Search Results (1,518)

In heavy-haul railway systems, effective empty railcar distribution (ERD) can optimize composition planning and meet empty railcar requirements (ERRs) at all loading ends, thereby improving the efficiency of train operations. To solve practical challenges such as the imbalanced supply–demand of empty trains, redundant loading and unloading cycles, and prolonged waiting times, this study establishes a multi-objective and 0-1 integer programming model for ERD at the loading end of a heavy-haul railway. The model can simultaneously maximize the fulfilment of all ERRs, minimize the ERD delay time, and reduce the waiting time in the heavy-train combination problem under complex constraints, including the passing capacity of sections, combination capacity of stations, and ERR at the loading end. While traditional optimization methods such as mathematical programming or heuristic algorithms partially address these issues, they are ineffective under dynamic constraints and state-space explosion. Furthermore, traditional reinforcement learning-based methods, such as Q-learning, exhibit limitations in railway scheduling due to the state-space explosion problem and inadequate model generalization. To overcome these limitations, this study proposes an innovative framework; the ERD at the loading end of the heavy-haul railway is formalized as a Markov decision process and optimized using deep Q-network (DQN) reinforcement learning. In addition, this study proposes an experience data fusion mechanism that integrates the empirical rules of the dispatchers through a modular architecture, achieving real-time constraint compliance while maintaining scalability for practical implementation. The NSGA-II genetic algorithm for multi-objective problems is used in this study to evaluate the performance of the DQN algorithm. The experimental results demonstrate that the DQN algorithm can fully meet ERRs with zero delay and produce optimal schemes for train combinations. Meanwhile, NSGA-II presents superior performance in minimizing the combination waiting time and same-destination train combinations. Meanwhile, the DQN algorithm can identify superior ERD strategies in the expanded-action and state spaces, enabling the effective handling of complex constraint-based ERD. Full article

This paper presents a new process for forming stepped shafts by upsetting with two movable deformation zones. The developed technology enables several shaft steps to be formed at the same time, thereby increasing process efficiency and reducing material consumption. A distinctive feature of the process is that it uses two forming sleeves, each with a variable cross-section of the impression, which move in an opposite direction to that of the punches during operation. This results in a simultaneous occurrence of upsetting and extrusion, thus leading to intensified plastic deformation and stabilized metal flow. The practical applicability of the process is demonstrated on the example of a forged railway axle. An analysis is carried out by the finite element method (FEM) using specimens of hot-formed C35 steel. The obtained results reveal proper material flow and the correct filling of the tool impressions. The examination of strain and stress distributions confirms favorable forming conditions. The calculated values of the Cockcroft–Latham integral indicate favorable forming conditions and a low risk of fracture initiation during the analyzed process. The results demonstrate the potential of the proposed technology and provide a basis for future experimental verification and industrial assessment. Full article

The fatigue failure of railway axles is directly associated with the operational safety of trains. As 25CrMo4 steel is commonly employed for high-speed train axles, precise evaluation of its fatigue life is essential for transportation reliability. This study compared six machine learning models following hyperparameter optimization via a differential evolution algorithm. The DE-optimized Gaussian process regression (DE-GPR) model exhibited superior predictive performance, achieving a coefficient of determination (R²) of 0.8020 and a root mean square error (RMSE) of 0.1250 on the most significant outer test fold. Furthermore, an interpretable analysis of the model utilized a combination of SHapley Additive exPlanations (SHAP) and partial dependence plots (PDP) to elucidate feature importance. The results indicate that the applied stress level is the predominant feature affecting fatigue life predictions and that it slightly interacts with surface residual stress and full width at half maximum to influence the predicted fatigue life. This study can provide valuable insights into the fatigue life assessment and process optimization of 25CrMo4 steel components. Full article

