Railway Management Research Articles

Travelers’ Propensity to Use Intercity Railway Services in Emerging Economies: Significance of Passengers’ Satisfaction and Communication Technologies

This paper focuses on the perspectives of passengers who were railway users and how railways as a service can be uplifted with technological advancements through the introduction of information and communication technologies (ICTs). For this purpose, a questionnaire was designed comprised of six sections related to information on socio-economic-demographics, travel, station facilities, train facilities, customer care, and familiarity with and benefits of ICTs. A total of 800 respondents were recruited on trains and in railway stations to collect data through a random sampling technique. Data were analyzed through descriptive statistics, factor analysis, bivariate correlation analysis, and ordered logistic regression analysis. The three hypotheses tested showed that (i) there is a correlation between socio-demographic factors, train frequency, and satisfaction levels, (ii) satisfaction with station and train facilities and customer care impacts users’ travel likelihood with the train service, and (iii) users’ familiarity with perceived benefits of ICTs influences passengers’ travel likelihood with the train service. The results indicate that the users’ satisfaction with attributes of station facilities, train facilities, and customer care and perceptions about ICTs significantly influences their travel frequency with the train service. This study is useful for multiple stakeholders, especially for railway management authorities, to provide inclusive services to passengers and to plan for future transportation, which should be well-equipped with ICTs, well-integrated with other transport modes, and well-connected with optimum stops.

Application of vibration signals in railway track diagnostics using a mobile railway platform

The article presents a comprehensive method for using vibration signals to diagnose railway tracks. The primary objective is to gather detailed information on track conditions through a passive experiment. This involves using mobile diagnostic tools and techniques to assess railway infrastructure. The article elaborates on the range of diagnostic activities conducted in accordance with detailed railway regulations and highlights the benefits and capabilities of mobile diagnostics in railway transport. The research includes mobile field measurements across the general railway manager’s network, employing vibration signals to detect and evaluate track conditions. The methodology section provides a thorough description of the mobile measurement rail platform, detailing the equipment used, the routes taken for measurements, and the processes of data acquisition and processing. The data obtained from these measurements is crucial for understanding the actual technical condition of the railway tracks. The method of obtaining and processing data is explained in relation to the real technical condition of the railway track. This involves using transducers with specific parameters and parametrically defined signal recording, along with dedicated analysis techniques in post-processing. Vibration signals serve as the primary carrier of information in this diagnostic method. The article details the step-by-step procedures for collecting and analyzing these signals to provide accurate assessments of track conditions. Based on the results from the mobile measurement rail platform, the article characterizes various areas of diagnostics where vibration signals are particularly effective for technical evaluation. These areas include identifying track defects, monitoring track surface and railway crossing and assessing the overall structural health of the railway infrastructure. The use of vibration signals offers a non-invasive and efficient means of track diagnostics, providing real-time data for maintenance and repair decisions. In conclusion, the article underscores the significance of mobile diagnostics in enhancing the safety and reliability of railway transport. By leveraging vibration signals and advanced data processing techniques, this method provides a framework for continuous monitoring and assessment of railway track conditions, ultimately contributing to improved maintenance strategies and operational efficiency.

Innovative Approaches in Railway Management: Leveraging Big Data and Artificial Intelligence for Predictive Maintenance of Track Geometry

Innovative Approaches in Railway Management: Leveraging Big Data and Artificial Intelligence for Predictive Maintenance of Track Geometry

Thermal imaging analysis of ballast fouling: Investigating the effects of parent rock and fouling materials through IRT passive camera

This study explores the use of infrared thermography (IRT) technology for the non-destructive evaluation of ballast fouling in railway tracks, focusing on the influence of parent rock types and fouling materials. Utilizing thermal imaging, the research investigates how variations in ballast conditions affect surface temperature, which serves as an indicator of structural integrity and health. The experimental setup involved ballast samples derived from three different rock types—basalt, limestone, and andesite—fouled with commonly encountered materials like sand and clay at varying percentages. Results demonstrate that fouling level and type significantly influence the thermal signatures captured by IRT passive camera. Notably, ballast derived from darker rocks exhibited higher temperatures, indicating greater emissivity, while fouled ballast showed distinct temperature patterns compared to clean samples, emphasizing the potential of thermal imaging in detecting and quantifying fouling in ballast layers. This research underscores the viability of IRT passive camera in the routine maintenance and monitoring of railway infrastructure, providing a foundation for further development of integrated diagnostic tools for railway management systems.

