This study examines how leadership and digital capability influence supply chain agility and operational performance within railway supply chains in North America, with a focus on both direct and indirect pathways.

A quantitative research design was employed using survey data collected from 214 organizations operating within railway-centered supply chain networks across Canada, the United States and Mexico. The proposed model was tested using partial least squares structural equation modeling (PLS-SEM).

The results indicate that leadership plays a dual role in railway supply chain networks by directly enhancing supply chain agility and indirectly influencing agility through digital capability. Digital capability significantly improves both operational agility and performance within railway supply chain networks, while supply chain agility partially mediates the relationship between digital capability and performance.

The findings suggest that railway organizations should align leadership, digital investments and agile operational processes to improve responsiveness, coordination and operational performance in infrastructure-intensive environments.

This study extends existing literature by integrating leadership into capability-based models and demonstrating its direct and indirect influence on agility and performance in railway supply chains. This study highlights how performance in railway systems depends on the alignment of leadership, digital capability and agility within structurally constrained operational environments.

This study aims to investigate how artificial intelligence can enhance the resilience and efficiency of railway timetables in disruption-prone commuter corridors. Specifically, it focuses on Moroccan railway networks connecting Casablanca, Mohammedia, and Rabat, where recurrent delays and congestion compromise service reliability. The research seeks to determine how integrating predictive delay modeling with adaptive passenger behavior can reduce secondary delays, alleviate congestion, and maintain timetable stability under operational disturbances.

A unified, simulation-based framework was developed, combining 3 interlinked modules: (1) machine learning-based predictive delay forecasting, (2) agent-based modeling of passenger adaptive behavior, and (3) dynamic timetable reoptimization using a rolling-horizon heuristic approach. The framework operates as a closed-loop system, where predicted delays and simulated passenger responses continuously inform real-time timetable adjustments. Empirical validation was conducted using operational data from Moroccan commuter trains, with scenario-based analysis comparing baseline, prediction-only, and fully integrated interventions.

Results show that the fully integrated framework significantly improves operational performance. Average train delays were reduced by 46%, total passenger waiting time decreased by 43%, and congestion intensity was nearly halved, while timetable stability remained high at 95%. The study also demonstrates that passenger behavior plays a critical role in delay propagation, and that combining predictive forecasting with adaptive control strategies prevents the nonlinear amplification of secondary delays that traditional train-centric models fail to address.

This research advances the field of railway operations by presenting a passenger-centered, AI-driven timetable reoptimization framework that integrates predictive analytics and behavioral simulation within a dynamic feedback loop. Unlike conventional models, it captures emergent congestion patterns, anticipates disruptions proactively, and provides actionable operational strategies without requiring major infrastructure expansion. The study offers a novel methodological contribution with practical implications for enhancing commuter railway resilience in high-density, disruption-prone contexts.

With the continuous expansion of railway hubs, increasing functional complexity and growing capacity constraints, the coordinated and efficient utilization of transportation resources－such as stations, lines and maintenance facilities－has become a critical issue for improving hub operational efficiency. This study focuses on the division of functions within railway hubs that incorporate shared stations operating under mixed high-speed and conventional train services.

An optimization model for hub functional allocation is developed to achieve efficient resource utilization in hubs containing mixed-operation stations. A node-arc network representation combined with an improved multi-commodity flow model is employed, taking train dwell and operation time within the hub as the optimization objective. A case study is conducted to derive optimized solutions, followed by both qualitative and quantitative analyses.

The results indicate that optimizing train operation routes and station assignments within the hub can effectively reduce the total occupation time of train flows and significantly improve resource utilization efficiency.

The proposed model demonstrates both scientific rigor and practical effectiveness. In real-world operations, it can provide operators with preliminary and proactive functional allocation schemes, help identify key constraints limiting hub capacity utilization and offer decision support for transport plan adjustments or infrastructure and facility upgrades.

The pantograph-catenary system (PCS) plays a crucial role in ensuring stable and continuous current collection in electric railway operations. This paper aims to review and synthesize existing research on contact strip (CS) wear, with particular emphasis on dominant wear mechanisms, influencing parameters, and material performance under high-speed train (HST) conditions.

