Search Results (26)

Nature-based solutions (NBSs) are central to urban climate-neutrality strategies, but their implementation still lags behind policy ambition. One reason is that most NBS benefits are public goods and rarely generate direct revenue, so we know far less about the institutional and financial conditions needed to deliver them than about their ecological performance. This paper develops a business model framework linking NBS interventions to financing and governance configurations, applying established NBS typologies and the Pestoff Triangle of state, market, and community provision to thirteen interventions across eleven sites in the Railway District of Thessaloniki, Greece. The interventions are organised into five business model categories: public space greening, private space upgrades, public–private hybrids, building retrofits, and renewable energy. Their financing logic is shaped less by intervention type than by ownership structure and stakeholder configuration. To address this gap, we followed category-specific blended finance strategies combining grants, private investment, regulatory incentives, and community resources, underpinned by stewardship-oriented governance. The findings suggest that scaling NBSs depends less on technical readiness than on institutional capacity to match financing and governance to local ownership and stakeholder contexts, offering a pathway from climate-neutrality strategy to implementable urban regeneration. Full article

With the advancement of China’s “dual carbon” goals and the green transformation of the railway sector, railway stations, as key energy-consuming nodes, require integrated energy systems that support low-carbon and renewable energy utilization. This review focuses on zero-carbon railway station integrated energy systems incorporating photovoltaic (PV) generation, energy storage, hydrogen systems, and charging facilities. Based on existing studies, the paper systematically reviews system configuration methods, operational strategies, and capacity optimization approaches. It first summarizes the roles of photovoltaic, energy storage, and hydrogen systems in railway station energy supply and outlines representative integration frameworks. It then compares standalone operation and coordinated multi-energy complementary operation, with particular attention to technical challenges in renewable energy accommodation, energy storage coordination, and hydrogen utilization. Mainstream capacity optimization approaches are further reviewed according to different energy configurations, including photovoltaic systems, energy storage systems (ESSs), hydrogen systems, and multi-energy complementary systems, with emphasis on optimization objectives, constraint formulations, and solution methodologies. The review shows that existing studies have gradually shifted from single-energy configurations toward coordinated multi-energy planning, but limitations remain in load forecasting accuracy, dynamic operational optimization, and large-scale engineering validation. Existing uncertainty management methods mainly include stochastic programming, robust optimization, chance-constrained optimization, and scenario-based approaches, which are used to address renewable energy fluctuations and load uncertainties. Future research should strengthen uncertainty modeling, real-time scheduling, and case study platforms considering diverse meteorological and load scenarios. This review provides a theoretical reference for planning and optimizing zero-carbon railway station integrated energy systems. Full article

Energy Strategy 2050 promotes photovoltaic (PV) deployment to reduce fossil fuel dependence in Switzerland. However, limited available land constrains conventional solar farms, motivating the deployment of linear PV (LPV) systems along transport corridors such as highways or railways. This paper contributes to a systematic design and modeling methodology for an LPV power plant interconnected through a medium-voltage direct current (MVDC) collection network. The main methodological contribution is the development of a modified iterative modified nodal analysis (MNA) framework tailored to LPV–MVDC systems. In long-distance feeders with high line impedance, nonlinear voltage–current coupling becomes significant. These nonlinearities cannot be accurately captured by conventional MNA assuming fixed current injections. The proposed iterative approach can more accurately capture these effects compared to conventional MNA. A case study of a 5 km railway-based LPV system is investigated to present the design and modeling methodology, including layout design, network modeling, and cable sizing. The LPV power plant reaches a peak power of 3.4 MW and requires 40 DC/DC converter stations rated at 100 kW each. Cable analysis shows that 6 mm² copper conductors satisfy voltage drop limits at a string level, while 95 mm² conductors maintain MVDC voltage variations within 3%. These results highlight technical feasibility of MVDC-based integration for efficient long-distance renewable energy distribution. Full article