Predictive-Cognitive Maintenance for Advanced Integrated railway Management

Railway systems play a vital role in modern transportation and Predictive-Cognitive Maintenance (PCM) has emerged as a transformative approach in the context of Advanced Integrated Railway Management for ensuring the safety, reliability, and efficiency of these systems. PCM leverages data analytics and machine learning to optimize railway system maintenance. This requires effective structural health monitoring (SHM) using low-cost sensor devices. This paper presents a prototype solar-powered wireless sensor node with a 3-axis MEMS accelerometer and energy-harvesting features for monitoring rail track vibrations. The node contains a microcontroller that runs embedded machine-learning models to preprocess the vibration data after train crossing. Abnormal vibrations, indicative of defects, were detected in real time using the TinyML inference at the edge. Instead of raw data, only the model results were wirelessly transmitted to a digital twin in the cloud. The digital twin aggregates data across the rail network for system-level assessment of the RUL and maintenance planning. This edge computing approach minimizes wireless transmission and cloud storage compared to raw sensor streaming. Embedded ML enables real-time damage detection, whereas the cloud digital twin enables system-level prognosis insights. The solar-powered platform enables long-term remote monitoring at low cost without wiring or battery changes. A full-scale physical model was used to validate the edge node prototypes against calculation models and wired accelerometers for impulse loads. The results demonstrate that these nodes can provide a sensor layer for cost-effective PCM in railway systems. In summary, this work proposes an edge computing and embedded ML approach for SHM that integrates cloud-based digital twins to enable the predictive-cognitive maintenance of railway infrastructure. Wireless nodes demonstrate potential for low-cost, convenient, and automated rail health monitoring.

Enhancing Performance of Public-Private Partnership Projects: Data Management and Capacity Building of Standard Gauge Railway

This study assessed the impact of data management and capacity building on the performance of the Standard Gauge Railway (SGR) project in Kenya, specifically within the context of monitoring and evaluation (M&E) practices in public-private partnership (PPP) frameworks. Anchored in the tenets of the Principal-Agent Theory, Institutional Theory, and Transaction Cost Economics Theory, this research employs a survey-based approach, drawing insights from 55 SGR project employees. In exploring the influence of data management, the findings illuminate its significant correlation with the orchestration and advancement of the SGR project. Additionally, the study underscores the positive repercussions of capacity-building initiatives on enhancing the project’s performance metrics. Notably, participants exhibited a consensus regarding the efficacy of these programs in augmenting the skills and knowledge base of the project team and stakeholders. The study accentuates the imperative for a comprehensive review aimed at fortifying the accuracy and security of project data. It recommends a strategic emphasis on initiatives enhancing data reliability and security within the SGR project’s framework. These insights contribute crucial perspectives on the pivotal roles of data management and capacity building, specifically underscoring their influence on the SGR project in Kenya.

Construction management and technical innovation of the Beijing-Shanghai High Speed Railway

Construction management and technical innovation of the Beijing-Shanghai High Speed Railway

Applicability of an Hertzian-type contact model for wheel-rail pairings as seen by an improved post-processing scheme for ultrasonic data

The contact between wheel and rail crucially affects the management of railways, since the vehicle dynamics, safety, and performance, for example, are all dependent on this. Therefore, in this contribution the load-dependence of nominal contact area – a broadly not considered aspect of the wheel-rail contacts within the literature – is addressed. We are applying an ultrasonic technique for the detection of the contact zone and an improved post-processing scheme for the measured ultrasonic reflection data. Accordingly, we found that the nominal contact area shows a power-law dependence on load which only on average is predicted by the elliptical Hertzian contact theory.

Running Safety Assessment of a High-Speed Train on Bridges During Braking Under Near-Fault Ground Motions

This study aims to investigate the adverse effects of near-fault ground motions on the long-term development of high-speed railways. In this paper, the ground motions are analyzed based on the Train–Track–Bridge Coupling Braking System (TTBCBS) which has been validated during earthquakes. The effects of earthquakes on the whole system are discussed in depth, focusing on the random nature of earthquakes, the impulsive character of near-fault earthquakes and the effects of different initial speeds on the system behavior. The results show that under random earthquakes, the probability of train derailment gradually increases with the increase of peak ground acceleration (PGA) of earthquakes. When the PGA exceeds 0.2[Formula: see text]g, the train is highly susceptible to derailment and the bridge itself may incur damage. When analyzing the nature of pulses of near-fault ground motions, it was found that the responses induced by class B and class C pulses are significantly similar. Meanwhile, the calculated values of derailment coefficients are basically the same when the PGA of class A pulse wave is at 0[Formula: see text]g and 0.1[Formula: see text]g. This suggests that train braking somewhat mitigates the response induced by near-fault ground motions. Furthermore, the value of improved spectral intensity (SI) for the running safety during earthquakes indicates that the increase in seismic intensity is detrimental to the system. In terms of the effect of train initial speeds on the system during earthquakes, it is observed that the system response reaches the lowest point when the train speed is 250[Formula: see text]km/h, which is more favorable to the smooth deceleration of the train. When the train speed is 400[Formula: see text]km/h, the system response reaches its maximum value. These research findings provide crucial insights and guidance for seismic safety design and management of high-speed railways.

