested facility location problem, as a mixed-integer linear program to minimize total annualized investment and operational costs. Due to the complexity of real-sized instances, we propose a matheuristic that estimates the infrastructural costs for each yard and train assignment by combining a constructive algorithm with a set covering model. It then solves a single-stage facility location problem to select yards and assign trains, followed by a yard-level improvement phase. Numerical experiments on a real Dutch case show that our approach delivers high-quality solutions quickly and offer insights into the optimal infrastructure design depending on the discretization of yard areas, number of trains, and other parameters.

氢在交通运输中的使用正在增加，包括在铁路部门。我们与荷兰的一家政府机构合作，研究如何为铁路系统设计高效的氢燃料基础设施。这个问题涉及到在一个网络中选择加氢站，为这些加氢站分配火车，定位储氢站和加氢站，并通过管道将它们连接起来。这一关键规划阶段必须避免成本高昂的加氢基础设施规模过大，同时考虑到堆场的轨道可用性和加氢操作的成本。我们将这个具有嵌套设施选址问题结构的新问题表述为一个混合整数线性规划，以最小化总年化投资和运营成本。由于实际规模实例的复杂性，我们提出了一种数学方法，通过将构造算法与集合覆盖模型相结合来估计每个场站和列车分配的基础设施成本。然后，它解决了一个单阶段的设施选址问题，即选择车场和分配列车，随后是车场水平的改进阶段。在一个真实的荷兰案例中进行的数值实验表明，我们的方法可以快速提供高质量的解决方案，并根据场站面积、列车数量和其他参数的离散化，为优化基础设施设计提供见解。

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引用次数: 0

Utilizing phase-type distributions for queueing-based railway junction performance determination 基于相位型分布的排队铁路枢纽性能确定

IF 2.6 Q3 TRANSPORTATION

Journal of Rail Transport Planning & Management

Pub Date : 2025-05-26 DOI: 10.1016/j.jrtpm.2025.100523

Tamme Emunds, Nils Nießen

To ensure the effective and objective development of transportation networks, it is crucial to identify performance limitations across various subsystems. A timetable-independent assessment of infrastructure capacity at railway junctions is a fundamental aspect of long-term rail network planning. While recent research introduced queueing-based methods to quantify route-based railway junction performance, modelling arrival and service processes has been limited to exponential distributions. This work utilizes Phase-Type Distributions to propose an extension to a previously described Continuous-Time Markov Chain model. In a comparison between assumed distribution combinations, the effect of a more detailed stochastic process modelling is described. Furthermore, an analysis of the differences to a simulation method is conducted for an exemplary railway junction. The introduced method enables infrastructure managers to accurately model stochastic processes for performance determination in the early stages of the strategic planning phase.

为了确保交通网络的有效和客观发展，确定各个子系统的性能限制是至关重要的。对铁路枢纽的基础设施能力进行独立于时间表的评估是长期铁路网规划的一个基本方面。虽然最近的研究引入了基于排队的方法来量化基于路线的铁路枢纽性能，但对到达和服务过程的建模仅限于指数分布。这项工作利用相型分布提出了对先前描述的连续时间马尔可夫链模型的扩展。在假设分布组合之间的比较中，描述了更详细的随机过程建模的效果。此外，以铁路枢纽为例，分析了该方法与仿真方法的差异。所介绍的方法使基础设施管理人员能够在战略规划阶段的早期阶段准确地为性能确定的随机过程建模。

引用次数: 0

Solving rescheduling problems in heterogeneous urban railway networks using hybrid quantum–classical approach 用量子-经典混合方法求解异构城市铁路网重调度问题

IF 2.6 Q3 TRANSPORTATION

Journal of Rail Transport Planning & Management

Pub Date : 2025-05-17 DOI: 10.1016/j.jrtpm.2025.100521

Mátyás Koniorczyk , Krzysztof Krawiec , Ludmila Botelho , Nikola Bešinović , Krzysztof Domino

