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Energy saving has become a key concern of metro systems to reduce operating costs and meet the requirements of sustainable development. Energy-efficient timetabling, an effective way to reduce traction energy consumption, has gotten much attention in recent decades. However, limited research about energy-efficient timetabling simultaneously considered realistic speed profiles and the influence of train loads, which change greatly in different sections and significantly affect energy consumption. Therefore, this paper proposes an energy-efficient timetabling approach to improve regenerative energy utilization based on varying train loads and realistic speed profiles for bidirectional metro lines. First, based on the operational and safety constraints, this paper constructs an integer energy-efficient timetabling model where dwell times and nonuniform headways are taken as decision variables. Besides, the turnaround process is also considered to ensure rolling stock circulation efficiency. Sectional passenger volumes are employed to determine the varying train loads. Energy consumption is calculated based on realistic speed profiles. Owing to the complexity of the model, an improved adaptive large neighborhood search (ALNS) algorithm is designed to solve this model according to its characteristics. Finally, numerical examples are conducted based on the practical data from Shanghai Metro Line 17. The results indicate that the proposed approach effectively solves the energy-efficient timetabling problem with varying train loads and realistic speed profiles for bi-directional metro lines. The approach is suitable for off peak hours and can reduce energy consumption effectively, and the reduction is up to 11.9% for the presented cases. The improved ALNS algorithm can find good solutions within reasonable times in different cases. Moreover, compared with the widely used genetic algorithm in the literature, the improved ALNS algorithm is more efficient in solving the proposed energy-efficient timetabling problem in different cases. [ABSTRACT FROM AUTHOR]

Driven by the concept of mass customization, manufacturing enterprises are now required to transform their traditional workshops into smart workshops. The integration of cyber-physical systems (CPS), which serves as the core of Industry 4.0, is crucial in achieving smart manufacturing. By implementing CPS, the conventional workshop can transition into a new paradigm characterized by intelligence and flexibility. However, the implementation of CPS in workshops is a complex undertaking that is still in its early stages. Therefore, this research paper aims to provide a comprehensive perspective on CPS-based workshops, with the intention of facilitating their implementation in the industry. Initially, the paper identifies seven key features of CPS-based workshops, namely self-sensing, self-awareness, self-assessment, self-optimization, self-adjustment, self-configuration, and self-control. Subsequently, the paper proposes the architectural framework of CPS-based workshops from a technical standpoint. Furthermore, a conceptual model of CPS-based workshops is developed, which highlights the three fundamental elements and closed-loop mechanism of such workshops. Finally, a case study of a Rolling stock manufacturing company in Indonesia is presented to demonstrate the feasibility of implementing CPS-based workshops in the industry. [ABSTRACT FROM AUTHOR]

The railway system is a complex technical system-of-systems (SoS). To address the complexity of the railway system, a holistic approach is needed that facilitates the development of an appropriate asset management regime. A systems-of-systems (SoS) approach considers the complex nature of the railway system, comprising interconnected subsystems like rolling stock and infrastructure. Neglecting these interdependencies risks sub-optimization of the overall system performance. Asset management of the railway system utilising a SoS approach ensures the focus of asset management on overall system requirements. The efficiency and effectiveness of the railway system is based on aspects such as availability, reliability, and safety performance. To enhance these aspects, monitoring, and improvement of key performance indicators (KPIs) emphasizing increased capacity and reduced operational costs is essential. The KPIs offer quantifiable parameters for performance optimization. Augmenting asset management through data-driven technologies can improve the efficiency and effectiveness of asset management. However, challenges persist in the implementation of data-driven solutions due to the railway system's complexity and lack of a holistic perspective. A systematic performance-driven framework with a system-of-systems approach for augmented asset management of railway system provides handrail for the utilisation of data-driven technologies with railway system requirements at the centre while developing an asset management regime. The proposed framework aims to establish a clear relationship between system KPIs, and the performance of sub-systems and components aiding railway organizations in asset management design and implementation. This paper explains the important components of the proposed framework and demonstrates the application the framework for asset management and maintenance planning of high value components in the fleet of railway rolling stock. Adoption of the proposed framework is expected to enhance asset management through development and implementation of data-driven solutions that are aligned with system KPIs, to support asset management decision making. [ABSTRACT FROM AUTHOR]

