Search Results (31)

High-speed trains are prone to severe lateral vibrations induced by track irregularity excitations under complex operating conditions, which deteriorate ride comfort, reduce running stability, and accelerate wheel–rail wear. Existing lateral suspension vibration control methods mainly rely on fixed parameters and expert experience. To further optimize and improve the control performance of semi-active lateral suspension systems for high-speed trains, this paper proposes the Physics-Constrained Dual-Attention Reinforcement Learning for Semi-Active Lateral Vibration Control of High-Speed Trains (SA-PRL). The proposed method is built upon a semi-active Twin Delayed Deep Deterministic Policy Gradient algorithm (SATD3). To ensure the physical realizability of control actions, the Logical Constraints of Skyhook Control (LCSC) are introduced to map the controller output into physically feasible damping commands. Furthermore, to enhance the perception of critical state features and improve value estimation capability, a Critic Network with Dual-Head Self-Attention (CDHA) is developed. Based on a railway vehicle lateral dynamic model, a series of simulation experiments are conducted under four excitation conditions, including single-peak sinusoidal excitation and multi-peak sinusoidal excitation, as well as the Chinese high-speed railway track irregularity (CHSRTI) and German low-interference track irregularity (GLITI) excitations. The results demonstrate the lateral vibration suppression capability of the proposed method over passive suspension, skyhook damping control, and existing reinforcement learning methods, with reductions of 47.5% and 47.7% in the RMS carbody lateral acceleration under the CHSRTI and GLITI spectra, respectively. The proposed method provides an effective solution for semi-active lateral vibration control of high-speed trains. Full article

Human presence in rail track areas represents a critical safety risk, particularly under low-visibility conditions where RGB-based monitoring systems become unreliable. This paper presents a thermal-only railway monitoring framework for human detection, auxiliary pose-based skeletal representation, identity-preserving tracking, temporal refinement, track zone classification, and camera-to-human distance estimation. The proposed approach combines a YOLO-based human detector trained on annotated thermal railway images with a pretrained YOLO pose model used as an auxiliary module to obtain a reduced 13-keypoint skeletal representation for thermal-image interpretation. ByteTrack is used for identity association across frames, while Kalman filtering reduces frame-to-frame keypoint localization jitter and supports short-term trajectory continuity during temporary missed detections. Unlike fully learned monocular depth estimation, the proposed distance estimation method exploits rail track geometry as a physical scene reference. A YOLO-based rail track detection model extracts the rail region, after which rail line candidates are fitted, and the apparent rail track width is measured at the vertical position of the detected human. Distance is estimated using an inverse-perspective relationship based on the known railway gauge. Experiments on thermal video sequences acquired along the Niš–Prokuplje railway line demonstrate reliable human detection, stable tracking, effective track zone classification, and distance estimation consistent with real distance trends. Full article

Railway marshalling yard inbound operations are affected by irregular train arrivals, finite receiving-yard capacity, service interruptions, and boundary states carried across operating days. This study develops a hybrid fluid–queueing and simulation framework for analyzing congestion evolution in the arrival–technical-operation–hump-disassembly process. The framework integrates continuous arrival-input construction, boundary-state stability validation (PSSBV), and daily gated integer discrete-event simulation (DGDES). Circular kernel density estimation and observed train-level sequences represent non-stationary arrivals; PSSBV determines operationally reasonable initial conditions; and DGDES captures finite-capacity admission, FIFO outside holding, parallel technical operations, hump disassembly, and service-interruption windows. The framework is evaluated under deterministic and stochastic service-time conditions using continuous-operation data from a large Chinese marshalling yard and field-observed occupancy and waiting-time indicators. The results show that congestion is driven less by daily arrival volume alone than by the interaction of concentrated arrivals, pre-disassembly backlog, residual in-yard occupancy, and insufficient hump-disassembly clearance capacity. The simulated outside holding rate follows fluctuations in the field-based track–time load ratio and is closely associated with disassembly waiting time, system sojourn time, and pre-disassembly queue length. Lagged analysis indicates that previous-day saturation and residual workload can increase next-day outside holding risk. Stochastic service times produce similar high-risk patterns while increasing variability and tail-risk exposure. The proposed threshold–probability diagnostic framework supports rapid congestion-risk identification and dispatching-oriented control under continuous and uncertain operating conditions. Full article