We address the applicability of a hybrid quantum–classical heuristics for practical railway rescheduling management problems. We build an integer linear programming model and solve it with D-Wave’s quantum–classical hybrid solver (CQM) as well as with CPLEX, for comparison. The proposed approach is demonstrated on a real-life heterogeneous urban network in Poland, including both single- and multi-track segments. All the requirements posed by the operator of the network are included in the model. The computational results demonstrate the readiness for application and the benefits of quantum–classical hybrid solvers in a realistic railway scenario: they yield acceptable solutions on time, which is a critical requirement in a rescheduling situation. In particular, CQM as a probabilistic heuristic solver provides a number of feasible, close-to-optimal solutions the dispatcher can choose from.

我们讨论了量子-经典混合启发式方法在实际铁路调度管理问题中的适用性。我们建立了一个整数线性规划模型，并用D-Wave的量子-经典混合求解器（CQM）和CPLEX进行求解，以作比较。提出的方法在波兰的一个现实的异构城市网络中得到了验证，包括单轨和多轨路段。该模型包含了网络运营者提出的所有要求。计算结果证明了量子-经典混合求解方法在实际铁路场景中的应用可行性和优点：它们可以在重新调度的情况下按时得到可接受的解。特别是，CQM作为一种概率启发式求解器提供了许多可行的、接近最优的解决方案供调度程序选择。

引用次数: 0

Optimizing and synchronizing timetables in an urban subway network considering trains’ speed profiles and skip-stop strategy 考虑列车速度分布和跳站策略的城市地铁网络时刻表优化与同步

IF 2.6 Q3 TRANSPORTATION

Journal of Rail Transport Planning & Management

Pub Date : 2025-05-05 DOI: 10.1016/j.jrtpm.2025.100520

Alireza Eslami , Yousef Shafahi , Shayan Bafandkar

This research aims to introduce a mathematical model capable of producing an optimal and coordinated timetable for the entire urban rail network to minimize passengers’ travel times and the trains’ energy consumption. The proposed model focuses on different speed profiles and a skip-stop strategy while considering the stochastic nature of passengers’ arrival and departure rates. This novel model can generate an optimal real-time schedule under variations in passenger demand. The implementation of a multi-agent deep deterministic policy gradient has been described, and it has been compared with a genetic algorithm. Eventually, this methodology is implemented on lines 1, 2, and 4 of Tehran’s metro network as a case study. The results indicate that using the skip-stop strategy and optimizing trains’ speed profiles along their paths can reduce the networks’ costs, including passengers’ waiting costs and the trains’ energy consumption costs, by 2.9% and 14.9%, respectively.

本研究旨在引入一种数学模型，能够为整个城市轨道网络产生最佳和协调的时间表，以最大限度地减少乘客的旅行时间和列车的能源消耗。该模型在考虑乘客到达率和离开率的随机特性的同时，重点考虑了不同的速度分布和跳停策略。该模型可以在乘客需求变化的情况下生成最优的实时时刻表。描述了一种多智能体深度确定性策略梯度的实现，并将其与遗传算法进行了比较。最后，将该方法应用于德黑兰地铁网络的1、2、4号线作为案例研究。结果表明，采用跳站策略和优化列车运行路径的速度分布，可使网络成本（包括乘客等待成本和列车能耗成本）分别降低2.9%和14.9%。

引用次数: 0

Weekly crew scheduling for freight rail engineers: A network approach 货运铁路工程师每周班组调度：网络方法