The paper deals with the problem of obtaining a given dynamic behavior of simply supported bridge beams from the impact of rolling stock. Resonant vibrations of beams become a reality at high speeds if the train is formed of identical cars, which is typical for passenger trains. The phenomenon of suppression of beam vibrations by a train, known from publications, is possible only with an exact ratio of the length of the car and the beam. This significantly reduces the possibilities of choosing the necessary spans of beams. Consideration of issues of interaction in the "bridge–track—train" system for security purposes requires the involvement of a rather complex mathematical apparatus and appropriate software. The paper proposes a new method for determining the specified dynamic behavior of bridge beams, suitable for any spans and available to engineers at the stage of pre-design assignment of dynamic parameters of beams. The requirements that are necessary and sufficient conditions for preventing the resonance of bridge beams on the HSR are determined. At the same time, it does not require the involvement of software based on a complex mathematical technique. [ABSTRACT FROM AUTHOR]

With the expansion of the metropolitan area, the application of express/local mode is gradually increasing. In contrast to the normal mode, the express/local mode has advantages in reducing energy consumption and saving total travel time by having express trains skipping some stops. This paper aims to minimize the total energy consumption of express and local train throughout the day by optimizing the train operation strategy in the same power supply section and increasing the overlap time between train traction acceleration and train regenerative braking to obtain the optimal energy‐efficient timetable. As the consumed energy of a train is highly dependent on the rolling stock weight and the on‐board passengers' weight. An integer programming model is proposed with on‐board passengers considered accurately, in which the dwell times, departure headway, and total turnaround time of express and local trains are determined. An improved grey wolf algorithm is designed by improving convergence factor and incorporating differential evolution to solve the proposed problem. The real data on Guangzhou Metro Line 18 is adopted for numerical studies. The results show that the optimized timetable increases the regenerative energy utilization rate by 21.37% and reduces the total energy consumption by 5.02% compared to the operational timetable. This paper aims to minimize the total energy consumption of express and local train throughout the day by optimizing the train operation strategy in the same power supply section and increasing the overlap time between train traction acceleration and train regenerative braking to obtain the optimal energy‐efficient timetable. An integer programming model is proposed with on‐board passengers considered accurately, in which the dwell times, departure headway, and total turnaround time of express and local trains are determined. [ABSTRACT FROM AUTHOR]

The railway industry forecasts growth in passenger and freight traffic over the next 30 years. This places additional demands on rolling stock depot facilities, many of which were designed and built before the modern age of information technology. This paper explores the potential of improving the efficiency and effectiveness of rolling stock maintenance management to meet the challenges of the near future, by utilising advanced computing techniques. The objective of the work is to create optimised maintenance plans for a fleet of trains, considering optimal use of resources. As a "glue" for joining up functions and operations, a generic Depot and Vehicle ontology (called the Virtual Depot) is introduced. The ontology captures the structures, relationships, and attributes of objects in the Depot (rolling stock, sensors, depot assets, tools, resources, and staff). The ontology is populated with example company and fleet-specific knowledge using an automated knowledge acquisition method. This paper describes the systematic method for the creation of a Virtual Depot. Two particular aspects are discussed in detail—knowledge acquisition of fleet-specific information obtained from a manufacturer's Vehicle Maintenance Instruction manuals and the construction of a short-term scheduling process within the Virtual Depot. Our evaluation considers the integrative aspects of the method, demonstrating how the ontological structure and its acquired specific information informs and benefits the scheduling process, in particular with respect to schedule optimisation. Results from an initial case study show there is significant potential to optimise short-term maintenance schedules, and the ability to automatically consider resource availability in short-term scheduling is demonstrated. [ABSTRACT FROM AUTHOR]

This paper presents a deep learning approach for predicting rail corrugation based on on-board rolling-stock vertical acceleration and forward velocity measurements using One-Dimensional Convolutional Neural Networks (CNN-1D). The model's performance is examined in a 1:10 scale railway system at two different forward velocities. During both the training and test stages, the CNN-1D produced results with mean absolute percentage errors of less than 5% for both forward velocities, confirming its ability to reproduce the corrugation profile based on real-time acceleration and forward velocity measurements. Moreover, by using a Gradient-weighted Class Activation Mapping (Grad-CAM) technique, it is shown that the CNN-1D can distinguish various regions, including the transition from damaged to undamaged regions and one-sided or two-sided corrugated regions, while predicting corrugation. In summary, the results of this study reveal the potential of data-driven techniques such as CNN-1D in predicting rails' corrugation using online data from the dynamics of the rolling-stock, which can lead to more reliable and efficient maintenance and repair of railways. [ABSTRACT FROM AUTHOR]