A structured and critical review of the literature is conducted, covering mechanical, electrical, and electro-mechanical wear mechanisms. Relevant studies are analysed in terms of operating conditions, including train speed, contact force, electrical current intensity, system configuration and environmental influences. Special attention is given to metal-impregnated carbon materials, which are widely adopted in current HST applications, as well as to composite materials that are being actively investigated and developed for future HST systems. Comparative analysis is performed to identify governing factors and methodological trends in existing investigations.

The literature indicates that CS degradation results from strongly coupled mechanical and electrical loading, with the relative contribution of each wear mechanism varying according to operating regimes and material characteristics. Carbon-based composite materials demonstrate a favourable balance between conductivity, wear resistance and compatibility with the contact line (CL). However, inconsistencies remain in the quantification of wear interactions and in the standardisation of evaluation approaches under diverse climatic and operational conditions.

This review provides an integrated and up-to-date synthesis of multi-mechanism wear behaviour in CS, highlighting current research gaps and methodological limitations. The paper offers structured insight to support material selection, performance optimisation and future research directions, contributing to enhanced reliability and maintenance efficiency of HST systems.

This review aims to provide insights for researchers and practitioners to utilize the full potential of digital twins (DT) in railway infrastructure, furthermore, promoting the future advances of DT technology in the field.

This paper comprehensively reviews the latest progress in the application of digital twins in railway infrastructure (Digital Twin for Railway Infrastructure (DTRI)). It systematically summarizes the application scenarios and elaborates on the core components of DTRI.

The core components of DTRI include virtual entity models, twin data, and virtual-physical connections. Emerging developments such as artificial intelligence (AI), data fusion, the Internet of Things (IoT) and advanced algorithms have been incorporated as the key technologies. The primary application scenarios focus on monitoring and maintenance, failure prediction and prevention and life cycle management.

DT technology has emerged as an innovative framework in the railway infrastructure sector, offering unprecedented opportunities for real-time monitoring, predictive maintenance and optimization control. The paper analyzes current and future challenges alongside emerging development directions, highlighting the transformative potential of DT technology in promoting intelligent and efficient railway infrastructure operations.

This study aims to propose a cooperative adhesion control method for multi-motor electric locomotives that explicitly considers axle load transfer (ALT). The method is intended to optimize the output torque of each motor, maximize the utilization of available wheel-rail adhesion within the total torque command, mitigate wheel skidding and sliding phenomena, and achieve optimal torque allocation across all axles.

An advanced cooperative maximum adhesion tracking control strategy is developed using Model Predictive Control (MPC). First, a comprehensive multi-agent dynamic model of the locomotive traction system is constructed based on Newton's second law, which incorporates longitudinal train dynamics, individual axle rotational dynamics, nonlinear wheel-rail adhesion characteristics, and dynamic ALT-induced load redistribution. Then, a novel MPC-based multi-axle co-optimization method is presented. This controller calculates the optimal output torque through real-time iteration based on a reference slip speed, ensuring coordinated torque allocation under strict physical constraints imposed by the traction control unit.

Simulation studies conducted under dry, wet, and mixed rail surface conditions indicate that the proposed MPC system effectively compensates for ALT. The results demonstrate that explicitly embedding ALT into the control framework allows the system to adaptively redistribute motor torques according to real-time axle loads. This guarantees stable slip regulation and significantly improves overall traction performance and power distribution compared to conventional strategies that ignore ALT.

This study introduces a novel cooperative adhesion tracking control scheme that uniquely integrates axle load transfer into a multi-agent MPC for multi-motor electric locomotives-a complex configuration rarely addressed in previous papers. This approach resolves the critical issues of torque imbalance, lightly loaded axle slip, and heavily loaded axle under-utilization, offering significant theoretical and practical value, especially under variable and non-uniform rail conditions.

Against the backdrop of rising railway network density and operational complexity in China, the traditional reactive safety management paradigm fails to meet full-lifecycle safety control demands, and the current model suffers from poor institutional coherence, low coordination efficiency and inadequate control precision. This study explores the inherent logic and operational mechanism of railway safety management, constructs a well-structured safety management framework and dynamic operational model and provides theoretical and practical support for modernizing China's railway safety governance capacity to address safety challenges in high-density, high-speed and heavy-haul railway operations.

