Search Results (754)

The Jiangsu Yangtze River city cluster is a key growth pole of the Yangtze River Economic Belt, yet substantial disparities in development levels persist across cities, and the role of rail transit investment in fostering regional economic coordination remains insufficiently understood. This study aims to reveal the dynamic mechanisms through which railway transportation investment influences regional economic growth via population migration and service industry agglomeration, and to quantify the economic multiplier effects under different investment scenarios. Considering the close economic linkages among cities, spatial autocorrelation analysis is applied to assess intercity economic dependence, which provides the basis for developing a system dynamics model that links the rail transit system with the regional economy. Using data from eight core cities over the period 2014–2023, the model is employed to simulate long-term economic responses under different investment scenarios. The results indicate that increasing the rail transit investment ratio from 0.0077 to 0.02 is associated with an estimated 13.2% increase in regional GDP by 2030, with a corresponding economic multiplier of approximately 1.8, while simulation errors remain within 4.1–16.2% compared with historical data. The findings suggest that rail transit investment promotes regional growth through improved accessibility, factor agglomeration, and industrial upgrading, and that coordinated planning at the urban agglomeration scale is more effective than isolated city-level strategies. By integrating spatial dependence analysis with system dynamics modeling, this study offers a dynamic perspective on the regional economic impacts of rail transit investment. Full article

This study presents a comprehensive structural optimization workflow for a railway motor bogie frame, aimed at developing an innovative and lightweight design compliant with the reference European standards. The methodology integrates a two-stage topology optimization process, supported by an extensive numerical simulation campaign and a dedicated sensitivity analysis to identify the most critical load scenarios. In the first optimization stage, a global evaluation of the frame performance revealed that increasing the number of optimization parameters leads to a rise of approximately 50% in solver iterations. Symmetry constraints proved essential for simplifying both the optimization and the subsequent geometric reconstruction. The minimum feasible feature dimension strongly affected the final solution, modifying the material distribution and enabling a mass reduction of about 18%. The second optimization stage, focused on the cross beams, highlighted the relevance of manufacturing constraints in guiding the solver toward practical configurations. Static and fatigue assessments confirmed stress distributions consistent with the original frame, providing designers with a reliable basis for future material upgrades. Finally, the dynamic analysis showed a first natural frequency above 60 Hz, with variations in the first eigenvalue within 1% and preservation of the local flexural mode shape, ensuring full compatibility with the original frame interfaces and enabling seamless replacement with the optimized configuration. Full article

The development of digital twins for railway freight vehicles represents a key step toward more efficient, data-driven maintenance and safety assessment. This study focuses on the creation of a digital twin of the T3000 articulated freight wagon, one of the most widespread intermodal transport solutions in Europe. Despite its relevance, the dynamic behavior of this vehicle type has been scarcely investigated so far in scientific literature. A dedicated onboard measurement layout was defined to enable comprehensive monitoring of vehicle dynamics and the interactions between adjacent wagons within the train. The experimental setup integrates inertial sensors and a 3D vision system, allowing for detailed analysis of both rigid-body and vibrational responses under real operating conditions. A high-fidelity multibody model of the articulated wagon was developed and tuned using the acquired data, achieving optimal agreement with experimental measurements in both straight and curved track segments. The resulting model constitutes a reliable and scalable digital twin of the T3000 wagon, suitable for predictive simulations and virtual testing. Future developments will focus on a deeper investigation of the buffer interaction through an additional experimental campaign, further extending the digital twin’s capability to represent the full dynamic behavior of articulated freight trains. Full article

