Search Results (221)

UAV-based inspections are an effective way to ensure railway safety and have gained significant attention. However, images captured during complex weather conditions, such as rain, snow, or fog, often suffer from severe degradation, affecting image recognition accuracy. Existing algorithms for removing rain, snow, and fog have two main limitations: they do not adaptively learn features under varying weather complexities and struggle with managing complex noise patterns in drone inspections, leading to incomplete noise removal. To address these challenges, this study proposes a novel framework for removing rain, snow, and fog from drone images, called WeatherClean. This framework introduces a Weather Complexity Adjustment Factor (WCAF) in a parameterized adjustable network architecture to process weather degradation of varying degrees adaptively. It also employs a hierarchical multi-scale cropping strategy to enhance the recovery of fine noise and edge structures. Additionally, it incorporates a degradation synthesis method based on atmospheric scattering physical models to generate training samples that align with real-world weather patterns, thereby mitigating data scarcity issues. Experimental results show that WeatherClean outperforms existing methods by effectively removing noise particles while preserving image details. This advancement provides more reliable high-definition visual references for drone-based railway inspections, significantly enhancing inspection capabilities under complex weather conditions and ensuring the safety of railway operations. Full article

A particularly important activity carried out by railway infrastructure managers to maintain railway devices in full working order is the diagnostic process. It increases the level of railway safety. The diagnostic process involves collecting information about the equipment through inspections, tests, functional trials, parameter measurements, and analysis of the working environment, followed by comparing the obtained information with the required parameters or permissible conditions. This activity also enables the formulation of a technical diagnosis regarding the current ability of the devices to perform its intended functions, taking into account the impact of its technical condition on railway traffic safety. This is especially important in the case of railway traffic control devices, as these devices are largely responsible for ensuring railway traffic safety. The collection of data on the condition of railway traffic control devices in the form of Big Data sets and diagnostic inference is an effective factor in making operational decisions for such devices. It enables the acquisition of complete information about the actual course of the exploitation process and allows for obtaining reliable information necessary to manage this process, particularly in the areas of diagnostics forecasting of devices conditions, renewal, and organization of maintenance and repair facilities. To support this, a service data acquisition and analysis system for railway traffic control devices (SADEK) was developed. This system can serve as a software platform for maintenance needs in the railway sector. Full article

This study investigates the performance of lattice-structured heat sinks based on BCCz unit cells in comparison to conventional straight-fin and pin-fin designs. Various lattice configurations were explored. Numerical simulations and experimental evaluations were carried out to analyze thermal resistance, pressure drop, and temperature distribution under different operating conditions. Among the designs, the BCCz configuration with a circular cross-section was identified as the most promising candidate for integration into the final heat sink demonstrator, offering reliable and consistent performance. A prototype using the BCCz lattice structure was additively manufactured, alongside a conventional design for comparison. The results highlight the superior heat dissipation capabilities of lattice structures, achieving up to a 100% improvement in thermal performance at high flow rates and up to 300% at low flow rates compared to a conventional straight-fin heat sink. However, the pressure drop generated by the lattice structures remains a challenge that must be addressed. This work underscores the potential of optimized lattice-based heat exchangers to meet the severe thermal management requirements of railway power electronics. Full article

As electric multiple unit (EMU) operations and maintenance demands have expanded, spare parts supply chain management has become increasingly crucial. This study emphasizes the supply challenges of EMU spare parts, including inadequate minimum inventory levels and prolonged response times. Redesigning the OEM–railway bureau vendor-managed inventory (VMI) model contract incentive and penalty system is the key goal. Connecting the spare parts supply system with its characteristics yields a game theory model. This study analyzes and compares the equilibrium strategies and profits of supply chain members under different mechanisms for managing critical spare parts. The findings demonstrate that mechanism contracts can enhance supply chain performance in a Pareto-improving manner. An in-depth analysis of downtime loss costs, procurement challenges, and order losses reveals their effects on supply chain coordination and profit allocation, providing railway bureaus and OEMs with a theoretical framework for supply chain decision-making. This study offers theoretical justification and a framework for decision-making on cooperation between OEMs and railroad bureaus in the management of spare parts supply chains, particularly for extensive EMU operations. Full article