Nonparametric efficiency analysis with unobserved inputs in multi-output settings

Transport service providers are increasingly interested in benchmarking. The digitized nature of railway management, which relies heavily on automation and intangible inputs, conflicts with standard assumptions in the benchmarking literature that maintain full knowledge of all input factors. This creates a potential for omitted variable bias during efficiency measurement. In addition, designing accurate improvement actions requires output-specific rather than overall efficiency scores. To address these concerns we compute output-specific cost efficiency scores for multi-output entities where a subset of inputs remain unobserved to the analyst. We apply the method to highly customized data on Belgian railway traffic control rooms. We document that the magnitude of the omitted variable bias differs across outputs. Moreover, the results of a metafrontier analysis suggest that there are substantial differences in production possibilities across control rooms.

Application of Degradation and Optimization Models for digitization of Maintenance Management in Railway Infrastructures

This study presents an innovative methodology to improve the maintenance of railway infrastructures, integrating degradation models and economic optimization. The combination of a degradation model and a semi-Markovian model supported by the Weibull function, calculates the economically optimal degradation, to release the preventive intervention. The development of digital twins of assets and infrastructures makes possible the integration of online data and the incorporation of models that help the maintenance manager in decision-making. Organizations must determine the information to be collected from the assets. Transform the information into inputs for the models and, after their application, would allow them to identify the most economically favorable global maintenance scenarios. This work evolves the decision support on preventive maintenance implementation in the railway sector. From a decision table, used by railway managers, with three levels of risk established subjectively on an experimental basis, it evolves to the objective methodology, designed to determine the optimal degradation. The methodology can be applied to assets by maintenance managers but can also be incorporated into the digital twins. The case study illustrates the practical application of this methodology to the degradation of railway track sections. The research emphasizes the importance of data-driven decision-making for economic maintenance management. However, other priorities such as transportation safety and efficiency could complement the economic one.

ECONOMIC BENEFITS OF ROUTE MONITORING: GPS TRACKING IN KAZAKHSTAN’S RAILWAYS

In the context of Kazakhstan's railway operations, this study explores the economic advantages derived from the implementation of GPS tracking technology focused on route monitoring. Employing Extreme Programming (XP) principles, a case study within a national railway company details the process, challenges, and outcomes of integrating GPS trackers on mobile phones for workforce location tracking and geo-fencing. Results demonstrate the significant improvement in monitoring workforce activities and precise payroll calculation based on actual working hours. Emphasizing the role of the XP methodology, the study showcases enhanced stakeholder communication, iterative development, and continuous system improvement. Technical aspects of the GPS tracking system, including architecture, data flow, and integration with existing railway management systems, are examined. Moreover, the study delves into encountered challenges such as privacy concerns, data security, and user acceptance during implementation. This case study presents valuable insights for transportation organizations aiming to implement location-tracking and geo-fencing technologies, providing a blueprint for effective project management, iterative development, and stakeholder engagement in pursuit of economic benefits through route monitoring.

Damage Identification for Railway Tracks Using Onboard Monitoring Systems in In-Service Vehicles and Data Science

Nowadays, railway track monitoring strategies are based on the use of railway inspection vehicles and wayside dynamic monitoring systems. The latter sometimes requires traffic disruption, as well as higher time and cost-consumption activities, and the use of dedicated inspection vehicles is less economical and efficient as the use of in-service vehicles. Furthermore, the use of non-automated algorithms faces challenges when it comes to early damage detection in railway infrastructure, considering operational, environmental, and big data aspects, and may lead to false alarms. To overcome these challenges, the application of artificial intelligence (AI) algorithms for early detection of track defects using accelerations, measured by dynamic monitoring systems in in-service railway vehicles is attracting the attention of railway managers. In this paper, an AI-based methodology based on axle box acceleration signals is applied for the early detection of distributed damage to track in terms of the longitudinal level and lateral alignment. The methodology relies on feature extraction using an autoregressive model, data normalization using principal component analysis, data fusion and feature discrimination using Mahalanobis distance and outlier analysis, considering eight onboard accelerometers. For the numerical simulations, 75 undamaged and 45 damaged track scenarios are considered. The alert limit state defined in the European Standard for assessing track geometry quality is also assumed as a threshold. It was found that the detection accuracy of the AI-based methodology for different sensor layouts and types of damage is greater than 94%, which is acceptable.