Long and narrow shielded confined spaces, represented by traffic tunnels and underground utility tunnels, constitute critical application scenarios for indoor and underground positioning services. Despite their relatively simple geometric configurations, such environments suffer from severe spatial distortion of geometric dilution of precision (GDOP). Coupled with pervasive low-elevation signal propagation and intensive multipath reflection effects, conventional BeiDou Navigation Satellite System (BDS) positioning services are unable to provide continuous and reliable coverage in these scenarios. To date, existing research on high-precision pseudolite positioning for narrow confined spaces remains largely confined to theoretical analysis and laboratory experimental verification, while systematic studies on application-oriented signal atlas feature network design are significantly insufficient, forming a prominent gap that restricts the practical engineering deployment of relevant technologies. To address the aforementioned technical bottlenecks, this paper proposes a novel BDS pseudolite signal atlas network design method to improve the continuity, stability and comprehensive positioning performance in spatially distorted narrow shielded environments. Field vehicular tests were carried out in actual engineering tunnels and underground utility tunnels to systematically analyze the variation characteristics of raw BDS pseudolite observation data, including pseudorange, carrier phase, carrier-to-noise ratio (C/N₀) and Doppler shift. The test results verified that kinematic Doppler parameters exhibited outstanding stability in complex shielded environments with strong multipath interference. On this basis, a spatial feature model based on kinematic Doppler measurements was constructed, and wavelet denoising technology was adopted to extract effective typical spatial feature parameters. Combined with the deterministic one-to-one mapping relationship between Doppler peak characteristics and spatial positions, a multi-peak kinematic Doppler atlas was established, which eliminates the dependence on pre-deployment data collection, dedicated database construction and offline model training. Furthermore, comprehensively considering multi-dimensional constraints such as spatial environment scale, carrier dynamic characteristics and terminal output rate, the atlas network scheme was optimized to achieve a balanced trade-off among positioning detection accuracy, absolute positioning precision and suppression of the pseudolite near-far effect. Comparative experimental results demonstrate that the proposed BDS pseudolite atlas network effectively resolves the inherent GNSS positioning difficulty in long and narrow shielded spaces. Benefiting from the rational spectral peak configuration strategy, the system can satisfy the continuous and stable positioning requirements of multiple carrier types including motor vehicles and railway locomotives under variable motion speeds and terminal output rates. This study provides a robust and feasible technical solution for high-precision BDS positioning services in long and narrow shielded confined spaces, and holds favorable engineering application prospects for underground navigation scenarios. Full article

Ultra-high-speed railway communication systems face several technical challenges due to extremely high mobility, including Doppler-induced channel variations, frequent handovers, and increasing network traffic. These challenges not only degrade communication reliability but also negatively affect the efficiency of network resource utilization. In this paper, we review the key technical challenges in ultra-high-speed railway communication environments and investigate artificial intelligence (AI)-based intelligent network control techniques to address these issues. In particular, we examine mobility management approaches focusing on AI-based predictive handover schemes and intelligent network control architectures based on the Open Radio Access Network (O-RAN). In addition, network resource management strategies are discussed through mobile edge computing (MEC)-enabled traffic offloading and task migration techniques. Through this analysis, we discuss the potential applicability of intelligent network control technologies for improving communication reliability and enhancing network resource utilization efficiency in ultra-high-speed railway communication environments. Full article

