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This chapter examines the transport policy of Bosnia and Herzegovina (BiH) across three sub-dimensions including planning and management, regulation and connectivity, and sustainability. Topics include BiH's minor progress in improving its transport policies and their implementation since the previous assessment, the lack of progress in updating its state-level single project pipeline (SPP) since 2019, and the need for maintenance and refurbishment of BiH's rail infrastructure.

Railway infrastructure safety is a paramount concern, with bolt integrity being a critical component. In the realm of railway maintenance, the detection of missing bolts is a vital task that ensures the stability and safety of tracks. Traditionally, this task has been approached through manual inspections or conventional automated methods, which are often time-consuming, costly, and prone to human error. Addressing these challenges, this paper presents a state-of-the-art solution with the development of a lightweight convolutional neural network (CNN) featuring an integrated attention mechanism. This novel model is engineered to be computationally efficient while maintaining high accuracy, making it particularly suitable for real-time analysis in resource-constrained environments commonly found in railway inspections. The proposed CNN utilises a distinctive architecture that synergises the speed of lightweight networks with the precision of attention-based mechanisms. By integrating an attention mechanism, the network selectively concentrates on regions of interest within the image, effectively enhancing the model's capability to identify missing bolts with remarkable accuracy. Comprehensive testing showcases a remarkable 96.43% accuracy and an impressive 96 F1-score, substantially outperforming existing deep learning frameworks in the context of missing bolt detection. Key contributions of this research include the model's innovative attention-integrated approach, which significantly reduces the model complexity without compromising detection performance. Additionally, the model offers scalability and adaptability to various railway settings, proving its efficacy not just in controlled environments but also in diverse real-world scenarios. Extensive experiments, rigorous evaluations, and real-time deployment results collectively underscore the transformative potential of the presented CNN model in advancing the domain of railway safety maintenance. [ABSTRACT FROM AUTHOR]

This paper examined the impact of COVID-19 using a quantitative approach on disbursement of maintenance railway projects. Information was gathered by deploying questionnaire survey to practitioners who involved in railway maintenance projects. Primary factors effected to disbursement procedure were identified from 70 major railway maintenance projects (Civil, Mechanical and Electrical works). Thirty-two (32) factors effected to the disbursement procedure were then distributed in the form of questionnaire. A total 115 respondents were sent back and ranking the severity of effective factors to disbursement procedure. Findings revealed in submission stage that contractor's failure to follow the procedures submitting invoices. contractor submits claims with errors and government prevention policies were mainly factors which effected the performance of submission. While, quarantine period and adverse weather factors were the main factor which effected the performance of inspection stage. Financial crisis and shortage of staff were main factors which effected the payment execution period. All mentioned factors effected to disbursement procedure were caused by contractors and engineers and owner staffs. It appeared factors effected to performance of disbursement procedure that were normally occurred in construction projects. [ABSTRACT FROM AUTHOR]

A successful predictive maintenance strategy for wheels and rails depends on an accurate and robust modelling of damage evolution, mainly uniform wear and Rolling Contact Fatigue (RCF). In this work a life prediction framework for wheels and rails is presented. The prediction model accounts for wear, RCF, and their interaction based on the output from MBS simulations to calculate the remaining life of the asset, given in mileage for wheels and MGTs for rails. Once the model is calibrated, the proposed methodology can predict the sensitivity of the maintenance intervals against changes in operational conditions, such as changes in contact lubrication, track gauge, operating speeds, etc. The prediction framework is then used in two operational cases on the Swedish Iron-Ore line. The studied cases are, the analysis of wheel life for the locomotives, and the analysis of rail life for gauge widening scenarios. The results demonstrate the capabilities of the MBS-based damage modelling for predictive maintenance purposes and showcase how these techniques can set the path towards Digital Twins of railway assets. [ABSTRACT FROM AUTHOR]

