Journal of Rail Transport Planning & Management最新文献

英文中文

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。

{"title":"Weekly crew scheduling for freight rail engineers: A network approach","authors":"Jinhua Lyu, Jonathan F. Bard","doi":"10.1016/j.jrtpm.2025.100519","DOIUrl":"10.1016/j.jrtpm.2025.100519","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":51821,"journal":{"name":"Journal of Rail Transport Planning & Management","volume":"34 ","pages":"Article 100519"},"PeriodicalIF":2.6,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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。

{"title":"Identifying subway commuters travel patterns using traffic smart card data: A topic model","authors":"Peng He , Danyong Feng , Yang Yang , Zijia Wang","doi":"10.1016/j.jrtpm.2024.100497","DOIUrl":"10.1016/j.jrtpm.2024.100497","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":51821,"journal":{"name":"Journal of Rail Transport Planning & Management","volume":"34 ","pages":"Article 100497"},"PeriodicalIF":2.6,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Predicting primary delay of train services using graph-embedding based machine learning 使用基于图嵌入的机器学习预测列车服务的主要延迟

IF 2.6 Q3 TRANSPORTATION

Journal of Rail Transport Planning & Management

Pub Date : 2025-03-06 DOI: 10.1016/j.jrtpm.2025.100518

Ruifan Tang, Ronghui Liu, Zhiyuan Lin

Train delays can cause huge economic loss and passenger dissatisfaction. The Train Delay Prediction Problem has been investigated by a large number of studies. How to best represent certain features of a train is key to successful prediction. For instance, due to its complex topological nature, a train's route (i.e., origin, intermediate stations and destination) is one of the most difficult features to effectively represent. This study introduces graph embedding to understand and model the complex structure of a railway network which is able to capture a comprehensive collection of features including network topology, infrastructure and train profile. In particular, for the first time, we propose an approach to embed a train's route in a network topology perspective based on Structural Deep Network Embedding (SDNE) and Singular Value Decomposition (SVD). Compared to a conventional advanced method, Principle Component Analysis (PCA), our route embedding not only significantly reduces feature vector length and computational effort, but is also highly accurate and reliable in terms of capturing network topology as evidenced by K-means clustering. Computational experiments based on real-world cases from a UK train operator (TransPennine Express) show our graph-embedding based models are competitive in prediction accuracy and F1-score while are substantially computationally efficient compared to PCA.

火车延误会造成巨大的经济损失和乘客的不满。列车延误预测问题已经得到了大量的研究。如何最好地表现火车的某些特征是成功预测的关键。例如，由于其复杂的拓扑性质，火车的路线（即始发站，中间站和目的地）是最难以有效表示的特征之一。本研究引入图嵌入来理解和建模铁路网络的复杂结构，从而能够捕获包括网络拓扑、基础设施和列车轮廓在内的综合特征集合。特别是，我们首次提出了一种基于结构深度网络嵌入（SDNE）和奇异值分解（SVD）的网络拓扑视角下嵌入列车路线的方法。与传统的主成分分析（PCA）方法相比，我们的路径嵌入方法不仅显著减少了特征向量的长度和计算量，而且在捕获网络拓扑方面也具有很高的准确性和可靠性，K-means聚类证明了这一点。基于英国火车运营商（TransPennine Express）的真实案例的计算实验表明，我们基于图嵌入的模型在预测精度和f1分数方面具有竞争力，同时与PCA相比，计算效率更高。

{"title":"Predicting primary delay of train services using graph-embedding based machine learning","authors":"Ruifan Tang, Ronghui Liu, Zhiyuan Lin","doi":"10.1016/j.jrtpm.2025.100518","DOIUrl":"10.1016/j.jrtpm.2025.100518","url":null,"abstract":"<div><div>Train delays can cause huge economic loss and passenger dissatisfaction. The Train Delay Prediction Problem has been investigated by a large number of studies. How to best represent certain features of a train is key to successful prediction. For instance, due to its complex topological nature, a train's route (i.e., origin, intermediate stations and destination) is one of the most difficult features to effectively represent. This study introduces graph embedding to understand and model the complex structure of a railway network which is able to capture a comprehensive collection of features including network topology, infrastructure and train profile. In particular, for the first time, we propose an approach to embed a train's route in a network topology perspective based on Structural Deep Network Embedding (SDNE) and Singular Value Decomposition (SVD). Compared to a conventional advanced method, Principle Component Analysis (PCA), our route embedding not only significantly reduces feature vector length and computational effort, but is also highly accurate and reliable in terms of capturing network topology as evidenced by K-means clustering. Computational experiments based on real-world cases from a UK train operator (TransPennine Express) show our graph-embedding based models are competitive in prediction accuracy and F1-score while are substantially computationally efficient compared to PCA.</div></div>","PeriodicalId":51821,"journal":{"name":"Journal of Rail Transport Planning & Management","volume":"34 ","pages":"Article 100518"},"PeriodicalIF":2.6,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A new look at the shape characteristics of optimal speed profile for energy-efficient train control considering multi-train power flow 考虑多列功率流的节能列车控制最优速度剖面形状特征的新研究