This paper investigates the quality of mobile network coverage along the Riga–Tukums railway corridor with a focus on the performance of 4G and 5G technologies. Ensuring reliable mobile connectivity along suburban railway corridors remains a significant technical challenge due to mixed forest–urban propagation conditions, macro-cell-dominated LTE infrastructure, mobility-induced channel variability, and fluctuating passenger density. Unlike high-speed railway environments that are extensively studied in dedicated 5G-R scenarios, suburban railway systems often rely on existing macro-cell deployments, where coverage continuity, signal quality stability, and capacity constraints must be addressed simultaneously. This study presents a measurement-based evaluation of 4G and 5G radio performance along the Riga–Tukums railway corridor under real operational conditions (50–90 km/h). Classical propagation models (Okumura–Hata and COST231-Hata) are quantitatively validated using MAE and RMSE metrics, followed by correlation analysis between RSSNR and QoS indicators. A theoretical Doppler sensitivity assessment (80–200 km/h) is conducted to evaluate mobility robustness across LTE and 5G frequency bands. Mobility transition regions and handover-related time windows are geometrically estimated, and passenger density-based capacity modeling is applied to assess throughput degradation under peak occupancy scenarios. Based on these results, a multi-layer network planning strategy integrating 700 MHz macro coverage, 1700 MHz capacity enhancement, and 3500 MHz 5G NR deployment is proposed. The optimization strategy resulted in an estimated 22–28% increase in stable service coverage in previously weak-signal zones and demonstrated that propagation model deviations remain within ranges comparable to recent railway studies (≈15–25 dB RMSE). These findings provide a structured framework for suburban railway communication optimization and support the gradual modernization of railway infrastructure toward FRMCS-ready architectures. The study illustrates the applicability of modern modelling tools for assessing and improving mobile communication systems and contributes to the broader development of digital infrastructure within Latvia’s transport sector. Full article

With the integration of renewable energy into traction power supply systems at a high proportion and penetration level, the intermittency and randomness of renewable energy output significantly intensify the fluctuation characteristics of traction loads, posing severe challenges to the stable operation and precise dispatch of the system. To effectively address the dynamic tracking and anti-disturbance issues arising from the dual uncertainties of source and load, this paper proposes a dual-timescale two-layer optimization dispatch strategy based on Model Predictive Control (MPC). In the upper-layer optimization, with the objective of optimal system economic operation, a multi-step rolling optimization method is adopted to formulate a long-timescale baseline dispatch plan, fully considering the temporal correlation of photovoltaic and wind power outputs and the periodic characteristics of traction loads. In the lower-layer optimization, aimed at smoothing power fluctuations and correcting prediction deviations, the technical advantages of supercapacitors—high power density and fast response—are utilized to perform real-time tracking and dynamic compensation of the upper-layer baseline plan. This effectively reduces the impact of prediction errors on control accuracy, achieves smooth control of tie-line power, and enhances overall system stability. Case study results based on an actual railway traction power supply system demonstrate that the proposed method can fully leverage the coordinated and complementary characteristics of the hybrid energy storage system, effectively suppress power fluctuations from renewable energy output and traction loads, and achieve economic operation objectives while ensuring system disturbance rejection performance, thereby validating the effectiveness and practicality of the strategy. Full article

To address the challenges of feature extraction and degradation state identification for railway turnout switch machine power signals over the full life cycle, this paper proposes a multi-dimensional feature-fusion-based degradation state identification method for S700K turnout switch machines. Multi-domain features are first extracted from degradation power signals in the time domain, frequency domain, and time-frequency domain. Subsequently, a Uniform Manifold Approximation and Projection (UMAP)-based feature fusion strategy is employed to construct low-dimensional feature representations that effectively characterize the evolution of the equipment’s operating state, and corresponding degradation performance indicators are established. Based on the fused features, the K-means++ clustering algorithm is applied to divide the performance degradation process of the switch machine into different stages. The clustering results are comprehensively evaluated using the silhouette coefficient, Calinski–Harabasz (CH) index, and Davies–Bouldin (DB) index, and are compared with those obtained by the fuzzy C-means algorithm and the conventional K-means algorithm. Experimental results demonstrate that the proposed method achieves superior clustering quality and stability in degradation stage partitioning, enabling refined identification of degradation states and providing reliable theoretical support and technical foundations for condition monitoring and maintenance decision-making in intelligent railway turnout operation and maintenance systems. Full article