Imbalanced Anomaly Detection Using Augmented Deeper FCDDs for Wooden Sleeper Deterioration Prognostics

Maintaining high standards for user safety during daily railway operations is crucial for railway managers. To aid in this endeavor, top or side-view cameras and GPS positioning systems have facilitated progress toward automating periodic inspections of defective features and assessing the deteriorating status of railway components. However, collecting data on deteriorated status can be time-consuming and requires repeated data acquisition because of the extreme temporal occurrence imbalance. In supervised learning, thousands of paired data sets containing defective raw images and annotated labels are required. Concretely, the one-class classification approach offers the advantage of requiring quite a few anomalous images to optimize parameters for training large normal images. The deeper fully-convolutional data descriptions (FCDDs) were applicable to several damage data sets of concrete/steel components in structures, and fallen tree, and wooden building collapse in disasters. However, it is not yet known to feasible to railway components. In this study, we devised a prognostic discriminator pipeline to automate one class classification using the augmented deeper FCDDs for defective railway components. We also performed sensitivity analysis of the mixture and erasing augmentations, and the deeper backbone rather than the shallow baseline of convolutional neural network (CNN) with 27 layers. Furthermore, we visualized defective railway features by using transposed Gaussian upsampling. We demonstrated our application to railway inspection using a video acquisition dataset that contains wooden sleeper deterioration. Finally, we examined the usability of our approach for prognostic monitoring and fu ture work on railway component inspection.

Simulation-Based Headway Optimization for the Bangkok Airport Railway System under Uncertainty

The ever-increasing demand for intercity travel, as well as competition among all modes of transportation, is an unavoidable reality that today’s urban rail transit system must deal with. To meet this problem, urban railway companies must try to make better use of their existing plans and resources. Analytical approaches or simulation modeling can be used to develop or change a rail schedule to reflect the appropriate passenger demand. However, in the case of complex railway networks with several interlocking zones, analytical methods frequently have drawbacks. The goal of this article is to create a new simulation-based optimization model for the Bangkok railway system that takes into account the real assumptions and requirements in the railway system, such as uncertainty. The common particle swarm optimization (PSO) technique is combined with the developed simulation model to optimize the headways for each period in each day. Two different objective functions are incorporated into the models to consider both customer satisfaction by reducing the average waiting time and railway management satisfaction by reducing needed energy usage (e.g., reducing operating trains). The results obtained using a real dataset from the Bangkok railway system demonstrate that the simulation-based optimization approach for robust train service timetable scheduling, which incorporates both passenger waiting times and the number of operating trains as equally important objectives, successfully achieved an average waiting time of 11.02 min (with a standard deviation of 1.65 min) across all time intervals.

Indian Railways: enhancing northeast connectivity with Half-Y connection

Research methodology The case has been developed as an illustrative case study using primary data. The data and images used for developing the case have been collected from the Alipurduar Division of Indian Railways with suitable permissions. The specific newspaper studies used in developing the case content have been referenced as footnotes on the relevant pages of the case study document. Case overview/synopsis Train operations in Alipurduar Division of Northeast Frontier Railway zone of Indian Railways are difficult. The division provides the vital rail link between the northeast states and the rest of India. Railway lines in the Alipurduar Division pass through several forests with a significant elephant population. As an outcome of train–elephant conflicts, train operations on one of the railway lines operate under severe speed restrictions. Moreover, the region is known for heavy rainfall and thunderstorms for almost six months every year. For the Divisional Railway Manager (DRM), the protagonist of the case, this implied repeated congestion, traffic disruptions, inadvertent delays and lack of time for critical maintenance of the tracks. A solution was planned several years ago for creating an alternative path to de-congest the traffic; however, it ran into a roadblock due to land acquisition issues.With all these limitations, could a solution be found and implemented? How could it be executed?The case illustrates how a simple yet innovative solution was proposed by the DRM in 2015 and implemented in 2016.With this case, students will be able to understand the innovation process that is embedded within long-term infrastructure projects. The case will help students understand how innovation can take place even in the later stages of project implementation, and how simple and creative solutions can have a long-term impact. Complexity academic level The case can be used in graduate and executive education courses in General Management and in Public Policy Management. It can also be used in Doctoral-Level Programmes such as those taught to scholars pursuing Fellow Programme in Management. Since the case brings out elements of problem framing and critical thinking, the case can be used for courses in strategic management. Many professionals, particularly those working in large organizations dealing with large infrastructure projects, will identify with the DRM and the challenges faced by him.