A systematic review and research integration approach is adopted. Theoretical advances and practical applications of railway safety management systems in the EU, the USA and Japan, as well as mature practices in China's civil aviation, power, petrochemical and coal mining industries, are systematically analyzed. Based on China's railway safety management practices and industrial characteristics, a "1+3+1" hierarchical three-dimensional core framework is constructed. Drawing on PDCA cycle theory, a SERA dynamic operational model is developed. Key digital technologies supporting the framework and model are identified via focused analysis of railway safety management digital transformation.

The findings indicate that integrating general international safety management concepts, foreign railway professional practices and domestic localization experiences is pivotal to constructing a railway safety management system tailored to the Chinese context. The "1+3+1" framework comprises legal-institutional and organizational responsibility systems as its strategic foundation, three core execution systems—prevention and control, emergency response and disposal, and assessment and improvement—as its operational pillars, and technological and cultural support systems as its enabling underpinning. This architecture enables full-element coverage, full-process integration and comprehensive coordination of railway safety management. The SERA dynamic functioning model achieves closed-loop iteration through the cycle of "Systematize and Support→Execute and Enforce→Respond and Recover→Assess and Advance". Integration of key digital technologies—including multi-source data fusion and intelligent risk alerting—enables efficient functioning of the framework and model. This integrated approach effectively addresses fragmentation and inadequate coordination in traditional railway safety management, thereby driving transformation from experience-driven to data-driven paradigms, from static control to dynamic optimization, and from passive response to proactive prevention.

First, this study transcends traditional fragmented safety management paradigms, constructing a railway safety management framework spanning the full lifecycle, all factors and full processes, thereby transforming railway safety management from scattered measures to systematic, goal-oriented endeavors. Second, it integrates general international safety management principles with Chinese railway contextual attributes, establishing the SERA dynamic functioning model with global interoperability and local adaptability, thereby achieving closed-loop connectivity from strategic design to operational implementation. Third, it comprehensively delineates key digital technologies for the digital transformation of China's railway safety management system, thereby providing technical solutions for precision and intelligentization in complex operational scenarios. Fourth, these findings enhance systematic rigor and methodological soundness in China's railway safety governance, while furnishing a replicable model for safety management of other complex transportation infrastructure－including highways and urban rail transit. This lays the foundation for a railway safety management paradigm that harmonizes Chinese characteristics with global interoperability, thereby contributing Chinese experience to global railway safety management.

In response to the problems of performance degradation, sealing failure and medium leakage of metal components in air conditioning pipelines due to material corrosion, this study intends to lay the foundation for exploring the causes of corrosion and optimising anti-corrosion measures in the future, ensuring the safe and stable operation of air conditioning pipelines.

Systematic investigation of corrosion mechanisms and cleaning agent effects was conducted using scanning electron microscopy (SEM), atomic force microscopy, corrosive element detection and cleaning agent immersion tests.

Findings indicate dense corrosion pitting on the tail surface of air conditioning copper tubes (TP2 deoxidised copper), with enriched corrosive Cl and P elements serving as primary corrosion drivers. Under continuous immersion, metal corrosion rates significantly exceed air exposure conditions. Both acidic and alkaline cleaning agents exhibit stronger corrosive effects on carbon steel pressure sections and copper tubes than neutral cleaning agents.

The main factors causing corrosion of TP2 deoxygenated copper pipes in air conditioning, the effects of different environments, and types of cleaning agents on the corrosion of pipeline metals have been identified in the study. It provides reliable theoretical support for the optimisation of anti-corrosion design and scientific selection of cleaning agents for air conditioning pipelines.

Vibrations induced by external loads play a critical role in the performance and safety of high-speed train bogies. Accurate knowledge of the dynamic forces acting on bogie frames is essential for predicting structural responses, enhancing numerical modelling and planning maintenance more effectively. This study aims to develop and evaluate a comprehensive model-based framework for predicting excitation forces on railway bogies, addressing the challenges posed by forces that are difficult or impossible to measure directly.