Efficient and seamless railway operations depend on the systematic and well-coordinated maintenance of both rolling stock and infrastructure. However, track maintenance, or 'trackwork', can cause substantial delays if not properly aligned with train schedules. This study comprehensively investigates how trackwork influences train operations in Sweden. It involves an in-depth analysis of an extensive dataset comprising over 225,000 recorded instances of planned trackwork and approximately 32.5 million train passages throughout the year 2017. Multiple logistic and negative binomial regression models showed that train running time delay occurrence is higher in the sections with scheduled trackwork. Trains passing through trackwork are 1.43 times more likely to experience delays compared to trains that do not pass through scheduled trackwork. The likelihood of an opportunity for the train delay recovery passing the section with scheduled trackwork is reduced by 11%. Additionally, the frequency of train delay increase is 16% higher, and delayed recovery is 4% lower in relation to trackwork. With the number of trackwork set to increase over the coming years, these results bring attention to train scheduling and the performance of trackwork. [ABSTRACT FROM AUTHOR]

This study addresses data-driven predictive maintenance, an area in which machine learning has received considerable attention. Traditionally, a machine learning model is trained on static data before being put into production to predict failures on incoming data. However, new data typically present novelties that were not included in the training data, such as unexpected anomalies or faults. Such novelties reduce the model accuracy and require model retraining, which we consider to be a suboptimal practice.Therefore, we propose to leverage online machine learning as an adaptive and continuous alternative to implement efficient predictive maintenance on systems that produce data continuously. The literature on predictive maintenance concentrates primarily on failure prediction, whereas there are multiple stages in a standard predictive maintenance framework, such as data preprocessing and diagnostics, that require attention. In this study, we propose a modular pipeline consisting of three modules to execute many stages inside a predictive maintenance solution. Each module represents one of our original contributions. Firstly, because a system generates repeating patterns in the form of cycles when performing its functions, we construct an online active learning-based framework to extract these cycles from a stream of sensor data (cycle extraction with InterCE). Secondly, we implement an autoencoder for encoding the extracted cycles into feature vectors (feature learning with LSTM-AE). Thirdly, we develop an adaptive scoring function to compute the health of any system at any time using online clustering on the stream of feature vectors (health detection with CheMoc). These three contributions establish our framework for processing raw sensor data for predictive maintenance. We evaluate our methods using a real-world data set provided by SNCF, the French national railway company. For each experiment, we simulate a data stream consisting of sequentially arriving data from the provided data set to test our online algorithms. The experimental results demonstrate that (i) InterCE is able to extract cycles from a high-speed stream with greater accuracy than a hand-crafted expert system, (ii) LSTM-AE can identify meaningful features from the extracted cycles, and (iii) CheMoc can discover clusters that represent physical anomalies of the systems and capture the health evolution of the monitored systems. Due to a lack of ground-truth data at the time of writing, we have not implemented the prognostics method and will reserve this for future works. This study confirms the potential of online machine learning as an adaptive and lifelong learning solution for predictive maintenance. [ABSTRACT FROM AUTHOR]

This paper aims to discuss the common obstacles detected during the maintenance of railway communication optical cables, and explore the methods to analyze and locate and deal with such obstacles. According to the situation of fiber core blocking, the obstacles of optical cables are divided into several types, such as complete blocking, partial blocking, and excessive fiber attenuation. The causes of optical cable obstacles are analyzed, and are divided into natural disasters, external forces, human factors, and cable defects. For diff erent types of optical cable obstacles, corresponding handling methods are proposed, including the principles for dealing with faults and obstacles, the sequences for achieving emergent reconnection and recovery, and the methods for locating and dealing with cable fault points. This paper emphasizes the importance of day-to-day maintenance management of railway communication optical cable lines, to ensure the safe operation of railway communication equipment, so as to guarantee the safety of railway transportation. [ABSTRACT FROM AUTHOR]