ATC signal equipment is an important equipment for controlling rolling stock (subways). The on-board system is closely connected with the control circuit system of the vehicle, and there are many electrical interfaces and information interfaces between them. As the on-board ATC signal equipment and the vehicle belong to two systems, the joint debugging between the systems is aff ected by many factors such as time, site and so on. At present, there is no equipment available that can independently detect the internal digital signal functions of the boards and cards onboard the vehicle, and there is a lack of special tools to verify the functions of the boards and cards after medium and large-scale overhaul. There are safety risks during the dynamic debugging on the test line after static overhaul. This paper mainly describes a debugging device that replaces professional vehicle equipment. The device is connected with the female head of int connector. Under the static condition of the train, OMAP software is used to force and drive the internal digital signal of the board or card, so as to test whether the internal contact of the board or card works normally. The positions of faulty components can be accurately identified before dynamic train testing. This device improves the efficiency of dynamic train debugging and further improves the maintenance quality and standard. [ABSTRACT FROM AUTHOR]

Rolling stock manufacturers are finding innovative structural solutions to improve the quality and reliability of railway vehicles components. Structural optimization processes represent an effective strategy for reducing manufacturing costs, resulting in geometries easier to design and produce. In this framework, the present paper proposes a new methodology to design a railway metro bogie frame, combining structural–topological optimization methods and sensitivity analysis. In addition, manufacturing constraints were included to make the component design suitable for production through sand-casting. A robust sensitivity analysis has highlighted the most critical load conditions acting on the bogie frame. Its effectiveness was verified by carrying out two different structural optimizations based on different loadings. Two equivalent designs were obtained. Computational times were positively reduced by about 57%. The maximum value of stress was reduced about 23%. This new methodology has shown encouraging results to streamline the design process of this complex mechanical system, allowing researchers to also include manufacturing requirements. [ABSTRACT FROM AUTHOR]

Nowadays, the dynamic behavior of rolling stocks is an essential problem. In dynamic simulation, the correct wheel-rail contact modeling is an important task. In this paper, the stiffness of the wheel-rail contact zone is found by a new, direct, and simplified formulation. This subject is developed by applying more details, such as surface roughness based on the surface topography and hardening effects of a real contact problem. These characteristics have not been considered altogether in other similar works. In this work, the contact stiffness and frequency results are obtained for different material properties and verified with analytical and experimental research. It is shown that the presented new direct method with an acceptable accuracy is easier to use than the complicated existing methods. This paper discusses the effects of contact parameter variations on contact stiffness and frequency. For example, it is shown that the contact stiffness decreases by increasing the surface roughness and increases by increasing the strain-hardening factor. [ABSTRACT FROM AUTHOR]

Track deterioration models (TDMs) help to allocate maintenance work (direct costs) to vehicle runs. Furthermore, these models demonstrate the impact of rolling stock properties on infrastructure. This paper review provides an overview of the state of the art in railway track deterioration modelling and outlines the research potential in this domain. The main focus lies on ballast degradation, rail surface wear and fatigue, and their description in an empiric analytic wear formula. The basis for discussion is the wear formula of the Graz University of Technology. While the TDM demonstrates effectiveness, enhancements are sought, particularly with regard to adjusting the track parameters that vary across railway networks. Further exploration aims to refine the description of rail surface wear and rolling contact fatigue (RCF), incorporating factors such as traction energy and short-wave effects and adapting mathematical functions such as the t-Gamma function. This review underscores the need for ongoing research to develop TDMs that are both simple and detailed enough to encourage track-friendly rolling stock design. [ABSTRACT FROM AUTHOR]

The paper describes the existing technologies and challenges associated with galvanized metal production at the Novolipetsk Steel (NLMK) plant. Possible ways to improve the process using machine-learning tools are proposed. [ABSTRACT FROM AUTHOR]