The distributor valve is one of the most important components in the pneumatic braking system of trains. It performs the functions of filling and releasing the brake cylinder. The distributor valve most widely used on Bulgarian railways operates in two positions, respectively, in “freight train” mode (G) and in “passenger train mode” (P). The difference between them is determined by the different times for filling and emptying the brake cylinder. These times affect the moment of engagement of the braking system of each wagon in the train composition. This has a significant impact on the longitudinal forces obtained in the couplers. This paper is dedicated to the analysis of the influence of the distributor valve position on the longitudinal forces. A simulation study of the longitudinal behavior of a train set was carried out in Simulink^®, which consists of a locomotive and 43 freight wagons attached to it, with 80 t gross mass of each wagon. The railway cars are linked by elastic elements with nonlinear characteristics. The results represent the distribution of longitudinal forces in time. They are used for the investigation of the longitudinal dynamics of the train, with the aim of improving the running-dynamic qualities of the train during braking. Full article

Feeder buses play an important role in supporting the accessibility of high-speed railway stations which leads to the improved efficiency of the transportation system. This paper proposes a new optimization technique for the design of feeder bus routes to the stations. It uses dynamic programming with a pulse algorithm seeking to maximize the number of serviced people considering the distance between the urban areas and high-speed railway station. The proposed algorithm was tested in a hypothetical network to find the optimum solutions and the running time needed. Moreover, the algorithm was applied to a real network as a case study in Aswan city, Egypt. Our results demonstrated significant improvements in the route design accuracy and efficiency. By applying the proposed algorithm, the potential demand values increased from 19.8% to 37.9% with a reasonable decrease in the running time compared to the literature. This research contributes to the advancement of transportation planning strategies by providing valuable insights into the optimization of feeder bus systems. The proposed model contributes to the scientific re-search and practical implementation by promoting a coordinated development of high-speed railway stations and urban areas. This may enhance the Egyptian high-speed railway technology, yielding substantial economic and social benefits. Full article

To address the insufficient multi-layer optimization of fastener and cushion stiffness in ballastless tracks for mixed passenger and freight railways, a vehicle–track coupled dynamic model is developed, and the effects of individual and combined stiffness parameters on track and vehicle dynamics are systematically analyzed. Based on this model, a multi-dimensional stiffness matching approach is proposed to determine appropriate stiffness ranges for mixed-use railways. Results indicate that fastener stiffness primarily affects the local dynamic response of the rail, whereas cushion stiffness has a stronger influence on overall track performance. When the damping pad stiffness exceeds 600 MPa/m, the fastener force increases sharply, posing a risk of accelerated structural deterioration. Differences in axle load and speed between passenger and freight trains induce distinct excitation patterns, leading to nonlinear variations in interlayer forces. The optimal stiffness combination is 50 kN/mm for fasteners and 600 MPa/m for damping pads under passenger conditions, and 40 kN/mm and 600 MPa/m, respectively, under freight conditions. Considering the operational requirements of mixed lines, a fastener stiffness of 40–50 kN/mm and a damping pad stiffness of 600 MPa/m are recommended. This study provides theoretical support for stiffness design and parameter optimization in ballastless tracks for mixed-use railways. Full article

Ray tracing (RT) has become an important method for train-to-ground (T2G) wireless channel modeling due to its physical interpretability. In rail transit scenarios, RT suffers from modeling errors that arise due to environmental reconstruction and uncertainties in electromagnetic parameters, as well as dynamic phase errors caused by coherent multi-path superposition that is further triggered by such modeling errors. Phase errors significantly affect both the calibration accuracy and prediction precision of RT. Therefore, this paper proposes an intelligent RT calibration method based on local phase errors. The method builds a phase error distribution model and uses constraints from limited measurements to explicitly estimate and correct phase errors in RT-generated channel responses. Firstly, the method applies the Variational Expectation–Maximization (VEM) algorithm to optimize the phase error model, where the expectation step derives an approximate posterior distribution and the maximization step updates parameters conditioned on this posterior. Secondly, experiments are conducted using differentiable RT implemented in the Sionna library, which explicitly provides gradients of environmental and link parameters with respect to channel frequency responses, enabling end-to-end calibration. Finally, experimental results show that in railway scenarios, compared with calibration methods based on phase error-oblivious and uniform phase error, the proposed approach achieves average gains of about 10 dB at SNR = 0 dB and 20 dB at SNR = 30 dB. Full article