This paper presents a model that establishes a relationship between the financial resources allocated to maintain and replace railway station infrastructure assets and the resulting infrastructure quality. Until now, there has been no known relationship between quality and financial resources, making targeted planning for railway stations and the control of financial resources challenging. This model aims to predict infrastructure quality by analyzing the age-related degradation of infrastructure assets using historical data. The effects of maintenance and replacement measures are implemented in the model to map quality-improving measures. This allows for the comparison and analysis of different budget allocation scenarios on different assets, with different approaches for prioritizing replacement and maintenance measures. In this paper, the influence of budget allocation and prioritization strategies on station infrastructure quality is quantified by comparing different scenarios. Based on the results of the analysis, a quality assessment method can be evaluated, and disincentives can be identified. The analyses in this paper are carried out for quality measurement defined in Germany and the German railway station infrastructure. [ABSTRACT FROM AUTHOR]

In improving railways for the future, artificial intelligence and machine learning were identified as top-priority technology systems that enable data-driven methods and predictive maintenance. A local survey using semi-structured interviews showed that the railway industry lags behind in adopting and implementing data-driven and machine-learning methods for predictive maintenance. Insights from international studies were found to be relevant in South Africa. Other implementation barriers were identified in the socio-economic and socio-political areas of South Africa. An action priority matrix and technology roadmap was developed to guide the South African railway industry towards the implementation of data-driven and machine learning-based predictive maintenance. The action priority matrix was developed by using a tworound Delphi technique to rank the prioritisation of the required activities. The research showed the importance of considering insights from both international studies and the local context when adopting and implementing technology systems to improve business objectives. [ABSTRACT FROM AUTHOR]

In railway infrastructure, construction and maintenance is typically procured using competitive procedures such as auctions. However, these procedures only fulfill their purpose – using (taxpayers') money efficiently – if bidders do not collude. Employing a unique dataset of the Swiss Federal Railways, we present two methods in order to detect potential collusion: First, we apply machine learning to screen tender databases for suspicious patterns. Second, we establish a novel category-managers' tool, which allows for sequential and decentralized screening. To the best of our knowledge, our study represents the first attempt to adapt and implement machine-learning-based price screens within the context of a railway-infrastructure market. • First attempt of ex-ante screening the railway-infrastructure industry for cartels. • Application of machine learning to screen tender databases for suspicious patterns. • Proposal for a novel category-managers' tool that enables decentralized screening. [ABSTRACT FROM AUTHOR]

Railway noise barrier constructions are subjected to high aerodynamic loads during the train passages, and the knowledge of their actual structural condition is relevant to assure safety for railway users and to create a basis for forecasting. This paper deals with deterministic and probabilistic approaches for the condition assessment and prediction of the remaining lifetime of railway noise barriers that are embedded in a safety concept that takes into account the damage consequence classes. These approaches are combined into a holistic assessment concept, in other words, a progressive four-stage model in which the information content increases with each model stage and thus successively increases the accuracy of the determined structural conditions at the time of observation and the forecast of the remaining service life of the structure. The analytical methods used in the first stage of the developed holistic framework are based on common static calculations used in engineering practice and, together with expert knowledge and large-scale fatigue test results of noise barrier constructions, form the basis for the subsequent stages. In the second stage of the data-driven condition assessment and life cycle analysis approach, linking routines are implemented that combine the condition assessments from the visual inspections with the additional information from temporary or permanent monitoring systems with the analytical methods. With the application of numerical finite element methods for the development of a digital twin of the noise barrier in the third stage and the probabilistic approaches in the fourth stage, a maximum determination accuracy of the noise barrier condition at the time of observation and prediction accuracy of the remaining service life is achieved. The data-driven condition assessment and life cycle analysis approach enables infrastructure operators to plan their future investments more economically regarding the maintenance, retrofitting, or new construction of railway noise barriers. Ultimately, the aim is to integrate the presented four-stage holistic assessment concept into the specific maintenance and repair planning of infrastructure operators for aerodynamically loaded railway noise barrier constructions. [ABSTRACT FROM AUTHOR]