IF 2.6 Q3 TRANSPORTATION

Journal of Rail Transport Planning & Management

Pub Date : 2025-04-02 DOI: 10.1016/j.jrtpm.2025.100519

Jinhua Lyu, Jonathan F. Bard

Freight rail engineers and conductors have long faced unpredictable and inflexible work schedules, leading to on-the-job fatigue, compromised safety, and poor work-life balance. This paper aims to construct robust weekly schedules for these crew members to alleviate the pressures associated with irregular and unpredictable work hours. The scheduling problem is formulated as a multi-commodity network flow problem on a directed time-space graph. Both two-city and three-city districts are addressed. To account for the variability in travel times, a set of scenarios is defined in which demand is increased by up to 20% to build slack into the schedules. The results are validated using Monte Carlo simulation where 100 random weekly instances are generated for each city pair and key performance metrics assessed. Major findings show that (i) optimal weekly schedules can be constructed in minutes for engineers in crew districts with two cities, and in several hours for engineers in crew districts with three cities, (ii) different percentages of demand increase significantly affect the degree of robustness, and (iii) forming crew districts with three cities rather than two gives better results in terms of required number of engineers and trip coverage rates.

长期以来，货运铁路工程师和售票员一直面临着不可预测和不灵活的工作时间表，导致工作疲劳、安全性受损以及工作与生活的不平衡。本文旨在为这些船员构建健全的每周时间表，以减轻与不规律和不可预测的工作时间相关的压力。将调度问题表述为有向时空图上的多商品网络流问题。两个城市和三个城市的地区都有地址。为了考虑出行时间的可变性，我们定义了一组场景，在这些场景中，需求最多增加20%，从而在时间表中建立空闲。使用蒙特卡罗模拟对结果进行验证，其中为每个城市对生成100个随机的每周实例，并评估关键性能指标。主要研究结果表明：(1)两个城市的船员区工程师可以以分钟为单位构建最优周计划，三个城市的船员区工程师可以以几个小时为单位构建最优周计划；(2)不同的需求增长百分比显著影响鲁棒性程度；(3)三个城市的船员区比两个城市的船员区在所需工程师数量和行程覆盖率方面效果更好。

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引用次数: 0

Statistical analysis of geoinformation data for increasing railway safety 提高铁路安全的地理信息数据统计分析

IF 2.6 Q3 TRANSPORTATION

Journal of Rail Transport Planning & Management

Pub Date : 2025-03-20 DOI: 10.1016/j.jrtpm.2025.100517

Katarzyna Gawlak , Jarosław Konieczny , Krzysztof Domino , Jarosław Adam Miszczak

The impact of rail transport on the environment is one of the crucial factors for the sustainable development of this form of mass transport. We present a data-driven analysis of wild animal railway accidents in the region of southern Poland, a step to create the train driver warning system. We built our method by harnessing the Bayesian approach to the statistical analysis of information about the geolocation of the accidents. The implementation of the proposed model does not require advanced knowledge of data mining and can be applied even in less developed railway systems with small IT support. Furthermore, we have discovered unusual patterns of accidents while considering the number of trains and their speed and time at particular geographical locations of the railway network. We test the developed approach using data from southern Poland, compromising wildlife habitats and one of the most urbanised regions in Central Europe, based on this we conclude that our model is best suited to railway lines that pass through varying types of landscape.

铁路运输对环境的影响是这种大众运输方式可持续发展的关键因素之一。我们对波兰南部地区的野生动物铁路事故进行了数据驱动分析，这是创建火车司机警告系统的一步。我们利用贝叶斯方法对事故的地理位置信息进行统计分析，从而建立了我们的方法。所建议的模型的实现不需要高级的数据挖掘知识，甚至可以在IT支持较小的欠发达铁路系统中应用。此外，在考虑铁路网络特定地理位置的列车数量及其速度和时间时，我们发现了不寻常的事故模式。我们使用来自波兰南部的数据来测试开发的方法，这些数据损害了野生动物栖息地和中欧城市化程度最高的地区之一，基于此，我们得出结论，我们的模型最适合穿过不同类型景观的铁路线。

引用次数: 0

Identifying subway commuters travel patterns using traffic smart card data: A topic model 利用交通智能卡数据识别地铁通勤者的出行模式：一个主题模型

