Journal of Rail Transport Planning & Management最新文献

Weekly crew scheduling for freight rail engineers: A network approach

IF 2.6

Journal of Rail Transport Planning & Management Pub Date : 2025-04-02 DOI: 10.1016/j.jrtpm.2025.100519

Jinhua Lyu, Jonathan F. Bard

{"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

IF 2.6

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

引用次数: 0

Identifying subway commuters travel patterns using traffic smart card data: A topic model

IF 2.6

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

{"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

IF 2.6

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

引用次数: 0

Predicting primary delay of train services using graph-embedding based machine learning

IF 2.6

Journal of Rail Transport Planning & Management Pub Date : 2025-03-06 DOI: 10.1016/j.jrtpm.2025.100518

Ruifan Tang, Ronghui Liu, Zhiyuan Lin

{"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

IF 2.6

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

引用次数: 0

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

IF 2.6

Journal of Rail Transport Planning & Management Pub Date : 2025-02-20 DOI: 10.1016/j.jrtpm.2025.100508

Masoud Yaghini, Amirhosein Ezati

{"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

Journal of Rail Transport Planning & Management Pub Date : 2025-02-14 DOI: 10.1016/j.jrtpm.2025.100507

Yannian Lee

{"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

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

{"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

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

{"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