As a nationalised industry during the Cold War, Britain's railways were required to undertake civil defence work to prepare for a future conflict. Civil engineers that engaged with civil defence work were required to understand the destructive capacity of first atomic and later hydrogen weapons as well as the threats of nuclear fallout and radiation so that they could design and build structures capable of withstanding them. At first, these civil engineers objected as the work was beyond their expertise. But from 1952 onwards, each region of Britain's railways, led by their civil engineering departments, began to build infrastructure and train their staff to prepare for the continued functioning of the railways following a nuclear attack. The central focus of railway civil defence changed as the bomb threat itself evolved, but its central purpose was always more focused on repair work and the assistance of military operations over civilian evacuations. However, shackled to guidance and limited available funding from the UK's Ministry of Transport and the Home Office, planning was frequently delayed or scaled back. By the mid-1960s, alongside most other British civil defence programmes, the planning was abandoned and government funding withdrawn. But despite the myriad of setbacks and funding disappointments, those that undertook the railways' civil defence planning and training during the early Cold War saw the value in their work and hoped to contribute to a national effort to survive and rebuild should the worst ever occur. [ABSTRACT FROM AUTHOR]

The aim of the study is to evaluate the effectiveness of using georadar sounding as a high-speed method for monitoring the condition of subsurface layers of railway infrastructure facilities and identifying zones of abnormal structure in the embankment and underlying soil, which could worsen the bearing properties of the soil at the base of the railway track, and develop specific measures to eliminate soil defects. The methodology of the study involved conducting georadar surveys using Loza geophysical complex and antennas with 100 cm, lateral steps of 50 cm and 20 cm, and a depth of sounding up to 256 ns to profile the embankments of new and old railway tracks and transverse georadar profiles through problem areas. Abnormal violations of the railway embankment structure were detected through georadar survey, revealing local heterogeneities due to insufficient and uneven compaction, while the main part of the embankment and the bedrock at its base remain unaffected. The practical significance of this research is that it provides important information about the structural integrity of railway embankments, which can be used by railway engineers to identify potential problems and make necessary repairs, thus improving the safety and efficiency of railway transportation. [ABSTRACT FROM AUTHOR]

As one of the most important mobile inspection equipment for daily inspection and testing and commissioning of high-speed railway, the high speed comprehensive inspection train lacks efficient information integration, on-site decision-making and command management tools. Combined with the characteristics of daily inspection and testing and commissioning, the construction goal of multi-source information fusion integration platform for high speed comprehensive inspection train is proposed, and the overall architecture, function, interface and hardware architecture of the platform are designed. The relationship between information fusion and information integration is analyzed, and the realization process of multi-source information fusion technology in high speed comprehensive inspection train is expounded. Research and innovative applications of the key technologies such as BIM information fusion, GIS information fusion, infrastructure equipment fusion, multi-map collaborative linkage, dynamic loading of high-volume BIM models, cross-platform interconnection and information sharing, and multi-dimensional visualization are implemented. The multi-source information fusion integration platform of high speed comprehensive inspection train realizes multi-dimensional information integration and sharing, multi-disciplinary data fusion analysis, integrated decision-making and visual display based on 'One screen + M pictures', improves the efficiency of multi-party collaborative communication, improves the intelligent management level of infrastructure, and provides technical support for high-speed railway operation safety assessment and maintenance guidance. [ABSTRACT FROM AUTHOR]

Aiming at the repair of the damaged line caused by the earthquake disaster on the high-speed railway ballastless track line, this paper, starting from a comprehensive analysis of the track deformation characteristics, studies the reconstruction of post-disaster line geometry based on the principle of conforming to the deformation trend and lays the foundation for the overall repair plan of the line. Meanwhile, it studies the constraints on the fitting of the long straight section of high-speed railway and the technical route and method for fitting and reconstruction of high smoothness track line. Fitting line reconstruction scheme is put forward based on the post-earthquake deformation state analysis of Daliang Tunnel of Lanzhou-Xinjiang High-speed Railway segment line. The scheme discusses the element parameters of priority of "S" shaped curve joints segment line at fault breaks, and the concept of double constraints with small folding angle and side length introduced to the long line fitting, which can provide references for the similar line repair. And it is necessary to supplement the technical gap in the definition of long line of ballastless track line at present. The technical route of plane line shape visual fitting and reconstruction with virtual transformation is adopted to visualize the microscopic deformation, which is usually difficult to be seen directly, and observe and learn the details of line deformation. The efficiency and quality of straight line shape fitting and reconstruction can be improved through accurate manual judgment and active intervention. [ABSTRACT FROM AUTHOR]