IF 2.6 Q3 TRANSPORTATION

Journal of Rail Transport Planning & Management

Pub Date : 2025-02-26 DOI: 10.1016/j.jrtpm.2025.100515

Yu Rao , Qiangqiang Liu , Qingyuan Wang , Tianxiang Li , Mingyu Zhang

The key point for the energy-efficient train control (EETC) in a multi-train system is effectively utilizing the output power of other trains. However, obtaining the optimal solution of the EETC problem considering multi-train power flow requires high-precision calculation of the adjoint variables, which is time-consuming. In this paper, we revisit the problem and introduce a speed volatility functional to analyze the shape of the optimal speed profile and the corresponding optimal control modes for the train under different external power and track gradients. Based on this analysis, a fast-solving algorithm is devised. Case studies are conducted to validate our theoretical results, and demonstrate that the proposed algorithm achieves a significant improvement in computational speed (over 99%) compared to the global optimal algorithm (Rao et al., 2023a) while ensuring the energy saving effectiveness.

多列系统节能列车控制的关键是有效利用其他列车的输出功率。然而，考虑多列潮流的EETC问题的最优解需要高精度的伴随变量计算，耗时长。在本文中，我们重新审视了这个问题，并引入了一个速度波动函数来分析在不同外部功率和轨道梯度下列车的最优速度轮廓形状和相应的最优控制模式。在此基础上，设计了一种快速求解算法。通过实例研究验证了我们的理论结果，并证明了所提出的算法在保证节能效果的同时，与全局最优算法（Rao et al., 2023a）相比，计算速度显著提高（超过99%）。

引用次数: 0

Prediction of the estimated times of arrival of freight train based on operational and geospatial features

IF 2.6 Q3 TRANSPORTATION

Journal of Rail Transport Planning & Management

Pub Date : 2025-02-20 DOI: 10.1016/j.jrtpm.2025.100508

Masoud Yaghini, Amirhosein Ezati

In many railway systems, freight train schedules are often adjusted based on passenger train timetables at the operational level. Predicting the estimated time of arrival (ETA) for freight trains is a challenging task due to the high variability in transit times. This study introduces ETA prediction models developed using two years of operational data combined with geospatial features for freight trains operating within a sub-network of the Iranian railway. Prediction models for all origin-destination pairs in each direction (north-to-south and south-to-north) were created, predicting ETAs at three distinct locations along the routes. Four machine learning algorithms were evaluated, and the most accurate model was determined through comparisons with a baseline statistical model. The random forest algorithm demonstrated superior performance among the models at most locations. The performance improvements of the best prediction models with and without geospatial features were also investigated. Models incorporating geospatial features showed notably higher accuracy than those relying solely on non-geospatial predictors. These improvements were particularly more evident in the south-to-north direction and at locations closer to the destination. The results of this research offer practical insights for logistics centers, enabling optimized loading, unloading, and resource allocation strategies, thereby enhancing the efficiency of freight railway operations.

{"title":"Prediction of the estimated times of arrival of freight train based on operational and geospatial features","authors":"Masoud Yaghini, Amirhosein Ezati","doi":"10.1016/j.jrtpm.2025.100508","DOIUrl":"10.1016/j.jrtpm.2025.100508","url":null,"abstract":"<div><div>In many railway systems, freight train schedules are often adjusted based on passenger train timetables at the operational level. Predicting the estimated time of arrival (ETA) for freight trains is a challenging task due to the high variability in transit times. This study introduces ETA prediction models developed using two years of operational data combined with geospatial features for freight trains operating within a sub-network of the Iranian railway. Prediction models for all origin-destination pairs in each direction (north-to-south and south-to-north) were created, predicting ETAs at three distinct locations along the routes. Four machine learning algorithms were evaluated, and the most accurate model was determined through comparisons with a baseline statistical model. The random forest algorithm demonstrated superior performance among the models at most locations. The performance improvements of the best prediction models with and without geospatial features were also investigated. Models incorporating geospatial features showed notably higher accuracy than those relying solely on non-geospatial predictors. These improvements were particularly more evident in the south-to-north direction and at locations closer to the destination. The results of this research offer practical insights for logistics centers, enabling optimized loading, unloading, and resource allocation strategies, thereby enhancing the efficiency of freight railway operations.</div></div>","PeriodicalId":51821,"journal":{"name":"Journal of Rail Transport Planning & Management","volume":"34 ","pages":"Article 100508"},"PeriodicalIF":2.6,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143444613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A new approach to identify critical causal factors and evaluate intervention strategies for mitigating major railway occurrences in Taiwan

