Search Results (510)

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

This study presents an integrated geospatial framework for assessing the risk to high-speed railway (HSR) infrastructure, combining a persistent scatterer interferometric synthetic aperture radar (PS-InSAR) analysis with multi-criteria decision-making in a geographic information system (GIS) environment. Focusing on the Honam HSR corridor in South Korea, the model incorporates both maximum ground deformation and subsidence velocity to construct a dynamic hazard index. Social vulnerability is quantified using five demographic and infrastructural indicators, and a two-stage analytic hierarchy process (AHP) is applied with dependency correction to mitigate inter-variable redundancy. The resulting high-resolution risk maps highlight spatial mismatches between geotechnical hazards and social exposure, revealing vulnerable segments in Gongju and Iksan that require prioritized maintenance and mitigation. The framework also addresses data limitations by interpolating groundwater levels and estimating train speed using spatial techniques. Designed to be scalable and transferable, this methodology offers a practical decision-support tool for infrastructure managers and policymakers aiming to enhance the resilience of linear transport systems. Full article

Railway systems occupy a predominant role in urban transport, providing efficient, high-capacity mobility. Progress in rail transport allows fast traveling, whilst environmental concerns and CO₂ emissions are on the rise. The integration of railway systems with renewable energy source (RES)-based stations presents a promising avenue to improve the sustainability, reliability, and efficiency of urban transport networks. A storage system is needed to both ensure a continuous power supply and meet train demand at the station. Batteries (BTs) offer high energy density, while supercapacitors (SCs) offer both a large number of charge and discharge cycles, and high-power density. This paper proposes a hybrid RES (photovoltaic and wind), combined with batteries and supercapacitors constituting the hybrid energy storage system (HESS). One major drawback of trains is the long charging time required in stations, so they have been fitted with SCs to allow them to charge up quickly. A new fuzzy energy management strategy (F-EMS) is proposed. This supervision strategy optimizes the power flow between renewable energy sources, HESS, and trains. DC bus voltage regulation is involved, maintaining BT and SC charging levels within acceptable ranges. The simulation results, carried out using MATLAB/Simulink, demonstrate the effectiveness of the suggested fuzzy energy management strategy for various production conditions and train demand. Full article

Leaves react with rail steel and form a tribofilm, causing very low friction in the wheel/rail interface. This work uses twin-disc tribological testing with the addition of leaf particulates to simulate the reaction and resulting reduction in the friction coefficient in a laboratory setting. Diffuse Reflectance Fourier-Transform Infrared Spectroscopy was carried out on the organic material and the layers that formed on the twin-disc surface. Dark material, visibly similar to leaf layers formed on tracks during autumn, was used along with a transparent thin film. This “non-visible contamination” has been reported to cause low-adhesion problems on railways, but has not previously been characterised. This article discusses the nature of these layers and builds upon earlier studies to propose a degradation and bonding mechanism for the leaf material. This understanding could be used to improve friction management methods employed to deal with low adhesion due to leaves. 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

The accurate measurement of the tree diameter is vital for forest inventories, urban tree quality assessments, the management of roadside and railway vegetation, and various other applications. It also plays a crucial role in evaluating tree growth dynamics, which are closely linked to tree health, structural stability, and vulnerability. Although a range of devices and methodologies are currently under investigation, the widespread adoption of laser scanners remains constrained by their high cost. This study therefore aimed to compare high-end laser scanners (Trimble TX8 and GeoSLAM ZEB Horizon) with cost-effective alternatives, represented by the Apple iPhone 14 Pro and the LA03 scanner developed by mapry Co., Ltd. (Tamba, Japan). It further sought to evaluate the feasibility of employing these more affordable devices, even for small-scale forest owners or managers. Given the growing availability of 3D-based forest inventory algorithms, a selection of such processing pipelines was used to assess the practical potential of the scanning devices. The tested low-cost device produced moderate results, achieving a tree detection rate of up to 78% and a relative root mean square error (rRMSE) of 19.7% in diameter at breast height (DBH) estimation. However, performance varied depending on the algorithms applied. In contrast, the high-end mobile laser scanning (MLS) and terrestrial laser scanning (TLS) systems outperformed the low-cost alternative across all metrics, with tree detection rates reaching up to 99% and DBH estimation rRMSEs as low as 5%. Nevertheless, the low-cost device may still be suitable for scanning small sample plots at a reduced cost and could potentially be deployed in larger quantities to support broader forest inventory initiatives. Full article

Establishing a high-precision passenger flow prediction model is a critical and complex task for the optimization of urban rail transit systems. With the development of artificial intelligence technology, the data-driven technology has been widely studied in the intelligent transportation system. In this study, a neural network model based on the data-driven technology is established for the prediction of passenger flow in multiple urban rail transit stations to enable smart perception for optimizing urban railway transportation. The integration of network units with different specialities in the proposed model allows the network to capture passenger flow data, temporal correlation, spatial correlation, and spatiotemporal correlation with the dual attention mechanism, further improving the prediction accuracy. Experiments based on the actual passenger flow data of Beijing Metro Line 13 are conducted to compare the prediction performance of the proposed data-driven model with the other baseline models. The experimental results demonstrate that the proposed prediction model achieves lower MAE and RMSE in passenger flow prediction, and its fitted curve more closely aligns with the actual passenger flow data. This demonstrates the model’s practical potential to enhance intelligent transportation system management through more accurate passenger flow forecasting. Full article

Real-time train rescheduling is a widely used strategy to minimize knock-on delays in railway networks. While recent research has introduced intelligent solutions to railway traffic management, the tight interdependence of train timetables and the intrinsic complexity of railway networks have hindered the scalability of these approaches to large-scale systems. This paper proposes a multi-agent system (MAS) that addresses these challenges by decomposing the network into single-junction levels, significantly reducing the search space for real-time rescheduling. The MAS employs a Condorcet voting-based collaborative approach to ensure global feasibility and prevent overly localized optimization by individual junction agents. This decentralized approach enhances both the quality and scalability of train rescheduling solutions. We tested the MAS on a railway network in the UK and compared its performance with the First-Come-First-Served (FCFS) and Timetable Order Enforced (TTOE) routing methods. The computational results show that the MAS significantly outperforms FCFS and TTOE in the tested scenarios, yielding up to a 34.11% increase in network capacity as measured by the defined objective function, thus improving network line capacity. 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

This study presents a case of community-integrated project-based learning (PBL) at a Japanese National Institute of Technology (KOSEN). Three students collaborated to design and build a rideable 5-inch gauge railway system, integrating mechanical design, brushless motor control, and computer vision. The project was showcased at public events and a partner high school, providing authentic feedback and enhancing learning relevance. Over 15 weeks, students engaged in hands-on prototyping, interdisciplinary teamwork, and real-world problem-solving. The course design was grounded in four educational frameworks: experiential learning, situated learning, constructive alignment, and self-regulated learning (SRL). SRL refers to students’ ability to plan, monitor, and reflect on their learning—a key skill for managing complex engineering tasks. A mixed-methods evaluation—including surveys, reflections, classroom observations, and communication logs—revealed significant gains in technical competence, engagement, and learner autonomy. Although limited by a small sample size, the study offers detailed insights into how small-scale, resource-conscious PBL can support meaningful interdisciplinary learning and community involvement. This case illustrates how the KOSEN approach, combining technical education with real-world application, can foster both domain-specific and transferable skills, and provides a model for broader implementation of authentic, student-driven engineering education. 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