To adapt to the "fine" and "extensive" management characteristics of railway signal equipment operation and maintenance, achieving real-time and interactive monitoring of signal equipment operation status, and developing an integrated approach to equipment operation and maintenance, this paper takes a comprehensive management perspective. To create a lightweight BIM model, the Garland folding algorithm is utilized to simplify the IFC file format. Building on this approach, the data are divided based on building component division standards to obtain separate files containing geometric information and semantic attributes. The geometric information files are converted to a 3D Tiles format, combining BIM semantic attributes with semantic attribute files through an intermediate format. Dynamic data management is achieved by setting the octree space index structure in combination with a view-frustum culling algorithm. Then, the 3D Tiles target file is imported into the Cesium platform, and Node.js is used to achieve three-dimensional visualization of railway signal operation and maintenance. The proposed method is verified using an inbound signal as an example to assess its feasibility. The results demonstrate the potential of the proposed method to achieve stable integration between BIM equipment full lifecycle maintenance and GIS geographical space display. Railway signal equipment is endowed with comprehensive one-click information query functions for equipment positioning and spatial analysis, improving the efficiency and scientific decision-making level of equipment operation and maintenance. [ABSTRACT FROM AUTHOR]

The article presents selected results of thermovision measurements of EOR elements used for heating railway turnouts under the BRIK2/0036/2022 project. The tests were carried out for various types of railway turnouts (ordinary). The results made it possible to observe the temperature distribution for different construction solutions of turnouts. Further work is focused on preparing assumptions and criteria for developing an application supporting the interpretation of thermo-grams, which can be a tool supporting the diagnostics of EOR devices. The creation of a simple diagnostic tool that allows the evaluation and interpretation of the obtained thermogram may enable maintenance services to make the right decisions in the field of operation. [ABSTRACT FROM AUTHOR]

Maintenance of rail infrastructure is an important element in rail operations to keep traffic moving. However, maintenance causes infrastructure to be taken out of service, which impacts traffic flow. In this study, the requirements of a maintenance possession scheduler for a South African application were investigated and a proposed solution was developed. The main objective of the scheduler was to minimise the deviation of the train service on a subset of rail infrastructure while ensuring that the required maintenance was done. An application case – the railway infrastructure between Bellville and Wellington in the Western Cape province of South Africa – was identified. A novel mixedinteger linear programming model that could do possession scheduling for 24 hours on a microscopic level was formulated for this case, and implemented in the software Cplex, after which it was validated. Finally, several experiments were conducted to investigate the model’s performance and the results. It was found that the model delivered optimal results in less than nine minutes, which makes it a feasible maintenance possession scheduler for day-to-day work in the immediate planning horizon. [ABSTRACT FROM AUTHOR]

Transportation infrastructure is of vital importance to economic prosperity. Transportation infrastructure (e.g. railway, highway) is usually supported by a large number of cuttings and embankments, which require regular maintenance to ensure serviceability and safety. The funding available for maintenance is usually limited. Hence, the key challenge is to identify the infrastructure slopes that require the most urgent attention. This paper presents an analytical framework that can be efficiently used for slope stability analysis at network scales. The analytical approach enables calculation of the factor of safety according to the traditional approach, the over-design factor following Eurocode 7 and the probability of failure considering uncertainty of parameters. Application of the analytical framework is illustrated with examples. The analytical approach can readily be incorporated into a spreadsheet program as user-defined functions, which are particularly useful for the generic slope stability analysis of long linear transportation infrastructure. [ABSTRACT FROM AUTHOR]