This paper presents a comparative analysis of selected economic indicators within the Italian railway passenger transport sector during the 2010–2024 period. Characterized by high-speed rail (HSR) saturation and advanced market liberalization, the Italian railway system serves as a reference model for investigating structural shifts within mature transport networks. The study aims to quantify the dynamics of transport performance through a synthesis of multiple analytical dimensions: passenger volume, transport performance (passenger-kilometers), modal split, average transport distances, and indicators of general and dynamic population mobility. The methodological framework is based on the application of chain and base indices, enabling the precise identification of cyclical fluctuations, exogenous disruptions (primarily the impact of the COVID-19 pandemic), and the subsequent degree of systemic resilience. The analysis suggests a significant shift in demand composition after 2014, characterized by an expansion of short- and medium-distance segments alongside a transformation in travel behavior. The research findings determine the correlation between infrastructure investment and the actual positioning of rail transport within a multimodal system. This work provides an analytical foundation for strategic planning in transport policy and sustainable mobility within the context of European transport integration. Moreover, these insights are practically applicable for transport operators and planners in forecasting demand, optimizing network capacity, and enhancing infrastructure resilience against future exogenous shocks. Full article

Wind-blown sand threatens railway safety in arid regions. Existing measures mainly protect the windward side and cannot fully prevent particles from crossing the embankment. These particles can be re-entrained by leeward flows and redeposited on the track. This study combines wind tunnel experiments, large eddy simulation, and field observations to examine leeward-side protection for a high railway embankment. Three configurations are tested: no protection, baffles on the leeward slope, and a checkerboard barrier at the slope toe. The results show clear differences in flow structure and sand transport. Without protection, flow reattaches within 2–3 H (H is the height of the embankment) and near-surface velocity reaches 10–11 m/s. With baffles, reattachment shifts to 3–4 H and velocity decreases to 7–9 m/s. With a checkerboard barrier, reattachment is delayed to 4–5 H and velocity reduces to 4–6 m/s, forming a stable low-velocity zone. Surface shear stress decreases from 0.4–0.5 Pa to 0–0.2 Pa, and particle concentration near the shoulder drops by about one order of magnitude. Particle transport is weakened and deposition concentrates at the slope toe. Subgrade sand accumulation decreases from 350–480 g/min to 170–250 g/min. Field results confirm these trends. The checkerboard barrier effectively limits sand movement and improves deposition stability. The proposed leeward-side protection measures can effectively reduce sand accumulation on railway infrastructure, thereby improving the long-term operational safety, resilience, and sustainability of railways in desert environments under increasing wind–sand hazards. Full article

One difficult issue regarding adaptation and adaptive reuse of existing buildings is assessing the heritage significance of functional industrial-oriented heritage buildings, such as railway buildings, that have outlived their original purpose. There is a significant tension in developing strategies for the long-term viability of a sustainable, adaptive reuse of this type of heritage infrastructure. Complicating an assessment is that these buildings may be in constrained locations, or the location has changed beyond all recognition, such that the building inhabits a sterile space. Accepted practice for conserving heritage buildings is to discourage relocating these buildings, with a scholarly concern that presentation of relocated buildings for public engagement will undermine interpretive thinking. In all cases, functional heritage buildings complicate conservation decisions in comparison with mainstream heritage buildings. Existing conservation frameworks remain insufficiently equipped to evaluate industrial and utilitarian heritage buildings whose significance derives as much from operational function, social memory, and technological context as from architectural fabric or fixed location. In response, taxonomy surveying is advanced as a novel stakeholder-centred conservation methodology capable of reconciling tensions between authenticity, adaptive reuse, relocation, and public interpretation. The aim, using case study railway buildings in a museum of industrial heritage, is to test if this methodology is transferable to other functional building types. The findings suggest that taxonomy surveying, as tested on the case study buildings, offers a scalable and internationally transferable framework for evaluating complex industrial heritage assets across differing regulatory, cultural, and spatial contexts. Full article

