Virtual coupling (VC) has attracted increasing research attention as a train control concept for rail systems, enabling trains to operate at very short separations using cooperative, relative distance-based control. This paper reviews how VC affects railway operations across three interconnected layers: operation control, transport organization and system-level performance evaluation. At the control layer, we synthesize research on separation and collision avoidance models, trajectory planning, formation and cooperative control, and uncertainty-aware control methods, covering both model predictive control-based designs and alternative control strategies that address parametric uncertainty, heterogeneous braking, delays and cyber-attacks. At the planning layer, we survey line planning, timetabling, rolling stock circulation, train constitution and rescheduling models that embed VC-enabled headway relaxation, dynamic (de)coupling and platoon composition as decision variables, linking VC to passenger-oriented capacity allocation and disruption management. At the evaluation layer, we review signaling system safety assessment, capacity and headway analysis, and energy and environmental studies that quantify VC impacts under realistic signaling, infrastructure, and disturbance conditions. A thematic–methodological mapping highlights the prevalence of optimization and model predictive control, the limited integration between control and passenger-oriented planning, and the currently limited body of evidence for high-speed and heavy-haul applications. The review identifies consistent modeling patterns, critical gaps, and implementation challenges, and proposes a realistic research roadmap for treating VC as an operational and service innovation rather than a purely signaling upgrade.

Urban rail transit systems contain numerous sharp radius curves where rail fatigue defect occurs frequently. In severe cases, this leads to extensive rail scaling, rail surface fatigue cracks, and intensified wheel-rail vibration and noise. Appropriate rail profiles can effectively optimise wheel-rail contact status and mitigate the deterioration development of wear and wheel/rail fatigue defect. Addressing this issue, this paper combines field testing evaluation and simulation analysis methods to establish a metro vehicle-track coupled dynamics model validated by measured data. Three profile schemes and three simulation scenarios were considered to comprehensively study and analyze the matching performance between sharp radius curve rail profiles and actual wheel profiles from perspectives of wheel-rail contact stress distribution, fatigue and wear indices, and vehicle dynamic performance.

Results indicate that the 60N profile consistently exhibits optimal stress control effectiveness under all operating conditions. Compared to the traditional 60 profile condition, normal and tangential contact stress are reduced by up to 64% and 61%, respectively. Relative to the measured profile condition, stress reductions of up to 64% and 45% are achieved at the gauge corner. The 60N profile demonstrates the lowest fatigue index compared to the 60 profile and measured profile, while maintaining stable wear characteristics and improving vehicle lateral stability with reduced bogie acceleration peaks. Based on comprehensive analysis, the 60N profile is recommended as the optimal solution for both original rail profile selection and grinding target profile.

Insufficient wheel–rail adhesion during braking poses a significant threat to operational safety and accelerates component wear. Although anti-slide valves modulate braking force to mitigate sliding, the resulting speed fluctuations can inadvertently trigger emergency braking commands from the signaling system, potentially exacerbating the slide. Existing detection methods are largely reactive, relying on post-event identification rather than predictive foresight. To bridge this gap, an onboard module for predictive risk warning is proposed. Utilizing real-world vehicle and signaling data, a cross-attention CNN–GRU model is proposed for the accurate prediction of whole-train wheel slide probability. In parallel, a train dynamics model projects the braking trajectory to assess the displacement deviation relative to target positions and safety margins. By integrating the data-driven slide probability with the model-based displacement deviation, a dynamic risk matrix is constructed to assess different levels of train slide risk. With an optimal prediction horizon of 2.5 s and established alarm thresholds, the proposed module provides actionable inputs for the train operation control system. Experimental results demonstrate that the proposed method effectively predicts both wheel sliding and the associated overrun risk. This enables the signaling system to proactively intervene in sliding control, preemptively mitigating sliding occurrences and enhancing overall operational safety.

Accurate 3D reconstruction of railway tunnels is crucial for infrastructure maintenance, safety assessment, and digital twin development. However, existing methods often fail in real-world scenarios due to sensor noise and spurious geometry generation in non-structural ground regions—particularly caused by rails, sleepers, and ballast. To address these domain-specific challenges, we propose TunnelSDF-FilterNet, a domain-aware neural signed distance function (Neural SDF) framework explicitly designed for high-fidelity railway tunnel reconstruction. Our approach introduces three key innovations: (1) a robust preprocessing pipeline that integrates DBSCAN–RANSAC clustering with normal and height-based geometric filtering to automatically remove ground artifacts without manual intervention; (2) a tunnel-tailored Neural SDF training objective featuring a novel ground suppression loss to constrain reconstruction within the tunnel envelope and an edge-aware regularization term to preserve fine structural details such as segment joints; and (3) a NeuralPull-based surface refinement strategy applied during inference to achieve sub-voxel precision in surface extraction. Extensive experiments on real-world tunnel datasets demonstrate that TunnelSDF-FilterNet significantly outperforms state-of-the-art methods in both qualitative fidelity and quantitative metrics—including lower Chamfer Distance (CD), higher Normal Consistency (NC), reduced Ground Inclusion Rate (GIR), and improved F-Scores. The proposed framework delivers operationally deployable, high-precision 3D tunnel models, offering a practical AI-driven solution for intelligent railway infrastructure management and digital twin construction in modern railway systems.