With China’s high-speed rail network undergoing rapid expansion, turnouts constitute critical elements whose safety and stability are essential to railway operation. At present, the efficiency of wheel–rail force safety monitoring conducted in the small hours reserved for the construction and maintenance of operating lines without marking train operation lines is relatively low. To enhance the efficiency of turnout safety monitoring, in this study, a three-dimensional BIM model of the No. 42 turnout was established and a corresponding wheel–rail force monitoring scheme was devised. Collision detection for monitoring equipment placement and construction process simulation was conducted using Navisworks, such that the rationality of cable routing and the precision of construction sequence alignment were improved. A train wheel–rail force analysis program was developed in MATLAB R2022b to perform signal filtering, and static calibration was applied to calculate key safety evaluation indices—namely, the coefficient of derailment and the rate of wheel load reduction—which were subsequently analyzed. The safety of the No. 42 turnout and the effectiveness of the proposed monitoring scheme were validated, theoretical support was provided for train operational safety and turnout maintenance, and technical guidance was offered for whole-life-cycle management and green, sustainable development of railway infrastructure. Full article

Current risk assessment in railway track maintenance operations faces challenges (low spatiotemporal accuracy, limited adaptability to various scenarios, and tendency of linear fuzzy comprehensive evaluation (FCE) methods to underestimate high-risk factors). To address these, this study proposes a novel risk assessment model that integrates subjective–objective weighting techniques with a nonlinear FCE approach. By incorporating spatiotemporal information, the model enables precise localization of risk occurrence in individual maintenance operations. A comprehensive risk index system is constructed across four dimensions: human, equipment, environment, and management. The game theory combined weighting method, integrating the G1 method and entropy weight method, is employed; it balances expert judgment with data-driven analysis. A cloud model is introduced to generate risk membership matrices, accounting for the fuzziness and randomness of risk data. The nonlinear FCE framework enhances the influence of high-risk factors. Risk levels are determined using the combined weights, membership matrices, and the maximum membership principle. A case study on the Lanzhou–Xinjiang Railway demonstrates that the proposed model achieves higher consistency with actual risk conditions than conventional methods, improving assessment accuracy and reliability. This model offers a practical and effective tool for risk prevention and control in railway maintenance operations. Full article

The rapid growth of the railway industry has resulted in the accumulation of large structured data that makes data security a critical component of reliable railway system operations. However, existing methods for identifying and classifying often suffer from limitations such as overly coarse identification granularity and insufficient flexibility in classification. To address these issues, we propose ICRSSD, a two-stage method for identification and classification in terms of the railway domain. The identification stage focuses on obtaining the sensitivity of all attributes. We first divide structured data into canonical data and semi-canonical data at a finer granularity to improve the identification accuracy. For canonical data, we use information entropy to calculate the initial sensitivity. Subsequently, we update the attribute sensitivities through cluster analysis and association rule mining. For semi-canonical data, we calculate attribute sensitivity by using a combination of regular expressions and keyword lists. In the classification stage, to further enhance accuracy, we adopt a dynamic and multi-granularity classified strategy. It considers the relative sensitivity of attributes across different scenarios and classifies them into three levels based on the sensitivity values obtained during the identification stage. Additionally, we design a rule base specifically for the identification and classification of sensitive data in the railway domain. This rule base enables effective data identification and classification, while also supporting the expiry management of sensitive attribute labels. To improve the efficiency of regular expression generation, we developed an auxiliary tool with the help of large language models and a well-designed prompt framework. We conducted experiments on a real-world dataset from the railway domain. The results demonstrate that ICRSSD significantly improves the accuracy and adaptability of sensitive data identification and classification in the railway domain. Full article

The highly demanding safety standards adopted in the railway context imply that cutting-edge technologies must limit accidents. This paper presents the human-centered outcomes of the VRAIL project, an industrial research project aiming to use enabling technologies and develop methodologies for operators directly involved in infrastructure management in the railway field. Developing integrated monitoring systems and applications that exploit Augmented Reality (AR) and Virtual Reality (VR) becomes crucial to support the awareness of planning and maintenance operators required to comply with high-quality standards. This paper addresses the abovementioned issue by proposing the development of two different prototype applications in both AR and VR for railway infrastructure data management. These environments will provide the planning operator with a complete platform to explore, use to plan maintenance interventions, and gather detailed reports to improve the overall safety of the railway line effectively. Full article

The digital transformation of the railway industry is necessary for addressing growing challenges and advancing its sustainable development. Digital technologies include Automatic Train Operation (ATO) and Remote Train Control (RTC), which offer opportunities to potentially optimize operations and enhance safety. Both technologies, however, could pose significant challenges that need to be addressed in order to capture the anticipated benefits in an urban public street environment. This study thus bridges the gap between theory and practice by exploring the projected benefits and challenges of implementing RTC and ATO through a case study of a European public transport operator deploying these technologies in tramway operations. Employing a case study methodology, the research draws on 44 semi-structured interviews with stakeholders from the operator and its supplier. The findings highlight significant anticipated benefits, including increased productivity, improved safety, and enhanced sustainability. Yet, prospective challenges such as regulatory hurdles, technical complexities, and organizational changes pose barriers to implementation. Key obstacles include ensuring robust connectivity, addressing cybersecurity concerns, and managing workforce transitions. This study underscores the importance of collaborative approaches, stakeholder engagement, and incremental deployment to mitigate risks and maximize the impact of automation technologies. By providing actionable insights into the practical adoption of RTC and ATO, this research supports the development of advanced urban transport systems. Full article