Despite the widespread adoption of model predictive control (MPC) in railway research, the integration of intelligent learning mechanisms into train control systems operating under moving block signaling remains limited, particularly in approaches that preserve constraint satisfaction and industrial feasibility. To address this gap, this paper presents a novel learning-based model predictive control (LMPC) framework for high-speed train control under the moving block signaling principle. Moving block signaling dynamically enforces safe inter-train separation based on the absolute braking distance, imposing stringent safety, comfort, and performance constraints on train operation. The proposed LMPC exploits the repetitive nature of railway operations by progressively improving its control policy through the incorporation of historical operational data into the terminal set of the optimization problem. This learning capability enables the controller to optimize train behavior on a given line while pursuing different control objectives, namely maximum-speed operation for leading trains and minimum safe inter-train separation for following trains, in full compliance with signaling requirements, speed limits, actuator constraints, and comfort-related jerk bounds. Simulation results on a representative high-speed line show that, compared with a conventional non-learning MPC, the proposed LMPC achieves a measurable reduction in traction-related energy consumption while maintaining comparable speed profiles, travel times, and strict constraint satisfaction. These improvements are achieved through a single software-level modification of the train control algorithm, without requiring additional onboard hardware or infrastructure upgrades, positioning the proposed LMPC as a promising and practically viable solution for energy-efficient deployment in high-speed railway operations. Full article

Railway transport is increasingly recognized as a key pillar of sustainable mobility, offering a low-carbon and energy-efficient alternative to road and air transport and playing a critical role in achieving climate objectives, regional connectivity, and sustainable tourism development. Despite extensive research on service quality, sustainability, and tourism, their interrelationship within the railway sector remains insufficiently explored. This study aims to systematically analyze the intersection of quality management systems (QMS), sustainability, and tourism in passenger railway transport and to identify structural gaps that hinder their integration. A systematic literature review was conducted following the PRISMA methodology, resulting in a final sample of 37 studies. The findings reveal a significant research gap, particularly the absence of integrated and empirically supported QMS frameworks linking passenger satisfaction with sustainability and tourism objectives. Quality-management-oriented constructs appear in 48.6% of the analyzed studies, sustainability in 32.4%, and tourism in 24.3%, while none demonstrate full integration of all three dimensions. The study contributes by providing a conceptual basis for future research on the integration of operational quality management, environmental performance, and passenger-oriented service quality in railway systems. Full article

With the integrated development of high-speed railways and urban underground rail transit, large high-speed railway station buildings are often seamlessly connected or even co-constructed with subway structures, forming a complex structural system that integrates high-speed rail, subway, and station buildings. To investigate the dynamic performance of such “ integrated station-bridge” station buildings constructed with subways, this paper takes Yichang North Station as an engineering case study and examines its vertical dynamic characteristics under multi-source train-induced loads. The station adopts a structural configuration where the station tracks are fully integrated with the station building, while the main lines are separated from it. To accurately simulate the entire process of train operation, this study established a refined “train-track-station” spatially coupled dynamics model that incorporates high-speed and subway trains, tracks, and the station structure. Based on this model, various operational scenarios were systematically analyzed, including high-speed trains passing at different speeds, parallel operation of multiple train lines, and combined operation of high-speed and subway trains. The results demonstrate that, when single or multiple high-speed train lines pass through the station at the design entry speed of 80 km/h, the vertical vibration acceleration of the elevated waiting level meets human comfort standards. The train-induced vibration response is transmitted and superimposed along the “column–beam–slab” path, resulting in localized acceleration peaks at the mid-span regions of beams and slabs directly above the tracks. Second, the impact of subway train operation alone on the vibration of the elevated level is significantly weaker than that of high-speed trains. Furthermore, under combined high-speed and subway train operations, the additional vibration contribution from subway trains shows a decreasing trend as the number of simultaneously operating high-speed train lines increases. The findings of this study validate the effectiveness of the structural design of Yichang North Station in terms of train operational safety and passenger waiting comfort. The revealed patterns of multi-source vibration transmission and superposition can provide important theoretical and numerical references for the dynamic optimization design and vibration control of similar integrated transportation hub structures. Full article