This study proposes a marker-assisted method for measuring railway platform position using forward-view images captured from in-service trains. Conventional monocular-image-based approaches have limited applicability to precise infrastructure measurement because they suffer from depth-related uncertainty. To mitigate this limitation, the proposed method uses installed ground markers on the platform and sleepers, known marker dimensions, measured installation offsets, and track geometry information. The selected marker reference lines and points define a local transverse measurement plane under near-frontal imaging conditions. The method consists of YOLO-based marker detection, lens-distortion correction, DIC-based marker localization, local pixel-to-metric scale conversion, and vector-based geometric calculation. Field experiments were conducted on an operational regional railway line. When lens-distortion correction and the marker-center-based reference were used, platform gap estimation achieved an MAE of 4.6 mm, an RMSE of 5.3 mm, and a maximum absolute error of 8.8 mm. Platform height estimation improved after lens-distortion correction, with the MAE reduced from 14.2 mm to 9.0 mm, although the maximum absolute error remained 21.2 mm. These results suggest the feasibility of platform gap monitoring under the tested straight-track and near-frontal imaging conditions. Full article

This paper investigates an adaptive pilot-assisted channel estimation framework for orthogonal frequency-division multiplexing (OFDM)-based high-speed railway (HSR) communications over non-stationary wideband channels. Within this framework, a channel-aware adaptive pilot insertion (CA-API) mechanism is combined with an linear minimum mean square error (LMMSE) shrinkage technique to adjust pilot density based on temporal channel variations. Using the refined pilot-domain observations, three time-domain channel estimators namely piecewise cubic Hermite interpolation (PCHIP), autoregressive (AR), and Gaussian process regression (GPR), are comparatively evaluated under measurement-based HSR channel models. Simulation results across Remote Area (RA), Closer Area (CEA), and Close Area (CA) conditions demonstrate that the benefit of adaptive pilot scheduling is strongly scenario-dependent. In RA and CEA, the CA-API scheme reduces overhead while maintaining channel reconstruction accuracy close to that of the fixed-pilot baseline, with average overhead reductions of 38% and 30%, respectively. Under the more dispersive CA condition, the adaptive mechanism tends to increase pilot density to preserve reliable channel tracking. Among the evaluated algorithms, GPR delivers the highest estimation accuracy, AR provides a balanced trade-off between accuracy and implementation complexity, and PCHIP is less accurate but remains attractive because of its low complexity. This study provides practical insights into the joint design of adaptive pilot scheduling and channel estimation for HSR wireless communication systems. Full article

Rail infrastructure plays an important role in freight and passenger mobility, and the assessment of rail track structure depends critically on understanding how the rail interacts with the supporting foundation. When rail support degrades (e.g., due to ballast fouling, settlement, etc.), the rail exhibits greater localized deformation that can lead to serious deleterious conditions. Track modulus represents a fundamental diagnostic measure of rail support, encompassing the vertical stiffness characteristics of the foundation and its resistance against downward rail movement. Existing track modulus characterization methodologies typically comprise deflection measurements of railway track (e.g., tie deflections) under known loads. Track modulus estimations result from analyzing deflection and load under assumptions of a traditional Winkler foundation, which can oversimplify mechanic relationships. Specifically, in the context of rail–ballast–subgrade interaction, a tensionless foundation permits gap development which can occur as track structure separates from the supporting ballast; additionally, track modulus may vary along the track length as conditions vary spatially. This paper presents a general analytical solution of ballasted track support characterization based on an iterative algorithm for the static response of a finite beam resting on a tensionless Winkler foundation. The method relates to multiple loads (e.g., concentrated axle loads and distributed self-weight), deflection along the track, and track condition through singularity functions, superposition of discrete support springs, and moment–curvature relationships. The model estimates rail deflections, lift-off points and shear and moment diagrams along the track. The technique permits: (1) validations against benchmark solutions and previously published results, (2) estimations of track modulus from known loads and measured deflections, and ultimately, (3) a framework for designing and processing sensor data streams for use in analyses and evaluations of railway track structure. Full article

Virtual coupling control of trains is a promising technology for improving railway capacity and operational efficiency. However, existing multi-agent reinforcement learning (MARL) approaches struggle to capture long-sequence temporal dependencies among train states in complex multi-train interaction scenarios, resulting in limited robustness and coordination stability. To address this issue, this paper proposes a Predictive Mamba-based Multi-Agent Soft Actor–Critic (PM-MASAC) framework. A Mamba-based state prediction module is embedded into the centralized Critic network to model historical state sequences and generate predictive state representations, thereby enhancing value estimation accuracy. In addition, a multi-agent aggregated prioritized experience replay (PER) mechanism is introduced to improve the utilization of critical cooperative samples and stabilize training. A hierarchical local–global reward structure is further designed to ensure individual tracking performance while promoting overall formation coordination. Experimental results under realistic railway operating conditions demonstrate that PM-MASAC achieves superior robustness compared with baseline MARL methods. Velocity and spacing tracking errors are maintained within 3% and 1%, respectively, and the steady-state formation success rate exceeds 95.7% in the training environment. Full article