Reliable state estimation in railway environments presents significant challenges due to geometric degeneracy resulting from repetitive structural layouts and point cloud sparsity caused by high-speed motion. Conventional LiDAR-based SLAM systems frequently suffer from longitudinal drift and mapping artifacts when operating in such feature-scarce and dynamically complex scenarios. To address these limitations, this paper proposes SERail-SLAM, a robust semantic-enhanced multi-sensor fusion framework that tightly couples LiDAR odometry, inertial pre-integration, and GNSS constraints. Unlike traditional approaches that rely on rigid voxel grids or binary semantic masking, we introduce a Semantic-Enhanced Adaptive Voxel Map. By leveraging eigen-decomposition of local point distributions, this mapping strategy dynamically preserves fine-grained stable structures while compressing redundant planar surfaces, thereby enhancing spatial descriptiveness. Furthermore, to mitigate the impact of environmental noise and segmentation uncertainty, a confidence-aware filtering mechanism is developed. This method utilizes raw segmentation probabilities to adaptively weight input measurements, effectively distinguishing reliable landmarks from clutter. Finally, a category-weighted joint optimization scheme is implemented, where feature associations are constrained by semantic stability priors, ensuring globally consistent localization. Extensive experiments in real-world railway datasets demonstrate that the proposed system achieves superior accuracy and robustness compared to state-of-the-art geometric and semantic SLAM methods. Full article

With the rapid expansion of high-speed railway (HSR) networks, the vibration impact on adjacent energy infrastructure has become a critical safety concern. However, existing research lacks a comprehensive evaluation of buried sour gas pipelines specifically in tunnel-undercrossing scenarios. This research investigates the dynamic response characteristics of a sour natural gas pipeline under train-induced vibration loads using a case study in Chongqing. A three-dimensional dynamic coupling model of the track lining soil pipeline system was established based on FLAC-3D. The study innovatively quantifies the vibration superposition effect during bidirectional train encounters and assesses safety using fatigue life and velocity thresholds. Results indicate that pipeline vibration is predominantly vertical. As train speed increases from 250 km/h to 350 km/h, the response exhibits a non-linear rapid growth within the 300–350 km/h range. Under bidirectional encounters, the peak displacement reaches 2.00 times that of unilateral passage, representing the most critical load condition. The maximum peak vibration velocity is 0.1 mm/s, far below the 2 mm/s safety threshold, ensuring structural integrity under current operational standards. Full article

This study presents the development and high-fidelity finite element modelling of an innovative hybrid railway carbody structure, designed to achieve a substantial reduction in mass while maintaining the required mechanical performance under service conditions. The proposed concept integrates a traditional aluminium frame with an advanced honeycomb sandwich panel, joined through adhesive bonding to ensure structural continuity, compensate for thermal effects, and minimize over constraining stresses. Detailed numerical simulations were conducted to evaluate both the static and dynamic behaviour of the structure under the most demanding load cases prescribed by standards. Modal analysis showed excellent agreement with the original carbody, with variations in the first natural frequency about 3%, while a change in the nature of the corresponding eigenvector was observed. Static simulations under maximum vertical loading confirmed comparable stiffness and stress distributions. Localised stress peaks increased by approximately 19%; the corresponding material utilization factor remained below unity, demonstrating that the structure operates safely within its allowable limits. The introduction of the sandwich panel enabled a mass saving of approximately 60% in the replaced components, corresponding to 3.9% if referred to the whole structure. The results validate the structural feasibility and mechanical reliability of the proposed hybrid concept, laying the foundations for the subsequent experimental phase and for refining its predictive accuracy and industrial applicability. Full article