The object of the research of the article is transport system of a city. The paper reviews the necessity to assess the greenhouse emissions in the city and proposes the methods for greenhouse emissions inventory of the urban transport system. The proposed approach is aligned with guidelines for the development of Sustainable Energy and Climate Action Plan (SECAP) of Mayor Covenants of European Union (EU). The methodologies outlined in the paper allow to estimate annual greenhouse emissions from transport sector. The SECAP defines transport sectors based on ownership and functioning as following: municipal transport fleet, public transport, and private and commercial transport. The paper proposes the methodology to estimate direct and indirect emissions in each of the described sector based on the information that is typicaly available to municipalities in Ukraine. The assessment is conducted on disaggregated level for different fuel types (diesel, petroleum, natural gas, biofuel, electricity, etc.) and separately for each fleet type (buses, trucks, passenger vehicles, specialized machinery). Total CO2 emissions are then estimated by multiplying the amount of fuel consumed by the emission factor for each fuel type and vehicle type. Information of fuel consumption is estimated based on annual milage and estimated based on available data and number of assumptions proposed in the paper. The proposed methodology for greenhouse emissions inventory for the transport sector allows to analyze available data, recommendations for data collection and a methodology for determining CO2 emissions from the operation of the transport system with sufficient accuracy of calculations. Based on these results, it is possible to forecast changes in energy consumption and emissions in the transport sector as a result of various interventions. For example, in Zhytomyr city the results of inventory were used to develop a set of measures, which include updating the rolling stock of electric transport; increasing the energy efficiency of the power grids of the transport system, developing cycling infrastructure. [ABSTRACT FROM AUTHOR]

Green railway transport (RT) is not always a completely green transport solution, as there are still many nonelectrified or partially electrified lines in operation with polluting engine traction. Although electric rail is the most environmentally friendly, electrification of lines is a time-consuming and costly project, especially on less busy lines where electrification would be inefficient and unprofitable. Even though electric railway is the most environmentally friendly solution, electrification of lines is a time-consuming and expensive project, especially on less frequented lines where electrification would be inefficient and unprofitable. Alternative propulsion (AP) in RT is not only a way to reduce the operational emissions of passenger trains, but also to save energy and fuel, save time due to vehicle changes, and reduce the overall carbon footprint from transporting goods and people. The aim of this paper is to examine in detail the current state of development and availability within the AP railway industry, specifically for passenger transport needs, and to transparently present the results in a simple and clear way. In addition, the paper explores the possibility of applying the alternatively powered system to the conditions of the H0 model railway and research with this model. [ABSTRACT FROM AUTHOR]

Railway operations are highly susceptible to delays and disruptions caused by various factors, such as technical issues, operational inefficiencies, and unforeseen events. To counter these delays and ensure efficient railway operations during real-time management, several rescheduling approaches can be implemented. Among these approaches, passenger-oriented rescheduling considers train rescheduling while taking passenger data into account, as opposed to operation-oriented rescheduling. This paper provides an overview of the former group of approaches. Particular focus is put on different ways passenger data is exploited to optimize rescheduling and on the measures, the approaches can decide on. The rescheduling measures typically considered vary from decisions on maintaining transfers, canceling trains, adding emergency trains, changing routes and orders of trains, skipping or adding stops at stations, short-turning trains, applying speed control, and modifying rolling stock compositions. In this regard, the paper presents a comprehensive analysis of real-time rescheduling approaches adopted in both the conventional railway and urban rail transit and points out possible directions for further research in the field. [ABSTRACT FROM AUTHOR]

Assessing the technical condition of a railway track throughout its service life is crucial to ensuring functional safety. However, it is important to note that current approaches have theoretical and practical gaps that require attention. The purpose of this study is to discuss the reconceptualization of an integrated approach to assess changes in the technical condition of railway tracks caused by rolling stock over their service life. Improving existing systems for assessing the condition of the track can be achieved by modifying the model concept and, subsequently, the measurement and assessment procedures. This paper presents an alternative method for addressing technical issues related to the monitoring and diagnosis of railway tracks, with a particular focus on natural phenomena: energy transfer by elastic waves. The primary aim of this work is to propose an approach for designing a 'closed' measurement system that enables tracking of the cause-and-effect relationship. This system takes into account both the elastic dissipative characteristics of the track–rolling stock interaction and the influence of the dynamics of various components in the 'train-track' system. [ABSTRACT FROM AUTHOR]

In the railway sector, rolling stock and infrastructure must be maintained in perfect condition to ensure reliable and safe operation for passengers. Climate change is affecting the urban and regional infrastructure through sea level rise, water accumulations, river flooding, and other increased-frequency extreme natural situations (heavy rains or snows) which pose a challenge to maintenance. In this paper, the use of artificial intelligence based on predictive maintenance implementation is proposed for the early detection of degraded conditions of a bridge due to extreme climatic conditions. For this prediction, continuous monitoring is proposed, with the aim of establishing alarm thresholds to detect dangerous situations, so restrictions could be determined to mitigate the risk. However, one of the main challenges for railway infrastructure managers nowadays is the high cost of monitoring large infrastructures. In this work, a methodology for monitoring railway infrastructures to define the optimal number of transductors that are economically viable and the thresholds according to which infrastructure managers can make decisions concerning traffic safety is proposed. The methodology consists of three phases that use the application of machine learning (Random Forest) and artificial cognitive systems (LSTM recurrent neural networks). [ABSTRACT FROM AUTHOR]