Squat is a type of rail defect that frequently poses challenges for railway tracks, as they generate dynamics and accelerate track degradation. Detecting rail squats is resource-intensive, given their relatively small size compared to the railway track. Often, by the time they are detected, damage has usually already occurred in other track components. Currently, rail squats are primarily detected using dedicated railway measurement vehicles. There has been a recent trend in research towards utilizing trains in regular traffic to monitor the condition of railway tracks. However, there is a lack of research and general guidelines regarding the optimal placement of accelerometers or sensors on trains for squat detection. In this study, multibody simulation software GENSYS Rel.2209 is employed to simulate a passenger train traversing rail squats under various scenarios, with each scenario characterized by a distinct set of typical feature values for the squats. The results demonstrate that the front wheel set, positioned closest to the defects, exhibits the highest sensitivity to vertical accelerations. Squat length is much more sensitive than depth for detection at typical speeds, and accelerometers on bogies or the car body require speeds below 40 km/h to ensure reliability. The acceleration response mechanism during squat traversal is explored, revealing the effects of varying squat geometries and train speeds. This finding enables a detection method capable of locating squats and estimating their length with over 90% accuracy. Practical recommendations are provided for optimizing squat detection systems, including squat width detection, sensor selection criteria, and suggested train speeds. It offers a pathway to detect squat more efficiently with optimized installation locations of accelerometers on a train. Full article

Ensuring the safety and reliability of freight wagons requires continuous monitoring of couplings such as hooks and buffers, which are prone to stress, wear, and misalignments. This paper proposes a vision-based 3D monitoring system which uses an RGB-D camera and a computer vision pipeline to estimate angular excursions and longitudinal displacements of wagon couplers during train operation. The proposed approach combines depth-based reconstruction with a normalized cross-correlation tracking algorithm, providing geometric measurements of coupling motion without physical contact. The system architecture integrates real-time acquisition and post-processing analysis to 3D reconstruct the geometric characteristics of wagon couplings under field conditions. Experimental tests performed on a T3000 articulated wagon allowed us to measure an angular excursion of approximately 9.8° for the hook and a longitudinal displacement of 17 mm for the buffer. The results show robustness and suitability for embedded implementation, supporting the adoption of vision-based techniques for safety monitoring in railways. Full article

In this paper, the rail mean vertical alignment is estimated through double integration of wheel–rail contact forces measured using dynamometric wheelsets on a dedicated track recording vehicle (TRV). A simplified three degrees of freedom (DOF) linear model of half a train coach has been developed for this purpose. The model’s ability to simulate the average left and right longitudinal level has been tested using vertical contact force recordings from a constant speed track section, as measured by the TRV. The results are compared with available track geometry (TG) data, recorded by the optical system of the same vehicle, used for condition monitoring of the Italian railway infrastructure. Model parameters, such as masses, stiffness, and damping of the suspensive system have been optimized. An error analysis has been conducted on results. A good agreement is found between simulated and recorded vertical alignment at the D1 level, suggesting the feasibility of using contact forces measured with instrumented wheelsets for railway TG condition monitoring. This computationally efficient approach highlights the potential of strain gauges and instrumented wheelsets as alternative or complementary technologies to the widely adopted accelerometers, rate gyros, and optical devices for railway condition monitoring. Given its low computational cost, embedded and real-time TG estimation could be further investigated. Full article

In the operation of high-speed trains, the effective transmission of traction force heavily relies on the adhesion between the wheel and the rail. Excessive traction or braking force may exceed the adhesion limit, causing wheel creep or slide, which threatens both equipment and safety. To address this, a state estimation method based on the SVD-ACKF (singular value decomposition adaptive cubature Kalman filter) is proposed for high-precision estimation of train speed. Combined with an extremum-seeking algorithm, a closed-loop adhesion control strategy is developed to maintain train operations near the maximum adhesion point. Simulation results show that the method ensures accurate tracking under varying rail conditions and noise, while the control algorithm maintains adhesion utilization above 90%, thereby meeting operational demands and enhancing railway safety. Full article