IF 2.6 Q3 TRANSPORTATION

Journal of Rail Transport Planning & Management

Pub Date : 2025-02-14 DOI: 10.1016/j.jrtpm.2025.100507

Yannian Lee

Following two consecutive catastrophic railway accidents in Taiwan, public safety concern has been raised in railway transportation services. To improve train operation safety, this study integrates the Human Factors Analysis and Classification System (HFACS), Fuzzy Logic Modeling (FLM) method, and Human Factors Intervention Matrix (HFIX) to develop a safety assessment framework. Twenty eight major railway occurrence investigation reports published by the Taiwan Transportation Safety Board are collected for data extraction. Using the HFACS, causal factors causing major railway occurrences are first classified, followed by critical causal factors identification through FLM method. The HFIX is applied to categorized safety recommendations which were issued based on the identified causal factors of occurrence investigations and pair the results with critical causal factors for accident rate evaluations and effectiveness assessment. The evaluations reveal that the statistical accident rate in 2023 was higher than the predicted accident rate. The results also reveal that mitigating the frequency of identified causal factors is more efficient for occurrences reduction than through safety recommendations enforcement. Therefore, decision makers can determine the best intervention strategies based on available resources and develop relevant countermeasures for implementation.

{"title":"A new approach to identify critical causal factors and evaluate intervention strategies for mitigating major railway occurrences in Taiwan","authors":"Yannian Lee","doi":"10.1016/j.jrtpm.2025.100507","DOIUrl":"10.1016/j.jrtpm.2025.100507","url":null,"abstract":"<div><div>Following two consecutive catastrophic railway accidents in Taiwan, public safety concern has been raised in railway transportation services. To improve train operation safety, this study integrates the Human Factors Analysis and Classification System (HFACS), Fuzzy Logic Modeling (FLM) method, and Human Factors Intervention Matrix (HFIX) to develop a safety assessment framework. Twenty eight major railway occurrence investigation reports published by the Taiwan Transportation Safety Board are collected for data extraction. Using the HFACS, causal factors causing major railway occurrences are first classified, followed by critical causal factors identification through FLM method. The HFIX is applied to categorized safety recommendations which were issued based on the identified causal factors of occurrence investigations and pair the results with critical causal factors for accident rate evaluations and effectiveness assessment. The evaluations reveal that the statistical accident rate in 2023 was higher than the predicted accident rate. The results also reveal that mitigating the frequency of identified causal factors is more efficient for occurrences reduction than through safety recommendations enforcement. Therefore, decision makers can determine the best intervention strategies based on available resources and develop relevant countermeasures for implementation.</div></div>","PeriodicalId":51821,"journal":{"name":"Journal of Rail Transport Planning & Management","volume":"33 ","pages":"Article 100507"},"PeriodicalIF":2.6,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Capacity evaluation of ERTMS/ETCS HTD and moving block

IF 2.6 Q3 TRANSPORTATION

Journal of Rail Transport Planning & Management

Pub Date : 2025-02-03 DOI: 10.1016/j.jrtpm.2025.100506

Daniel Knutsen , Nils O.E. Olsson , Jiali Fu , Tomas Rosberg

This paper compares the capacity effect of different implementations of ERTMS/ETCS (European Rail Traffic Management System/The European Train Control System): Hybrid Train Detection (HTD) and moving block. This is done both on a conceptual level by looking at a scenario involving two trains, and for a simulated network. The effects are studied by modelling HTD and moving block in the simulation tool RailSys, looking at the performance indicators related to capacity: headway, capacity utilisation, and punctuality. The model uses existing infrastructure and a complete timetable. The results of the scenario with two trains show that similar results on headway can be achieved with HTD compared to moving block This is true even with relatively long virtual blocks of 500 m. The results from the simulated network show that various shares of trains with train integrity, as well as moving block, have a minor effect on the performance indicator punctuality. Moving block gives some improvements on capacity utilisation compared to HTD. However, by implementing shorter virtual blocks at sections of lower speed, it is possible to achieve results on capacity utilisation like that engendered by moving block.