A BIM/GIS DIGITALIZATION PROCESS TO EXPLORE THE POTENTIAL OF DISUSED RAILWAYS IN ITALY

Abstract. Digitization processes, i.e., the practice of converting physical assets into digital assets, are gradually transforming the Architecture, Engineering, Construction and Owner Operators (AECOO) industry, and it concerns new and existing buildings and infrastructures. This study is about the management of disused railways and aims to present a BIM (Building Information Modelling) / GIS (Geographic Information Systems) workflow to identify and model disused railways buildings. The workflow was tested on the ‘Potenza Inferiore Scalo’ – ‘Laurenzara’ disused railway located in the South of Italy. The objective is the creation of a comprehensive digital database to help decision-makers give new utility to these existing infrastructures and buildings. And this is because the railway heritage can represent an essential factor in sustainable development processes and landscape regeneration.

A Review on the Prospects of Mobile Manipulators for Smart Maintenance of Railway Track

Inspection and repair interventions play vital roles in the asset management of railways. Autonomous mobile manipulators possess considerable potential to replace humans in many hazardous railway track maintenance tasks with high efficiency. This paper investigates the prospects of the use of mobile manipulators in track maintenance tasks. The current state of railway track inspection and repair technologies is initially reviewed, revealing that very few mobile manipulators are in the railways. Of note, the technologies are analytically scrutinized to ascertain advantages, unique capabilities, and potential use in the deployment of mobile manipulators for inspection and repair tasks across various industries. Most mobile manipulators in maintenance use ground robots, while other applications use aerial, underwater, or space robots. Power transmission lines, the nuclear industry, and space are the most extensive application areas. Clearly, the railways infrastructure managers can benefit from the adaptation of best practices from these diversified designs and their broad deployment, leading to enhanced human safety and optimized asset digitalization. A case study is presented to show the potential use of mobile manipulators in railway track maintenance tasks. Moreover, the benefits of the mobile manipulator are discussed based on previous research. Finally, challenges and requirements are reviewed to provide insights into future research.

Higashi ajia no naka no mantetsu (The South Manchuria Railway in East Asia)

In the past few years, Lim Chaisung has published major works on the history of railways. In this ambitious book, he takes on explaining the railway management of the South Manchuria Railway (SMR), the sprawling enterprise at the centre of East Asian railway history. However, Lim’s book is not simply an SMR case study. He also explains SMR’s involvement in the process of turning the Empire of Japan into a distinctive railway empire. As a result, this book is spurring discussions among scholars of imperial history and colonial economic history, as well as among railway historians. The following paragraphs will introduce the book’s chapters, along with a little commentary. The book’s preface, ‘Railway empire and the SMR’, begins with a discussion of the framing of prewar Japan as a railway empire. It also recounts how SMR has been portrayed as emblematic of Japan’s absorption of Western technology and as an intermediary in transferring technology to its colonies. SMR overcame its technological disadvantage against its European counterparts and is credited with exceeding the transport capacity of Japan’s domestic railways. While noting the value of viewing SMR as a case study of Japanese settler colonialism and colonial management, Lim explains how the history of SMR is important to the history of the metropole as well.

WAYS OF OPTIMIZING TIME INDICATORS WHEN PLANNING THE TRANSPORTATION OF MILITARY UNITS BY RAIL TRANSPORT

The purpose of the work is to conduct an analysis of the organization of the continuity of military transportation in the conditions of the conduct of hostilities by the Armed Forces of Ukraine. Currently, the issue of readiness to move military units (units) in a short period of time, without violating their integrity, with the provision of the possibility of a sudden transition to the performance of assigned tasks, which is an integral component of military operations (combat operations), is important. Methods. The issue of optimizing time indicators when planning the transportation of military units by rail transport due to the clarification of time spent on loading and unloading operations. The results. The success of military transportation depends primarily on the quality of their planning, the organization of close cooperation between the military administration bodies and the administration bodies on the types of transport. Interaction with the management bodies on the types of transport is carried out on the issues of ensuring and regulating military transports, coordination of technical capabilities and control of preparations for carrying out transports. The Department of Military Communications on Railways, together with the management of railways, carries out timely provision of rolling stock transportation, continuous management of military transportation, measures are taken to accelerate the advancement of echelons in the area of responsibility, and control the saving of budget funds during the execution of military transportation. Practical significance. Today, the issue of creating groups of troops (forces) of the Armed Forces of Ukraine, rapid regrouping of troops and expansion of existing groups, ensuring high mobility and maneuverability of troops, and carrying out military transports within the established deadlines is an urgent issue.