The analysis of the statistical values of wear of brake pads in operation, based on which a wear model was developed, is presented in this paper. This model can be used for predicting the remaining service life of brake pads according to the wagon mileage. The distribution function of wear on the upper and lower parts of the pad was determined, which made it possible to find the main qualitative characteristics, namely, the wear intensity, the γ-percentage wear, and the average pad wear at a specified wagon mileage. The results of the study can be factored for solving complex engineering problems of excessive wear of composite brake pads used for freight rolling stock; they could also help to extend the guaranteed overhaul period and improve the railway traffic safety. [ABSTRACT FROM AUTHOR]

Railway infrastructure is generally classified as either fixed or movable infrastructure assets. Failure in any of the assets could lead to the complete shutdown and disruption of the entire system, economic loss, inconvenience to passengers and the train operating company(s), and can sometimes result in death or injury in the event of the derailment of the rolling stock. Considering the importance of the railway infrastructure assets, it is only necessary to continuously explore their behavior, reliability, and safety. In this paper, a proactive multi-criteria decision-making model that is based on an interval-valued intuitionistic fuzzy set and some reliability quantitative parameters has been proposed for the evaluation of the reliability of the infrastructure assets. Results from the evaluation show that the failure mode 'Broken and defective rails' has the most risk and reliability concerns. Hence, priority should be given to the failure mode to avoid a total system collapse. [ABSTRACT FROM AUTHOR]

Generally, when optimizing a rolling stock schedule, the locations of the depots, or places in the network where the composition changes and maintenance occurs, are assumed known. The locations where maintenance is performed naturally influence the quality of any resulting rolling stock schedules. In this paper, the problem of selecting new depot locations and their corresponding capacities is considered. A two-stage mixed integer programming approach for rolling stock scheduling with maintenance requirements is extended to account for depot selection. First, a conventional flow-based model is solved, ignoring maintenance requirements, to obtain a variety of rolling stock schedules with multiple depot locations and capacity options. Then, a maintenance feasible rolling stock schedule can be obtained by solving a series of assignment problems by using the schedules found in the first stage. The proposed methodology is tested on real-life instances, and the numerical experiments of different operational scenarios are discussed. [ABSTRACT FROM AUTHOR]

The paper analyses the history of two-axle rail vehicles (rail buses) used for operation on regional lines in Czechoslovakia. It also shows trends in the development of modern light autonomous rolling stocks usable for passenger transport on unprofitable regional lines in Western Europe. The measures adopted in the transport policy of the Czech Republic for the period 2021-2027 for regional railway vehicles in the Czech Republic are presented. The final part of the paper presents the results of study of the concepts of a two-axle hybrid and electric rail buses and a conceptual design of a modular solution of a partially low-floor vehicle BEMU and HEMU for passenger regional rail transport. This paper shows that it is very problematic to create a light rail vehicle (LVR) that will be economical and environmentally friendly for regional transport and at the same time will meet the requirements of standards ČSN EN 12 663, category P2 and ČSN EN 15 227, category C I and will be friendly to road users. [ABSTRACT FROM AUTHOR]

This paper addresses timetable disruptions in metro systems and their impact on passenger service quality. To mitigate these effects, the study introduces a time-space network-based traffic management approach with a two-layer network. The first layer focuses on timetable rescheduling, using an integer program for modeling and a Lagrangian relaxation-based heuristic for solution. The second layer addresses rolling stock duty rescheduling, employing a greedy heuristic for train set duties. The proposed approach is applied to a real-world problem presented in the INFORMS railway application competition (2022). Results demonstrate that, with topological vertex orders, the approach achieves schedule amendments within 3–6 minutes for various disruption scenarios. This rapid response highlights the method's efficacy and advantage in real-time applications, showcasing its potential for practical implementation in metro systems. [ABSTRACT FROM AUTHOR]