{"title":"Capacity evaluation of ERTMS/ETCS HTD and moving block","authors":"Daniel Knutsen , Nils O.E. Olsson , Jiali Fu , Tomas Rosberg","doi":"10.1016/j.jrtpm.2025.100506","DOIUrl":"10.1016/j.jrtpm.2025.100506","url":null,"abstract":"<div><div>This paper compares the capacity effect of different implementations of ERTMS/ETCS (European Rail Traffic Management System/The European Train Control System): Hybrid Train Detection (HTD) and moving block. This is done both on a conceptual level by looking at a scenario involving two trains, and for a simulated network. The effects are studied by modelling HTD and moving block in the simulation tool RailSys, looking at the performance indicators related to capacity: headway, capacity utilisation, and punctuality. The model uses existing infrastructure and a complete timetable. The results of the scenario with two trains show that similar results on headway can be achieved with HTD compared to moving block This is true even with relatively long virtual blocks of 500 m. The results from the simulated network show that various shares of trains with train integrity, as well as moving block, have a minor effect on the performance indicator punctuality. Moving block gives some improvements on capacity utilisation compared to HTD. However, by implementing shorter virtual blocks at sections of lower speed, it is possible to achieve results on capacity utilisation like that engendered by moving block.</div></div>","PeriodicalId":51821,"journal":{"name":"Journal of Rail Transport Planning & Management","volume":"33 ","pages":"Article 100506"},"PeriodicalIF":2.6,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using Time Signal at Red (TSAR) as a tool for analysing rail network performance

IF 2.6 Q3 TRANSPORTATION

Journal of Rail Transport Planning & Management

Pub Date : 2025-01-31 DOI: 10.1016/j.jrtpm.2025.100505

Anirban Bhattacharyya , Matthew Forshaw , David Golightly , Seb Merricks , Roberto Palacin , Ken Pierce , Pedro Pinto da Silva

Reactionary delays can adversely impact train service performance. This is particularly true for parts of the rail network at or near capacity. To detect the causes of such delays, a metric with a granularity smaller than those of typical rail delay metrics is required. We present an approach based on the Time Signal at Red (TSAR) metric. The purpose of TSAR is to measure the duration a berth is continuously occupied by a train or reserved, which is closely related to information regarding the red aspect of the berth signal at an entrance to the berth. Thus, TSAR provides a low-level metric to measure individual service and berth performance, and to observe system effects that reflect reactionary delay. The paper defines TSAR and describes a data processing methodology to extract TSAR and signal aspect on berth entry from disparate data sources. The use of TSAR is demonstrated for a case study area – comparing different service patterns, identifying patterns of reactionary delay, and showing the impact of adhesion at different times of year. The implications of TSAR are discussed, including its utility for applications such as analysis of simulated network performance.

{"title":"Using Time Signal at Red (TSAR) as a tool for analysing rail network performance","authors":"Anirban Bhattacharyya , Matthew Forshaw , David Golightly , Seb Merricks , Roberto Palacin , Ken Pierce , Pedro Pinto da Silva","doi":"10.1016/j.jrtpm.2025.100505","DOIUrl":"10.1016/j.jrtpm.2025.100505","url":null,"abstract":"<div><div>Reactionary delays can adversely impact train service performance. This is particularly true for parts of the rail network at or near capacity. To detect the causes of such delays, a metric with a granularity smaller than those of typical rail delay metrics is required. We present an approach based on the Time Signal at Red (TSAR) metric. The purpose of TSAR is to measure the duration a berth is continuously occupied by a train or reserved, which is closely related to information regarding the red aspect of the berth signal at an entrance to the berth. Thus, TSAR provides a low-level metric to measure individual service and berth performance, and to observe system effects that reflect reactionary delay. The paper defines TSAR and describes a data processing methodology to extract TSAR and signal aspect on berth entry from disparate data sources. The use of TSAR is demonstrated for a case study area – comparing different service patterns, identifying patterns of reactionary delay, and showing the impact of adhesion at different times of year. The implications of TSAR are discussed, including its utility for applications such as analysis of simulated network performance.</div></div>","PeriodicalId":51821,"journal":{"name":"Journal of Rail Transport Planning & Management","volume":"33 ","pages":"Article 100505"},"PeriodicalIF":2.6,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Journal of Rail Transport Planning & Management

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