Nowadays, it is essential to contribute to sustainable transport to the maximum extent possible. Therefore, a significant emphasis is placed on environmentally friendly modes of transport, especially railway transport. For this reason, it is very important to ensure sufficient capacity of the railway infrastructure and high-quality railway transport operations. Railway transport control, operation, and management bring several specifications and unique features. One of the most important things is to correctly determine the capacity and throughput of the railway infrastructure because it must be clear how many trains can be operated on a concrete railway line for a certain time. Therefore, the issue of railway infrastructure capacity is a relatively broad and complex topic. Currently, there are several methods and ways to determine it. However, for scientific progress and research in this field, it is necessary to look for new scientific and professional solutions to the mentioned issue with effective implementation into practice. The mentioned contribution deals with new modern progressive methods of determining the capacity of the railway line using simulations and software applications. The main objective is to establish a methodology, specifically a heuristic procedure, in which specific partial steps of a new method of determining the capacity of railway infrastructure are defined. Subsequently, this new way of determining it is directly applied and explained on the specific railway line Bratislava–Komárno, which is located in southwestern Slovakia. The first part of the paper contains a theoretical framework and a brief explanation of the issue, including current used methods, as well as current scientific and professional manuscripts and papers that deal with this topic. Subsequently, the mentioned railway line is described, including the current operational problems arising on it due to insufficient current capacity. As part of the results, the proposal part is presented, including a universal heuristic procedure, which includes partial steps of the new methodology with application to the mentioned railway line. The discussions present its theoretical and practical benefits and topics for the further development of this issue. However, the most significant benefit will be the more effective identification of bottlenecks in railway operations, which will improve its smoothness and will have a significant impact on sustainable development and its aspects in the field of transport. Full article

The Belt and Road Initiative’s infrastructure development faces significant challenges in understanding and addressing the divergent perceptions between urban and rural populations, particularly regarding high-speed rail projects. This study investigates the behavioral intentions and sustainability perspectives regarding the China–Laos High-Speed Rail Service among Thai people, with particular focus on urban–rural differences. While the China–Laos railway became operational in December 2021, it is important to note that the high-speed rail extension into Thailand is not yet in operation and remains in the planning and development stage. Using survey data from 2866 respondents (1301 urban and 1565 rural) across 22 Thai provinces, this study employs structural equation modeling to examine relationships between perceived benefits, service quality, cultural factors, emotional aspects, and behavioral intentions. The findings reveal significant urban–rural disparities in infrastructure acceptance patterns. Urban residents demonstrate stronger relationships between perceived benefits and attitudes (β = 0.260) compared to rural residents (β = 0.170), while rural populations show substantially stronger responses to cultural factors (β = 0.365 vs. β = 0.309). Service quality more strongly influences behavioral intentions in rural areas (β = 0.154 vs. β = 0.138), suggesting varying priorities across geographical contexts. The study recommends implementing differentiated development strategies that address these urban–rural differences, including culturally sensitive rural engagement approaches and comprehensive service quality management systems. This research contributes to infrastructure development literature by empirically validating spatial heterogeneity in acceptance factors, extending theoretical frameworks on sustainability perceptions, and providing evidence-based guidance for managing urban–rural disparities in major infrastructure projects. Full article

Transport networks are crucial for economic growth, yet their sustainability is increasingly threatened by natural hazards. Recent floods in Italy have highlighted the vulnerability of rail and road infrastructure, causing severe damage and economic losses. The Gargano Promontory in northern Apulia has experienced frequent hydrogeological disruptions over the past decade, significantly affecting bridges and the railway network managed by Ferrovie del Gargano s.r.l. (FdG). However, structural interventions are complex, time-consuming, costly, and involve certain risks. To enhance sustainability and comply with railway safety regulations, FdG has adopted non-structural measures to improve hydrogeological risk classification and management. Despite the prevalence of flood events, the existing literature often overlooks crucial technical aspects, which this study addresses. The HYD.RAIL (HYDraulic Risk Assessment for Infrastructure and Lane) research project aims to improve transport infrastructure resilience by refining hydraulic risk assessments and introducing new classification parameters. HYD.RAIL employs a multicriteria approach, integrating parameters defined in collaboration with railway professionals. This paper presents the initial framework, offering a methodology to identify, classify, and manage hydrogeological risks in transportation infrastructure. Compared to standard methods, which lack detailed risk classification, HYD.RAIL enables more precise flood risk mapping. For example, high-risk points were reduced from 37 to 6 locations on Line 1 and from 134 to 50 on Line 2 using HYD.RAIL. This approach enhances flood risk management efficiency, providing railway operators with a more accurate understanding of infrastructure vulnerabilities. Full article