Occupant impact safety is critical for train development. This paper proposes a systematic procedure for developing validated numerical occupant crash scenarios for high-speed trains by integrating experimental, computational, and inverse methods. As the train interior is the most potentially injury-causing factor, the material properties were acquired by mechanical tests, and constitutive models were calibrated using inverse methods. The validity of the seat material constitutive model was further verified via drop tower tests. Finite element (FE) and multibody (MB) models of train occupant-seat interactions in frontal impact were established in LS-DYNA and MADYMO software, respectively, using the experimentally acquired materials/mechanical characteristics. Three dummy sled crash tests with different folding table and backrest configurations were conducted to validate the numerical occupant-seat models and to further assess occupant injury in train collisions. The occupant impact responses between dummy tests and simulations were quantitatively compared using a correlation and analysis (CORA) objective rating method. Results indicated that the experimentally calibrated numerical seat-occupant models could effectively reproduce the occupant responses in bullet train collisions (CORA scores >80%). Compared with the train seat-occupant MB model, the FE model could simulate the head acceleration with slightly more acceptable fidelity, however, the FE model CORA scores were slightly less than for the MB models. The maximum head acceleration was 30 g but the maximum HIC score was 17.4. When opening the folding table, the occupant's chest injury was not obvious, but the neck-table contact and "chokehold" may potentially be severe and require further assessment. This study demonstrates the value of experimental data for occupant-seat model interactions in train collisions and provides practical help for train interior safety design and formulation of standards for rolling stock interior passive safety. [ABSTRACT FROM AUTHOR]

Copyright of Electric Drive for Locomotives is the property of Electric Drive for Locomotives Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper presents the motivations for the application of actively controlled elements in the running gear of a rail vehicle and the challenges that need to be addressed before deploying vehicles with actively controlled elements in real operation. Furthermore, the paper presents the design of an active element concept and its control for steering the axles of an urban rail vehicle to improve the vehicle's passage through small radius curves. The design takes into account the fundamental issues of cost and safety of the application of active elements, which are addressed in the presented control design by using data from autonomous driving subsystems such as the environmental database or the anti-collision system. [ABSTRACT FROM AUTHOR]

The issue of transport in underground hard coal mines is very rarely described in the literature. The financial aspects of this issue are even less often analyzed. Publications in this area focus on technical issues and the safety of mining crews. More attention is paid to transport in open-pit mines. The above premises and practical needs imply the need to conduct economic analyses of transport systems in underground hard coal mines. This paper is a scientific communication, which presents the concept of a multi-criteria cost analysis as a tool to support the selection of the optimal transportation option in an underground hard coal mine. Considerations in this area have not been carried out in the relevant literature, and the problem of selecting a transportation option is a complex and necessary issue in the practice of underground mines with extensive mine workings. The methodology presented includes five cost criteria (costs of carrying out the transportation task; route expansion costs; rolling stock maintenance costs; depreciation costs; and additional personnel costs). The simultaneous application of criteria relating to utility properties in addition to cost criteria makes it possible to adopt a specific technical and organizational model of the transportation system based on the indication of the optimal solution, resulting from the mathematical construction of functions of objectives relating to utility and cost. The optimal variant of the designed system and configuration of the material transportation system in underground workings takes into consideration the following: (1) seven utility criteria (KU1—transportation task completion time; KU2—compatibility of transportation systems; KU3—continuous connectivity; KU4—co-use with other transportation tasks; KU5—safety; KU6—inconvenience; KU7—operation under overplanning conditions) and (2) five cost criteria (KK1—costs of implementing the transportation task; KK2—costs of route expansion; KK3—rolling stock maintenance costs; KK4—depreciation costs; KK5—additional personnel costs). Based on the aforementioned criteria, two objective functions are built for each option: utility and cost. They present divergent goals; therefore, they are non-cooperative functions. Both utility and costs strive for the maximum. In the developed methodology, an ideal point is usually a fictitious solution representing a set of maximum values among all the achievable values in a set of solutions, but it is impossible to achieve this simultaneously based on all the criteria. This point illustrates the maximum utility and lowest cost among the alternatives considered, which is obviously impossible for any of the variants to meet at the same time, although it indicates the possibilities of the technique and the range of costs. For the developed method, a so-called "PND" nadir point is also determined, representing the least-preferred level of achievement of all goals simultaneously, determined from the set of optimal points in the Pareto sense. The originality of the conceptual considerations undertaken stems from: filling the gap in the economic methodology of complex transportation systems evaluation; embedding considerations in the trend concerning complex transportation systems of underground mines; and focusing considerations on the pre-investment phase, making it possible to optimize costs before expenditures are incurred. [ABSTRACT FROM AUTHOR]