Stray current in urban railway systems presents a significant challenge, including localized corrosion of rails, fastening systems, and adjacent metal structures, which compromises infrastructure safety. This study focuses on enhancing the resistance of rail fastening systems to mitigate stray current. Laboratory tests were conducted on samples where rails were fixed using two different fastening systems. During testing, the samples were subjected to 26 VDC, and electrical resistance and potential distribution were measured under controlled conditions. The results indicated that both systems were inadequately insulated, resulting in stray current, although in one system the anchor bolts were insulated primarily to prevent current leakage into the track substructure. Material analysis showed that the elastomeric elements in this system were not specified for high electrical resistance, so stray current was not prevented. Modifications for both systems were proposed to improve stray current prevention and were analyzed using numerical models in COMSOL Multiphysics software. The study highlights the importance of establishing precise requirements for the electrical resistance of fastening systems and developing a standardized methodology for measuring resistance.

The continuous expansion of the scale of global urban rail transit (URT) network has led to the aggravation of traction energy consumption and the utilization of regenerative braking energy (RBE). Applying energy storage technology in URT can enhance the stability of traction power supply system (TPSS), absorb RBE, reduce traction energy consumption, and bring an opportunity to access new energy. This paper focuses on the flywheel energy storage system (FESS). First, the basic working principle and basic structure combined with the application summary of FESS in URT are analyzed in detail. Second, the technical breakthroughs of flywheel devices that need further improvement are illustrated in terms of rotor, bearing, and converter. Then common energy management and control methods of single flywheel device and flywheel array are summarized and compared. Finally, from the aspects of high-performance development of flywheel unit as well as intelligent energy management of FESS, the future development of FESS is put forward, which provides a reference for the standardization, high efficiency, and simplicity of FESS in URT.

Flexible (un)coupling operation within a Y-shaped network allows for the dynamic adjustment of metro train formations at the diverging junction based on fluctuating passenger demand. Train timetable and rolling stock schedule serve as essential elements in organising efficient flexible (un)coupling train operation. However, reducing overall waiting time during the optimisation process may lead to disparities, where some passengers experience much longer waiting periods compared to others. To address this issue, we formulate a multi-objective optimisation model that jointly determines train formations, timetable, and rolling stock schedule while balancing operating cost, total waiting time, and fairness. Moreover, we design an operationalisation method for balancing the objectives and solve the problem using the Gurobi solver. Numerical experiments are conducted under different scenarios on a Y-shaped metro network in Guangzhou, China. The results reveal that the model can effectively improve fairness compared to cases without fairness considerations. By allowing a moderate increase in total waiting time or operating costs, passengers with the longest waiting times experience reduced waiting durations. The standard deviation of waiting times also decreases. For instance, a 25.6

We are presenting a crew planning optimization project for the train drivers of an urban rail transportation company. The core is about crew rostering, i.e., defining over a certain period of time (e.g., one year) for each train driver sequences of working days and rest days, and also specifying whether working days shall contain either some early, late, or night shift. But the project was not only about solving just one classical crew rostering problem. Rather, during the last three years, the entire process of crew rostering had been investigated. Apart from having modeled, solved, and implemented two different variants of crew rostering—a cyclic and an acyclic one—there have been designed and used three further optimization models for less typical surrounding sub-processes, as well as a simple crew assignment model. During the design and implementation process of these six optimization models, the focus had been put on their annual and daily usability, respectively. In particular, many practical requirements had been collected and implemented in rather straightforward ways. The result is a family of mathematical optimization models, whose results cover the valid annual crew rosters from the year 2024 on, as well as the daily assignment of specific duties to train drivers from May 2025 on at S-Bahn Berlin GmbH.

Subway Station Construction Site Layout Planning (SSCSLP) in dense urban cores is characterized by extreme spatial constraints. Conventional Constraint-Preserving Search (CPS) paradigms often exhibit significant limitations in such environments. Specifically, the strict rejection of infeasible solutions fragments the search space, frequently causing stagnation in local optima. To address these challenges, a novel Graph-based Dynamic Constraint-Relaxation Multi-Objective Optimization Framework is proposed. An Edge-Attributed Weighted Graph is utilized to capture complex spatial dependencies. Uniquely, the Graph-based Dynamic Constraint-Relaxation NSGA-II (GDCR-NSGA-II) is developed to overcome optimization bottlenecks. A Dynamic Constraint-Relaxation Strategy (DCRS) transforms hard constraints into a continuous penalty landscape. This mechanism establishes an infeasibility-driven search trajectory, guiding the population from the infeasible region toward the global optimum at the feasible boundary. The proposed framework was validated using a case study of Chongqing Rail Transit Line 27. Comparative analysis demonstrated that, when the single best feasible solution identified by the conventional method was strictly used as the benchmark, the proposed framework reduced the average construction cost by approximately 49.4% and improved average safety performance by 63.7%. Consequently, this study provides robust theoretical support for intelligent decision-making in ultra-constrained engineering scenarios.

Intercity transport infrastructure provides a framework for spatial linkages among urban agglomerations. This paper identifies the dynamic changes of spatial correlation degree of Beijing–Tianjin–Hebei (or “J-J-J”) urban agglomeration by calculating core entropy value and traffic connection degree. The result shows that, first, the urban spatial layout, urban economy, spatial connection, and transportation network within the urban agglomeration are closely linked. During the expansion of the transportation networks, their impact will be far greater than that of the geographical location. Second, the traffic network greatly changes the spatial layout of the urban agglomeration, thus reshaping the connections between cities. Third, the transportation network furthers the evolution of regional integration. This paper makes up for the one sidedness of existing research and provides a valuable perspective for the spatial research of urban agglomerations.