IF 2.6 Q3 TRANSPORTATION

Journal of Rail Transport Planning & Management

Pub Date : 2025-03-10 DOI: 10.1016/j.jrtpm.2024.100497

Peng He , Danyong Feng , Yang Yang , Zijia Wang

The paper presents a novel approach using Hierarchical Dirichlet Processes (HDP) integrated with K-means clustering to analyze public transit commuting behaviors using smartcard and POI data. The HDP, an unsupervised model, is designed to discern travel activities, however, little is done for this purpose. Our study proposed representing each trip using four features (duration, date, arrival time, and station type classified using POI-data) as inputs to the HDP model, which outputs the identification of specific activities such as home, work, and leisure. A comparison to other methods including trip frequency, activity duration, and Hidden Markov models demonstrates that our approach offers superior fit, as evidenced by lower perplexity and higher similarity metrics. To further refine the classification of commuting behaviors, we applied a two-step clustering algorithm that considers features such as regularity, temporality, and spatiality, resulting in the identification of strong and weak commuting behavior patterns. This classification provides urban planners with insights into the spatiotemporal characteristics of travelers in urban rail transit systems, thereby supporting more effective urban planning.

基于智能卡和POI数据，提出了一种基于分层狄利克雷过程（HDP）和K-means聚类的公共交通通勤行为分析方法。HDP是一种无监督模型，旨在识别旅行活动，然而，在这方面做得很少。我们的研究建议使用四个特征（持续时间、日期、到达时间和使用poi数据分类的站点类型）来表示每次旅行，作为HDP模型的输入，该模型输出特定活动（如家庭、工作和休闲）的识别。与其他方法（包括行程频率、活动持续时间和隐马尔可夫模型）的比较表明，我们的方法具有更好的拟合性，这一点可以通过更低的困惑度和更高的相似性指标得到证明。为了进一步完善通勤行为的分类，我们采用了一种考虑规律性、时代性和空间性等特征的两步聚类算法，从而识别出强弱通勤行为模式。这种分类为城市规划者提供了对城市轨道交通系统中旅客时空特征的洞察，从而支持更有效的城市规划。

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引用次数: 0

Efficiency analysis of European railway companies and the effect of demand reduction 欧洲铁路公司效率分析及需求减少的影响

IF 2.6 Q3 TRANSPORTATION

Journal of Rail Transport Planning & Management

Pub Date : 2025-03-08 DOI: 10.1016/j.jrtpm.2025.100516

Arsen Benga , María Jesús Delgado Rodríguez , Sonia de Lucas Santos , Glediana Zeneli

Enhancing the efficiency of railways is key to the future of sustainable transport. The objective of this work is to identify leading railways in Europe, investigate sources of inefficiency, and guide underperformers towards best practices. We explore efficiency for some selected 21 prominent railways during 2016–2018 using Network Data Envelopment Analysis. The ranking obtained indicates averagely low efficiency scores, with slight improvements over time. Next, we build a performance matrix to determine the priority improvements for each company. The Tobit regression implies that the nation's wealth, length of haul, length of trip, and traffic density have a significantly positive relationship with the efficiency scores. We also observed no significant impact of companies' outputs on their efficiency scores, indicating that any minor decrease in transport demand is unlikely to impose significant constraints on efficiency scores.

提高铁路的效率是未来可持续交通的关键。这项工作的目的是确定欧洲领先的铁路，调查效率低下的原因，并指导表现不佳的铁路走向最佳实践。我们使用网络数据包络分析探讨了2016-2018年期间选定的21条主要铁路的效率。所获得的排名表明，效率得分平均较低，随着时间的推移略有提高。接下来，我们构建一个绩效矩阵来确定每个公司的优先级改进。Tobit回归表明，国家财富、运输距离、旅行距离和交通密度与效率得分呈显著正相关。我们还观察到，公司的产出对其效率得分没有显著影响，这表明运输需求的任何微小下降都不太可能对效率得分施加显著约束。

引用次数: 0

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