Regulations in Poland require acceptance load tests to verify bridge response under moving loads before structures are approved for operation. These tests are mandatory for new bridges, after major renovations, and for reconstructed structures, and may also be conducted as supplementary assessments of existing bridges to determine their load-carrying capacity. This paper presents one of the first documented applications, to the authors’ knowledge, of low-cost sensing technology for capturing bridge displacements with sub-millimeter tracking precision during acceptance load testing. The study explores the use of modern remote sensing methods based on digital image correlation (DIC) to assess vertical displacements of a truss railway bridge span under moving loads. Video data were recorded using a standard smartphone under nighttime conditions with artificial lighting, demonstrating a highly accessible and cost-effective measurement approach. The collected data were processed using the DES Vision System and compared with results obtained from traditional measurement techniques, such as accelerometers, enabling an evaluation of the accuracy and precision of the DIC method. The findings show that smartphone-based video recordings can provide displacement measurements with millimeter- to sub-millimeter-level tracking precision. Additionally, a numerical finite element method (FEM) model was developed to support interpretation of the structural response under moving loads. Full article

Increasing the efficiency of the railway industry requires the creation of solutions aimed at improving the technical, economic, and operational performance of wagons. It would contribute to reducing the cost of their operation. One of the most damaged elements of wagon bodies is their paneling. Its damage not only affects the loss of cargo during transportation but also threatens the safety of the movement of goods. The article is aimed at the load analysis of the body of an open wagon, whose paneling is sandwiched with corrugated panels. This solution will improve the strength of the side walls of the body of the solved freight wagon. This hypothesis has been accepted based on the dynamic load as well as the strength calculated for the body of the solved freight wagon. The dynamic load of the open wagon body has been studied with a mathematical model of its oscillations during the lateral roll. The solution to this model has shown that the maximal values of accelerations are lower by 5% than those acting on the standard design, and they act on the body of the solved freight wagon. The values of accelerations, which act on the body of the solved freight wagon, were calculated by means of numerical simulations using the finite element method implemented in the SolidWorks Simulation software. The discrepancy between the results of mathematical modeling and computer modeling is 6.5%. The strength of the open wagon body has also been calculated. It has been found that the maximal values of stresses in the paneling were lower by 17% than those acting in a standard body structure and 12% lower than the stresses in the body with corrugated panels. The study has also included an analysis of the modal properties of the body of the solved freight wagon, which demonstrates that the safety of the open wagon in motion is observed. The studies conducted will be useful in developing proposals for the creation of the newest wagon designs, including the improved economic, operational, and technical characteristics. Full article

Interpreting complex, non-linear Interferometric Synthetic Aperture Radar (InSAR) displacement time-series data for infrastructure risk assessment remains a significant geotechnical challenge. This is particularly evident in regions with established road and railway infrastructures, where the primary objective is monitoring the entire network to ensure safety and operational continuity. Because landslide displacement is a highly complex process affected by a combination of internal geological conditions and external triggers, time-series data inherently encode non-linear trends and periodic fluctuations. To address this, a data-driven framework utilizing a sliding-window transformation to engineer temporal-kinematic features is proposed, providing a broader framework for the contextualization of slope stability assessment from a network perspective. This is paired with Principal Component Analysis (PCA) for dimensionality reduction and evaluated across four unsupervised architectures: K-means, Self-Organising Maps (SOMs), Hybrid SOM-K-means, and PCA-K-means. The comparative evaluation reveals that the PCA-K-means pipeline performed best, offering a highly efficient and scalable workflow. The analysis revealed that the optimized PCA-K-means architecture successfully captured 79.20% of the kinematic variance across the first two principal components. Furthermore, it achieved a robust Between-Cluster-to-Total-Sum-of-Squares (BCSS/TSS) ratio of 71.70%, an optimal Silhouette Score of 0.320, and a low Quantisation Error (QE) of 0.90, demonstrating superior spatial separation and geometric accuracy compared to traditional heuristic methods. When cross-validated against static topographic susceptibility models, the dynamic kinematic clusters exhibited a 23% spatial convergence at the polar bounds of risk, successfully grounding the algorithm’s predictions in physically verified geomorphological features. Relying on the statistical volatility of displacements, this optimal model successfully partitioned the data into five distinct geotechnical risk classes, ranging from stable (Class A) to extreme risk (Class E). The results demonstrate that the developed dynamic framework provides a highly reliable, actionable tool for proactive, large-scale slope stability and infrastructure risk assessment. Full article