Fly ash-slag-based geopolymer is a grouting material with good fluidity and excellent mechanical and eco-friendly properties. The geopolymer can react chemically with the inert minerals of road subgrade under alkali excitation to form a good interfacial bond between road subgrade; therefore, it is suitable for the repair of weak road sections. In order to solve the problems such as the difficulty to store and transport the liquid activator of existing geopolymer grouting materials and to study the unclear mechanism of the influence factors on the fluidity and mechanical properties of geopolymer; the research on the mechanical properties of fly ash-slag based geopolymer was carried out in this paper. Experiments on the preparation of geopolymer and research on different ash-slag ratios under solid alkali excitation were studied. The influence of slag content and solid alkali content (NaOH, Na2SiO3) on the fluidity, compressive and flexural strength of fly ash-slag-based grouting materials was also researched on the basis of single-factor gradient tests. The results showed that the slurry fluidity decreased but the compressive strength gradually increased when the content of slag was increased from 20% to 50%. With the increase in alkali content (NaOH: 2–5%; sodium silicate: 0–6%), the slurry fluidity decreased and the compressive strength increased and then decreased. Combined with the analysis of the test results of Scanning Electron Microscopy (SEM), the microscopic structures of mechanical properties of geopolymer were investigated. Lastly based on ridge regression theory, a regression model was established to predict the mechanical properties of fly ash-slag-based geopolymer. The results indicate that fly ash-slag-based geopolymer has good mechanical properties and fluidity with proper contents of slag and alkali activator, which provide a reference for experiment research and engineering application. [ABSTRACT FROM AUTHOR]

Manufacturing and maintenance procedures in the railway industry regularly implement welding and metal deposition operations to produce joints, coatings and repair structures. During these processes, residual stresses arise through the generation of heat affected zones and plastic deformation. This makes accurate measurements of the internal stresses a critical aspect of manufacturing, monitoring, repair and model validation in the develop new metallic coating and joining technologies. Selection of an appropriate residual stress measurement method has many important factors including component size, resolution and the magnitude and location of internal stresses, often resulting in a combination of techniques required to obtain complete assessment of the stress state. This paper offers a review of residual stress measurement techniques for railway components including rail joints and coatings through comparison of destructive and non-destructive approaches, their measurement capabilities, benefits and limitations. A comprehensive discussion of different applications is provided with a summary of facilities available to both research and industry. [ABSTRACT FROM AUTHOR]

The paper deals with the phenomenon of long-pitch corrugation in curves of the small radii and also the effects that accelerate or slow down its development. The paper presents data from sections of railway track measured for new rails, rails before and after grinding or replacement. The effect of train speeds running through the curves and the associated cant deficiency or excess were studied. The tendencies of long-pitch corrugation development are described, which can be used to estimate when maintenance action or rail replacement will be necessary. [ABSTRACT FROM AUTHOR]

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Methods for economic assessment are often used in the rail sector to evaluate large infrastructure investments such as new high-speed lines. With larger networks and ageing infrastructure, these methods can also be used for planning maintenance. In this paper, we focus on the newly introduced concept of maintenance windows in Sweden. These are preallocated slots in the train timetable dedicated to performing, among others, periodic/frequent maintenance activities. To justify the preallocation of such windows, this study presents a method to find minimal utilization rates depending on window designs and traffic situations. Using a cost-benefit approach, the windows are assessed using a total social cost including work costs, loss in traffic production and reliability gains in future traffic. Based on a case study from the Southern main line in Sweden, we study the minimal utilization rate in different test scenarios, i.e., night or day shifts, asset degradation functions and designs of maintenance windows. The results show that lower utilization rates (4-42%) can be accepted during low-volume traffic or for partial closures, while higher utilization rates (47-83%) are required for full closures during high-volume traffic. Whether the rates are measured as the share of used window time or the share of utilized windows is less important, especially when higher utilization is required. Sensitivity analyses of asset knowledge indicate that parameters such as asset degradation function and minimum asset quality (and to a lesser extent traffic volume, discount rate and failure likelihood) can have a substantial effect on the minimum required utilization rates. [ABSTRACT FROM AUTHOR]