Comfort on railway vehicles has been proven to depend on the number of passengers on the car, on their seating arrangement, as well as on the position in which it is assessed. The number of possible seating arrangement combinations is so large that comfort at a given location becomes a random variable even if passengers are considered deterministic vibration absorbers. In this paper we determine probability density functions (PDF) of comfort using a Monte Carlo scheme in which the number of passengers, their seating locations and their vibration characteristics (derived from passenger mass) are all random parameters with their own PDFs. Two types of distributions are obtained: the expected comfort of a single passenger and the compound comfort of all passengers on a given railway car. The carbody is represented by a beam model in which the first bending mode frequency has been allowed to vary so that the scope of results widens slightly. This variation is within the range of human sensitivity because this is the case in current lightweight construction, making carbody flexibility a key contributor to passenger comfort (or discomfort). Results confirm that comfort is indeed a stochastic variable with sizeable variance, and this prompts questions about the relation between the stochastic distribution and the tests prescribed in standard EN–12299. It will be shown that, surprisingly, comfort PDFs may be inferred with a high degree of accuracy (errors lower than 2%) from the tests in standard EN–12299. This is a most fortunate finding that radically transforms the interpretation of said standard, and allows rolling stock manufacturers and rail transport operators to have a much richer assessment of comfort on their vehicles from the same tests they now conduct, without having to perform Monte Carlo tests or simulations. [ABSTRACT FROM AUTHOR]

Copyright of Electric Drive for Locomotives is the property of Electric Drive for Locomotives Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

East Japan Railway Company (JR East) is aiming to "realize driverless train operation" as one of the key measures to respond to rapid changes in the business environment. Currently, Automatic Train Operation (ATO) equipment is not installed on the Shinkansen, but there are plans to introduce ATO or driverless operation in the near future. From 2018-2021, the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) held the "ATO Technology Study Group for Railways" in which the concept of technical requirements necessary for driverless operation was discussed. In 2021, JR East conducted the GOA4 demonstration test on the Joetsu Shinkansen. In this test, we were able to confirm the basic functions of Shinkansen vehicles such as automatic departure control, speed control, fixed position stop control, and remote stop control using ATO. We aim to realize unattended operation (GOA4) for deadhead trains between Niigata Station and the Niigata Shinkansen Rolling Stock Center by the end of the 2020 s, and driverless operation (GOA3) for passenger trains of the Joetsu Shinkansen by the mid-2030s and continue to develop the necessary technologies and build systems. [ABSTRACT FROM AUTHOR]

Estimating line impedance is relevant in transmission and distribution networks, in particular for planning and control. The large number of deployed PMUs has fostered the use of passive indirect methods based on network model identification. Electrified railways are a particular example of a distribution network, with moving highly dynamic loads, that would benefit from line impedance information for energy efficiency and optimization purposes, but for which many of the methods used in industrial applications cannot be directly applied. The estimate is carried out onboard using a passive method in a single-point perspective, suitable for implementation with energy metering onboard equipment. A comparison of two methods is carried out based on the non-linear least mean squares (LMS) optimization of an over-determined system of equations and on the auto- and cross-spectra of the pantograph voltage and current. The methods are checked preliminarily with a simulated synthetic network, showing good accuracy, within 5%. They are then applied to measured data over a 20 min run over the Swiss  16.7  Hz railway network. Both methods are suitable to track network impedance in real time during the train journey; but with suitable checks on the significance of the pantograph current and on the values of the coefficient of determination, the LMS method seems more reliable with predictable behavior. [ABSTRACT FROM AUTHOR]