With the increasing demand for heavy-haul railway freight, both the number and volume of heavy-haul freight cars continue to grow. As the core system of railway freight transportation, the reliable operation of the brake system is fundamental to ensuring train safety. The freight car braking system fault diagnosis model, which relies on historical data while failing to account for changes in braking curves when locomotives are coupled with different vehicles, is the main reason why early failures of the braking system are not diagnosed. Consequently, real-time monitoring of the freight car braking system and early fault diagnosis have emerged as a pivotal technical challenge that necessitates resolution within the framework of the railway freight maintenance reform. This paper proposes a novel hybrid-driven prediction method that effectively combines Convolutional Neural Networks, Adaptive Radial Basis Function Neural Networks, and Extreme Learning Machines (CARE). To achieve comprehensive fault feature extraction, based on CNN of the image data classification, the K-means clustering algorithm is introduced to adaptively initialize the radial basis centers of the RBF and recalculate the radial basis radii. Moreover, to improve the real-time performance and accuracy of fault diagnosis, the network layers are expanded, and the ELM algorithm is employed to construct an optimization strategy for high-dimensional data processing in the network layers. The experimental results demonstrate that when considering the coupling of different vehicles in the railway freight car, the proposed CARE model exhibits faster convergence speed and significantly improves the effectiveness and real-time performance of fault diagnosis in the railway freight car braking system. Full article

The rapid development of transportation infrastructure in challenging geological regions necessitates innovative tunneling methods that balance efficiency, safety, and cost. This study addresses the critical construction bottleneck of large-span soft rock tunnels under high ground stress, where conventional methods often lead to unacceptable delays. Focusing on a 24.53 m span railway tunnel in southwest China, we present the significant engineering application of a “pilot-tunnel-first” method as a strategic solution to stringent schedule pressures. The core innovation lies not only in the adoption of a large 13.2 m wide pilot tunnel but also in a synergistically enhanced support system, featuring elongated bolts (6 m and 12 m) and strengthened steel arches. Numerical simulations and field validation confirmed that this optimized approach achieves a stability comparable to the traditional double-side drift method while dramatically accelerating progress. The successful implementation shortened the construction period by 1.96 months for a key 123 m section, with a manageable cost increase of approximately Chinese Yuan (CNY) 782,000, thereby ensuring the timely opening of the entire tunnel. The primary significance of this research is to provide a proven and practical technical strategy for overcoming similar soft rock tunneling challenges where project timelines are paramount, offering a substantial value for the design and construction of modern infrastructure under complex constraints. Full article

In Europe’s changing transport landscape, railways are experiencing a renaissance, driven by environmental advantages, cost efficiency, growing demand, and political support. Yet this growth also exposes major challenges, especially regarding network capacity, infrastructure availability, maintainability, and the cost-effectiveness of maintenance. This study focuses on these aspects, analyzing their interdependence and their impact on building a more resilient and efficient rail system. A prediction model, based on historical measurement data, is developed to forecast track behavior and assess an alternative maintenance strategy. This maintenance strategy uses novel approaches to define maintenance-triggering intervention values. The overarching goal of this work is to contribute to the improvement of predictive maintenance approaches. Findings show no technical or economic justification for the continual reduction of section lengths, a practice common in heavily used networks. Instead, results demonstrate that with improved planning and long-section tamping, both track quality and service life can at least be kept at the same level or even be enhanced. Longer section lengths positively influence performance by lowering running meter costs and potentially reducing operational downtime in the long run. To validate these interrelationship, future research will integrate a model that explicitly considers the costs of operational hindrances. Full article