Efficiently planned railway maintenance helps ensure reliable infrastructure and the proper functioning of its components. The Swedish Transport Administration delegates maintenance work to contractors through a tender process. This paper analyses differences in communication and collaboration in the planning and execution of maintenance under two contract types. A document review and interviews with 22 project managers identifies issues in planning and scheduling related to knowledge transfer at contractor companies, booking time for maintenance and lack of trust between contractors and the client. The results show how the two types of contracts are perceived differently by the parties to the contracts. [ABSTRACT FROM AUTHOR]

Rail track breakages represent broken structures consisting of rail track on the railroad. The traditional methods for detecting this problem have proven unproductive. The safe operation of rail transportation needs to be frequently monitored because of the level of trust people have in it and to ensure adequate maintenance strategy and protection of human lives and properties. This paper presents an automatic deep learning method using an improved fully Convolutional Neural Network (FCN) model based on U-Net architecture to detect and segment cracks on rail track images. An approach to evaluating the extent of damage on rail tracks is also proposed to aid efficient rail track maintenance. The model performance is evaluated using precision, recall, F1-Score, and Mean Intersection over Union (MIoU). The results obtained from the extensive analysis show U-Net capability to extract meaningful features for accurate crack detection and segmentation. [ABSTRACT FROM AUTHOR]

As railway maintenance increasingly requires strict inspection and accurate repair, it is urgent to establish a reasonable assessment method for identifying the risks in high-speed railways. Severe track irregularities can intensify the vibration of vehicles and infrastructure and reduce vehicle stability and ride comfort. To more accurately identify the risks in railway tracks, this paper presents the acceleration indexes associated with ride comfort and an acceleration-based track quality index (ATQI) method. The proposed method involves a dynamic weighting factor that can extract the most sensitive information from acceleration and geometrical parameters. Results show that the weight change reflected the cumulative effect of fluctuation in each parameter in the past periods. In a case study, the ATQI method was able to identify seven risky sections that exceed the limit value, while only four risky sections were identified by the conventional track quality index. This demonstrates that the ATQI is more effective and efficient in predicting risks than the track quality index. [ABSTRACT FROM AUTHOR]

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This special issue contributes to the achievement of challenges related to human factors and automation dedicated to future railway systems. It includes transverse research topics by considering several emerging trends as the design of learning systems, of grades of automation, of cooperative system, or of analysis approaches about feedback of experience. To do so, contributions are detailed in different railway organization levels as training, design, operation, or maintenance. There are based on field studies, on simulation environments or on accident reports. They reveal the value of taking the human into account in the control and supervisory loop for the design, the analysis and the evaluation of future railway systems. Human-centered automation can indeed make system more resilient to various risks and threats by including human contributions through different degrees of automation and modalities of human–machine interaction. [ABSTRACT FROM AUTHOR]

The gradual deterioration of train wheels can increase the risk of failure and lead to a higher rate of track deterioration, resulting in less reliable railway systems with higher maintenance costs. Early detection of potential wheel damages allows railway infrastructure managers to control railway operators, leading to lower infrastructure maintenance costs. This study focuses on identifying the type of sensors that can be adopted in a wayside monitoring system for wheel flat detection, as well as their optimal position. The study relies on a 3D numerical simulation of the train-track dynamic response to the presence of wheel flats. The shear and acceleration measurement points were defined in order to examine the sensitivity of the layout schemes not only to the type of sensors (strain gauge and accelerometer) but also to the position where they are installed. By considering the shear and accelerations evaluated in 19 positions of the track as inputs, the wheel flat was identified by the envelope spectrum approach using spectral kurtosis analysis. The influence of the type of sensors and their location on the accuracy of the wheel flat detection system is analyzed. Two types of trains were considered, namely the Alfa Pendular passenger vehicle and a freight wagon. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny 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.)