Traffic impedance modeling is fundamental for analyzing the supply–demand characteristics of the comprehensive transportation system (CTS). However, existing studies mainly focus on highway traffic impedance, while the impedance of other transport modes, such as railways, waterways, and airways, has received much less attention. Moreover, most existing models are developed for traffic operation analysis rather than planning, which limits their comparability and practical applicability in CTS analysis. To address this gap, this study develops a unified multimodal traffic impedance modeling framework for CTS and proposes simple and comparable impedance models for highways, railways, waterways, and airways based on their respective travel characteristics. The proposed models are calibrated using real-world field data with $R^2$ values above 0.8. To further verify their effectiveness in large-scale network analysis, the models are applied to the New International Land–Sea Trade Corridor project to evaluate network flow distribution and infrastructure impacts after planned transport projects are introduced. The results show that the proposed models can reproduce network flow trends well and provide quantitative support for evaluating network performance improvement after infrastructure construction. The proposed framework can assist decision-makers in multimodal infrastructure planning and in improving the efficiency and sustainability of CTS. Full article

To enhance the overall capacity utilization of HSR networks and promote balanced capacity utilization across different lines, this study considers optimizing the capacity utilization of HSR networks by adjusting the travel routes of direct trains. Based on the arc-path modeling framework for multi-commodity network flows, the problem was formulated as a dual-objective mixed-integer linear programming model to minimize the total travel time and enhance the balance of capacity utilization across the railway network, with consideration of the unique train routes, the matching of paired train routes, section capacity, operating mileage, and maximum operating time per train trip limitations. Then, the model was transformed into a single-objective function using the weighted-sum approach. A case study based on actual data from China’s HSR network and train line plans in early 2024 was conducted to demonstrate the effectiveness of the proposed method. The results show that the proposed method can control the total travel time while significantly reducing the number of sections with over-utilized capacity and improve the balance of railway network capacity utilization. The method can thus provide decision support for the efficient utilization of HSR network capacity. Full article

Railway utilities play a vital role as social infrastructure, i.e., as an essential part of ordinary citizens’ lives. In this study, we aimed to evaluate the performance of railway utility activities in Japan from three perspectives: financial management, facility operations, and human resources. Examining the relationship between daily passenger kilometers and passenger kilometers across six JR (Japan Railways) companies, we found that they can be divided into two groups: Group A, located in urban areas with higher passenger demand, and Group B, located in rural areas with lower passenger demand. We further found that the passenger demand of JR companies can be captured by a single nonlinear mathematical model. We used this mathematical model to investigate passenger demand composition in terms of commuters/non-commuters and then proposed strategies to increase passenger demand. We applied a cluster analysis technique to classify the set of JR and major private railway companies into four clusters, characterizing the performance of the railway utilities within each cluster and highlighting similarities and differences among them. Then, we applied principal component analysis and selected three principal components to explain the performance of Japanese railway utility activities. We found that the first component represents an axis corresponding to comprehensive factors that improve the performance of railway utilities from the viewpoints of both revenue and financial balance, while the second component represents an axis related to the profit and cost for railway utilities from the viewpoint of financial balance. Through multivariate regression model analyses, we found factors that increase total revenue from Japanese railway utility activities: higher passenger numbers, longer passenger transport distance, and fewer staff. Full article