This paper introduces a novel multi-criteria evaluation system for railway sidings, designed to support sustainable freight transport and strategic infrastructure planning. The methodology is original because it integrates six criteria—technical, economic, operational, environmental, legal, and socio-economic—into a transparent scoring model based on the Analytic Hierarchy Process (AHP). This structured approach reduces subjectivity, ensures replicability, and enables the evidence-based prioritization of infrastructure investments. Policy relevance is emphasized by aligning the model with EU decarbonization targets and national railway development strategies, providing actionable guidance for decision-makers. The framework was applied to the SCP Mondi siding as a case study, achieving an Overall Quality Index (OQI) score of 4.175, categorizing it as a high-performing siding. Sensitivity analysis of weighting factors confirmed the model’s robustness and adaptability across different contexts. These results highlight the framework’s practical value in optimizing resource allocation, revitalizing underused infrastructure, and accelerating the modal shift to rail. Full article

The focus of this paper is the development of a sustainable model for increasing the interoperability between 1435 mm (standard gauge) and 1520 mm (broad gauge) railway systems and fostering the development of efficient and sustainable railway networks. By merging technical, economic and environmental variables, the model aids strategic planning and enhances connectivity and efficiency of multimodal transportation. The proposed model considers important criteria, from diverse perspectives, that encompass interoperability and sustainable development of these railway systems. The significance of these criteria was evaluated using an expert survey, calculating the weights according to the Analytic Hierarchy Process (AHP) and then validating them applying the PROMETHEE method. By ranking the criteria based on their significance, the model helps identify development alternatives and corresponding technological solutions for interoperable railway systems. This model establishes the basis for a methodology that secures the sustainable development of railway networks according to their technical, operational, and strategies objectives. Full article

This article examines the modernization of railway systems with a focus on sustainable infrastructure development, aligning with the European Commission’s strategy for integrating 1520 mm gauge railways into the European 1435 mm gauge network. A key challenge lies in addressing the technical aspects of the railway infrastructure that are not explicitly detailed in the European strategy but have evolved through the parallel historical development of two distinct railway engineering systems. An analysis of calculation methodologies highlights that the primary difference in determining technical parameters for 1435 mm and 1520 mm tracks stems from the selection of the primary classifier based on functional purpose and strength requirements. Furthermore, the existing concept of mechanical system motion presents limitations in harmonizing the technical aspects of railway systems with different track gauges. To bridge this gap, two potential solutions are proposed. The first suggests expanding the conventional mechanical system motion framework by incorporating principles from the theory of relativity, while the second explores the application of elastic wave propagation theory as a novel conceptual model for railway system dynamics. The choice of modernization strategy will play a crucial role in ensuring long-term sustainability of the railway infrastructure, requiring a balanced approach that accounts for the operational intensity, infrastructure wear, and specific technical requirements of track elements in different railway gauge systems. Full article

The hybridization of ADM-Type Rail Service Cars aims to enhance energy efficiency, environmental sustainability, and cost-effectiveness within Uzbekistan’s railway network. Diesel-powered service cars currently contribute to high fuel consumption, elevated emissions, and costly maintenance, necessitating a transition to hybrid technology. This study introduces an innovative “sequence of linear sets–torsion electric motor–wheel pairs” design, optimizing torque distribution and power efficiency for improved operational reliability. Through system modeling, performance simulations, and real-world field trials, the hybrid system demonstrates a 15% reduction in energy consumption, a 25% decrease in CO₂ emissions, and up to 30% lower maintenance costs compared to conventional diesel models. Additionally, the hybrid technology enhances operational flexibility, allowing seamless functionality on both electrified and non-electrified railway lines. From an economic perspective, retrofitting existing service cars instead of full fleet replacement provides a cost-effective alternative, offering an estimated 10-year return on investment (ROI) through fuel savings and reduced downtime. This initiative directly supports Uzbekistan’s Green Development Strategy and railway modernization plans while holding significant commercialization potential in Central Asia and other regions with aging railway infrastructure. By addressing technical scalability, regulatory compliance, and economic feasibility, this study proposes a practical and timely hybrid retrofit solution for sustainable railway operations, aligning current industry needs with long-term environmental and financial benefits. Full article