Maintenance and safety inspection of trains is a critical element of providing a safe and reliable train service. Checking for the presence of bolts is an essential part of train inspection, which is currently, typically carried out during visual inspections. There is an opportunity to automate bolt inspection using machine vision with edge devices. One particular challenge is the implementation of such inspection mechanisms on edge devices, which necessitates using lighter models to ensure efficiency. Traditional methods have often fallen short of the required object detection performance, thus demonstrating the need for a more advanced approach. To address this challenge, researchers have been exploring the use of deep learning algorithms and computer vision techniques to improve the accuracy and reliability of bolt detection on edge devices. High precision in identifying absent bolts in train components is essential to avoid potential mishaps and system malfunctions. This paper presents "BoltVision", a comparative analysis of three cutting-edge machine learning models: convolutional neural networks (CNNs), vision transformers (ViTs), and compact convolutional transformers (CCTs). This study illustrates the superior assessment capabilities of these models and discusses their effectiveness in addressing the prevalent issue of edge devices. Results show that BoltVision, utilising a pre-trained ViT base, achieves a remarkable 93% accuracy in classifying missing bolts. These results underscore the potential of BoltVision in tackling specific safety inspection challenges for trains and highlight its effectiveness when deployed on edge devices characterised by constrained computational resources. This attests to the pivotal role of transformer-based architectures in revolutionising predictive maintenance and safety assurance within the rail transportation industry. [ABSTRACT FROM AUTHOR]

This study reviewed the status quo of research on machine learning (ML), including deep learning, in the rail industry. This study conducted a scientometric analysis and critical review of 640 papers selected from 12,675 web-crawled papers. The extensive and complex networks of topics, researchers, and countries were analyzed using the Louvain method, a co-occurrence keyword analysis, a degree centrality analysis, and other network analysis methods. The results indicate that the majority of studies of ML in the rail industry focused on maintenance activities and traffic management, and mainly targeted rolling stock, rails, and passengers. Overhead contact systems, including catenaries, are a high-demand objective for ML-based maintenance. Although analyses of tunnels and stations remain rare, passenger flow prediction, station air quality estimation, shield tunneling performance improvement, and ground settlement are areas of high importance. Geographically, China, the US, and the United Kingdom lead ML studies in the rail industry, and the level of collaboration is higher among European countries than among countries on other continents. Future challenges include ensuring the security and stability of ML, along with considering novel mindsets, the black-box effect, improvements in ML techniques, and resource overload when introducing ML technologies. [ABSTRACT FROM AUTHOR]

Purpose. Improving the efficiency of interaction between enterprises that operate public and private railway infrastructure when performing cargo transportation and cargo operations. Methodology. The studies were carried out using the methods of the theory of railway operation and economic and mathematical modeling. Findings. The study found that the transition from a planned economy to a market economy along with increasing customer requirements for the composition and quality of transport services is an objective reason for an increase in carriage downtime at private sidings and junction stations, a shortage of track capacity and shunting locomotives. It is shown that the existing approaches to assessing the effectiveness of the interaction between train tracks of enterprises and mainline railways are aimed at improving the performance of the rolling stock and do not create incentives to solve the problem of the shortage of transfer and processing capacity of the railway infrastructure. Originality. The paper proposes an improved method for assessing the effectiveness of the interaction of participants in the transportation process when performing transportation and cargo operations on the private sidings of industrial enterprises, based on the time the infrastructure is occupied. This approach, unlike the existing one, encourages railways and industrial enterprises to jointly search for the best technological solutions for the use of public and private railway infrastructure. Practical value. The method proposed in the paper for assessing the interaction of participants in the transportation process makes it possible to separate station services in the tariff for the use of the railway infrastructure into separate services of service facilities and track access to them. This will create a competitive environment and encourage participants in the transportation process to use the existing railway infrastructure and develop a new one to obtain a synergistic effect more efficiently. [ABSTRACT FROM AUTHOR]

This paper presents IRONEDGE, an architectural framework that can be used in different edge Stream Processing solutions for "Smart Infrastructure" scenarios, on a case-by-case basis. The architectural framework identifies the common components that any such solution should implement and a generic processing pipeline. In particular, the framework is considered in the context of a study case regarding Internet of Things (IoT) devices to be attached to rolling stock in a railway. A lack of computation and storage resources available in edge devices and infrequent network connectivity are not often seen in the existing literature, but were considered in this paper. Two distinct implementations of IRONEDGE were considered and tested. One, identified as Apache Kafka with Kafka Connect (K0-WC), uses Kafka Connect to pass messages from MQ Telemetry Transport (MQTT) to Apache Kafka. The second scenario, identified as Apache Kafka with No Kafka Connect (K1-NC), allows Apache Storm to consume messages directly. When the data rate increased, K0-WC showed low throughput resulting from high losses, whereas K1-NC displayed an increase in throughput, but did not match the input rate for the Data Reports. The results showed that the framework can be used for defining new solutions for edge Stream Processing scenarios and identified a reference implementation for the considered study case. In future work, the authors propose to extend the evaluation of the architectural variation of K1-NC. [ABSTRACT FROM AUTHOR]

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