The vertical stiffness of railway tracks is crucial for ensuring safe and efficient rail transport. Rail-pad dynamic stiffness is a key component influencing track performance. Determining the dynamic stiffness of rail pads poses a challenge because it depends not only on the material and geometry of the rail pad but also on the testing conditions, due to the non-linear material response. To address this issue, a methodology is proposed in this paper to estimate dynamic stiffness using static stiffness measurements. This approach enables the prediction of dynamic stiffness for different situations from a single laboratory test. This study further examines whether this correlation remains valid for different types of rail pads, even when their mechanical behavior has been degraded by temperature, wear, or chemical agents. Experiments were conducted under varying temperatures and on rail pads that underwent mechanical and chemical degradation. The analysis assesses the validity of the static-to-dynamic stiffness correlation under degraded conditions and investigates the influence of each testing condition on the ability to estimate dynamic stiffness from static stiffness and operational parameters. The findings provide insights into the reliability of this predictive model and highlight the impact of degradation mechanisms on the dynamic behavior of rail pads. This research enhances the understanding of rail pad performance and offers a practical approach for evaluating dynamic stiffness. By considering all of the variables used in the analysis, the approach achieves R² values of up to 0.99, which carries significant implications for track design and maintenance. Full article

This manuscript proposes a methodology to leverage digitalisation to efficiently generate an overview of required condition-based railway track interventions, possession windows, and expected costs for railway networks at the beginning of the intervention planning process. The consistent and efficient generation of such an overview not only helps track managers in their decision-making but also facilitates the discussion among other decision-makers in later phases of the track intervention planning process, including line planners, capacity managers, and project managers. The methodology uses data of different levels of detail, discrete state modelling for uncertain deterioration of components, and component-level intervention strategies. It dynamically updates the condition estimates of components by capturing the interaction between deteriorating components using Bayesian filters. It also estimates the risks associated with different types of potential service losses that may occur due to sudden events using fault trees as a function of time and the condition of components. An implementation of the methodology is conducted for a 25 km regional railway network in Switzerland. The results suggest that the methodology has the potential to help track managers early in the intervention planning process. In addition, it is argued that the methodology will lead to improvements in the efficiency of the planning process, improvements in the scheduling of preventive interventions, and the reduction in corrective intervention costs upon the implementation in a digital environment. Full article

Achieving high-precision spatiotemporal synchronization is crucial for the implementation of virtual coupling (VC) in railway systems. This paper proposes a moving baseline real-time kinematic (MB-RTK) framework to enhance relative positioning accuracy and synchronization robustness between coupled trains. By leveraging global navigation satellite system (GNSS) carrier-phase differential processing and dynamic baseline estimation, MB-RTK effectively mitigates positioning errors caused by GNSS signal degradation, multipath interference, and synchronization latency, ensuring stable and reliable inter-train coordination. The proposed framework was evaluated through comprehensive simulations and field experiments. The results demonstrate that MB-RTK achieves centimeter-level relative positioning accuracy under normal GNSS conditions, maintains tracking errors within 10 m, and typically keeps velocity synchronization deviations within ±0.5 km/h. Furthermore, the RTK status analysis reveals that NARROW_INT provides the highest stability, while continuous RTK corrections are essential to ensure seamless synchronization in dynamic environments. To further enhance synchronization performance, a decentralized distributed synchronization algorithm was introduced, reducing communication overhead and improving real-time responsiveness. The proposed approach exhibits strong resilience to GNSS disruptions, making it well-suited for high-density and autonomous train operations. Overall, this study highlights MB-RTK as a promising solution for VC applications, offering high accuracy, low latency, and strong adaptability in complex railway scenarios. Future research will focus on AI-driven dynamic corrections, integration with complementary localization methods, and large-scale deployment strategies to further optimize the system’s robustness and scalability. Full article

This study presents a machine learning approach for estimating the presence and extent of flange-face lubrication on a rail. It offers an alternative to the current empirical and subjective methods for lubrication assessment, in which track engineers’ periodic visual inspections are used to evaluate the condition of the rail. This alternative approach uses a laser-based optical sensing system developed by the Railway Technologies Laboratory (RTL) located at Virginia Tech in Blacksburg, VA, combined with a machine learning calibration model. The optical sensing system can capture the fluorescence emitted by the grease to identify its presence, while the machine learning model classifies the extent of grease present into four thickness indices (TIs), from 0 to 3, representing heavy (3), medium (2), light (1) and low/no (0) lubrication. Both laboratory and field tests are conducted, with the results demonstrating the ability of the system to differentiate lubrication levels and measure the presence or absence of grease and TI with an accuracy of 90%. Full article