The proposed framework integrates multi-body dynamics (MBD) simulations, structural finite element (FE) modelling and machine learning (ML) to estimate the forces acting on the bogie frame of a high-speed train. Firstly, an MBD model of a Chinese high-speed train was established and validated against in-service measurement data, from which realistic time-domain loads acting on the bogie frame can be obtained. Separately, modal dynamic simulations of the bogie frame’s FE model were performed with stochastic loading to extract corresponding accelerations over a broad range of dynamic behaviour. These were employed to train an ML model to learn the inverse mapping from structural response to applied forces. For validation, the MBD-derived forces were applied to the FE model to obtain corresponding accelerations, which were then used to assess the ML model’s ability to reconstruct the original forces.

The approach can successfully predict nonlinear excitation forces acting on the bogie frame. Based on modal system responses to randomized force inputs, the entire parameter space can be represented, and the trained ML model demonstrates a strong capability to estimate dynamic loads from validated MBD simulations. Through appropriate training, the method exhibits robustness against noise and sensor placement and opens new opportunities for improving the analysis of track–vehicle interaction and the dynamic modelling of bogies. Research limitations/implications – The approach depends on the accuracy of the validated MBD and FE models, meaning modelling assumptions and simplifications may introduce errors in the predicted forces. High-fidelity in-service measurements are required for model validation but are not always available. Purely simulation-based models enable the prediction of forces and load distributions at the bogie, but the results are strongly model-dependent. Even if the models are validated against reference data, they only reflect an idealized operating condition, and uncertainties in measurement parameters, model parameters, damping behaviour or contact models can significantly affect the accuracy of force predictions.

This research introduces a novel, integrated framework for indirect bogie force estimation that enhances both modelling accuracy and practical diagnostic capability in railway engineering. By integrating numerical simulations, in-service measurements and ML, the study advances current methodologies for analysing high-speed railway vehicles. The approach offers valuable potential for refining vehicle models, guiding maintenance strategies and informing future research on data-driven structural force prediction.

This paper presents a comprehensive systematic review of low-carbon solid waste materials applied in railway sub-ballast layers, aiming to critically assess their mechanical performance, durability, environmental benefits and regulatory readiness. The study addresses the growing need to decarbonize rail infrastructure while reducing dependence on natural aggregates, positioning sub-ballast as a strategic layer for circular economy implementation in ballasted track systems.

A PRISMA-based systematic review methodology was adopted to identify, screen and analyses peer-reviewed studies published between 2000 and 2025. The final database comprises experimental, numerical and field investigations covering mining residues, steel slags, construction and demolition waste, rubberized composites, alkali-activated materials and other industrial by-products applied to railway sub-ballast. Mechanical behavior under cyclic loading, resilient modulus, permanent deformation, hydraulic performance, durability and environmental indicators were extracted and synthesized. In parallel, an international regulatory analysis was conducted to compare sub-ballast specifications across Europe, North America, Asia-Pacific and Brazil, enabling identification of performance-regulation gaps and barriers to implementation.

The review demonstrates that several low-carbon waste-derived materials exhibit mechanical performance comparable to or exceeding that of conventional granular sub-ballast, particularly in terms of stiffness retention, resistance to permanent deformation and degradation under repeated loading. Steel slags, recycled concrete aggregates, slate waste and rubber-modified blends consistently show favorable resilient behavior and enhanced damping capacity, while certain mining residues and alkali-activated granular systems present promising strength and durability characteristics. Life-cycle evidence indicates substantial reductions in embodied carbon and natural aggregate consumption when these materials are adopted. However, current railway standards remain largely prescriptive and index-based, rarely incorporating cyclic performance criteria or carbon metrics, creating a structural disconnect between scientific evidence and regulatory acceptance. This gap significantly limits large-scale implementation despite growing technical maturity.

This study provides the first integrated synthesis focused exclusively on low-carbon solid waste materials for railway sub-ballast, combining mechanical performance, environmental assessment and international regulatory comparison within a unified analytical framework. By explicitly linking laboratory evidence to policy and standardization challenges, the paper advances performance-based pathways for sustainable railway substructure design. The findings offer actionable guidance for infrastructure managers, regulators and researchers seeking to accelerate the transition toward circular, low-carbon rail systems through sub-ballast innovation.