The rupture of the contact wire (CW) of a railway overhead contact line (OCL or catenary) is expected to be a rare event. However, when it occurs, and a pantograph transits under the already broken section of the CW, this can have catastrophic consequences for the pantograph which in turn can cause a further extension of the damaged portion on the OCL with a consequent disruption in the service and cause there to be a long time before the operating condition can be restored. Therefore, the prevention of such events through effective catenary monitoring is gaining significant attention. The purpose of this work is to investigate the feasibility of a monitoring system that can be installed at each end of an OCL section which is able to detect the occurrence of a broken CW event, sending an alert to the management traffic system, so as to stop the train traffic before the damaged catenary is reached by other trains. A nonlinear dynamic analysis is employed to model the OCL’s response following a simulated CW rupture and identify a set of variables that can be measured at the line’s extremities related to the occurrence of breakage in the CW. Several locations of the rupture of a CW section along the line are simulated to investigate the influence on the time pattern of the measured variables and consequently on the extraction of a signature. Finally, a proposed measurement setup is presented, combining accelerometers and displacement transducers, instead of the direct measurement of the axial load of the OCL conductors. Full article

Inductive power transfer (IPT) systems are transforming railway infrastructure by enabling efficient, wireless energy transmission for electric locomotives equipped with Li-ion batteries. This technology eliminates the need for overhead power lines and third rails, offering financial and operational advantages over conventional electric propulsion systems. Despite its potential, IPT deployment in rail applications faces significant challenges, including the fragility of materials (i.e., ferrite and Litz wires), thermal management during high-power transfers, and electromagnetic interference (EMI) on the transmitter side. This study discusses several factors affecting IPT efficiency and introduces magnetic concrete as a durable and cost-effective material solution for IPT systems. Magnetic concrete combines soft ferrite powder with water and coarse aggregates to enhance magnetic functionality while maintaining structural strength comparable to conventional concrete. Its durability and optimized magnetic properties promote consistent power transfer efficiency, making it a viable alternative to traditional ferrite cores. A comparative study has been performed on non-magnetic and magnetic concrete (with 33% ferrite powder) using both permeability tests and finite element analysis (FEA). The FEA includes both thermal and electromagnetic simulations using Ansys Maxwell (v.16), revealing that magnetic concrete can improve temperature management and EMI mitigation, and the findings underscore its potential to revolutionize IPT technology by overcoming the limitations of traditional materials and enhancing durability, cost-efficiency, and power transfer efficiency. By addressing the challenges of fragility, thermal management, and shielding of the unique coil topology design presented, this study lays the groundwork for improving IPT infrastructure in sustainable and efficient rail transport systems. Full article

This paper proposes the sizing optimization method and energy management strategy for a stationary hybrid energy storage system dedicated to a DC traction power supply system. The hybrid energy storage system consists of two modules—a supercapacitor, mainly dedicated to regenerative energy utilization, and a Li-ion battery, aimed to peak power reduction. The sizing method and energy management strategy proposed in this paper aim to reduce the aging effect of lithium-ion batteries. It is shown that the parameters of both modules could be sized independently. The supercapacitor module parameters are sized based on the results of a simulation determining the regenerative power, resulting in limited catenary receptivity. The simulation model of the DC electrification system is validated by comparing the results of the simulation with the measurements of 15 min average power in a 24 h cycle as average values of one year. The battery module is sized based on the statistical data of 15 min substation power value occurrences. The battery energy capacity, its maximum discharge C-rate, and the conditions determining its operation are optimized to achieve the maximum ratio of annual income resulting from peak power reduction to annual operating cost resulting from the battery aging process and total life cycle. The case study prepared for a typical 3 kV DC substation with mixed railway traffic shows that peak power could be reduced by ~1 MW, giving a ~10-year payback period for battery module installation, while the energy consumption could be decreased by 1.9 MWh/24 h, giving a ~7.5-year payback period for supercapacitor module installation. The payback period of the whole energy storage system (ESS) is ~8.4 years. Full article