Accurate point cloud registration is a fundamental prerequisite for reality-based 3D reconstruction and large-scale spatial modeling. Despite significant international progress, reliable registration in architectural and urban scenes remains challenging due to geometric intricacies arising from repetitive and strongly symmetric structures and photometric variability caused by illumination inconsistencies. Conventional ICP-based and color-augmented methods often suffer from local convergence and color drift, limiting their robustness in large-scale real-world applications. To address these challenges, we propose Hybrid Adaptive Residual Optimization (HARO), a unified framework that organically integrates geometric cues with hue-robust color features. Specifically, RGB data are transformed into a decoupled HSV representation with histogram-matched hue correction applied in overlapping regions, enabling illumination-invariant color modeling. Furthermore, a novel adaptive residual kernel dynamically balances geometric and chromatic constraints, ensuring stable convergence even in structurally complex or partially overlapping scenes. Extensive experiments conducted on diverse real-world datasets, including Subway, Railway, urban, and Office environments, demonstrate that HARO consistently achieves sub-degree rotational accuracy (0.11°) and negligible translation errors relative to the scene scale. These results indicate that HARO provides an effective and generalizable solution for large-scale point cloud registration, successfully bridging geometric complexity and photometric variability in reality-based reconstruction tasks. Full article

The study investigates the combined effects of material selection, manufacturing constraints, and a dynamic stress constraint function on the resulting material distribution achieved through a structural optimization process, while ensuring full compliance with the relevant European assessment standards for railway bogie. A high-fidelity finite element model of the complete bogie system was developed to accurately reproduce the operational loads and the structural interactions between the motor support and its surrounding components. The proposed methodology integrates topology optimization within a manufacturability-oriented framework, enabling a systematic evaluation of the influence of material properties, draw direction, and minimum feature size on the optimized configuration. In this context, an adaptive stress coefficient, derived from the performance of the original component, was introduced and proved effective in improving both the material distribution and the resulting stress levels of the optimized design. The results demonstrate that the combined consideration of material selection, manufacturing constraints, and adaptive stress control leads to a structurally efficient and production-feasible design. Three different materials were tested, showing consistent stress distributions and mass savings across all cases. The innovative optimized configuration achieved over 16% mass reduction while maintaining admissible stress levels. The proposed approach provides a generalizable and standard-compliant framework for future applications of topology optimization in railway engineering. Full article

Magnetic Barkhausen noise (MBN) represents a powerful non-destructive testing and material characterization methodology enabling quantitative assessment of microstructural features, mechanical properties, and stress states in ferromagnetic materials. This comprehensive review synthesizes recent advances spanning theoretical foundations, sensor design, signal processing methodologies, and industrial applications. The physical basis rooted in domain wall dynamics and statistical mechanics provides rigorous frameworks for interpreting MBN signals in terms of grain structure, dislocation density, phase composition, and residual stress. Contemporary instrumentation innovations including miniaturized sensors, multi-parameter systems, and high-entropy alloy cores enable measurements in challenging environments. Advanced signal processing techniques—encompassing time-domain analysis, frequency-domain spectral methods, time–frequency transforms, and machine learning algorithms—extract comprehensive material information from raw Barkhausen signals. Deep learning approaches demonstrate superior performance for automated material classification and property prediction compared to traditional statistical methods. Industrial applications span manufacturing quality control, structural health monitoring, railway infrastructure assessment, and predictive maintenance strategies. Key achievements include establishing quantitative correlations between material properties and stress states, with measurement uncertainties of ±15–20 MPa for stress and ±20 HV for hardness. Emerging challenges include standardization imperatives, characterization of advanced materials, machine learning robustness, and autonomous system integration. Future developments prioritizing international standards, physics-informed neural networks, multimodal sensor fusion, and wireless monitoring networks will accelerate industrial adoption supporting safe, efficient engineering practice across diverse sectors. Full article

With the development of heavy-haul trains towards long formation and large axle load, the longitudinal impulse problem of trains is aggravated not only by improving the transport capacity of railway freight cars, but also by the braking characteristics such as the asymmetry in brake release, which has a greater impact on the longitudinal impulse of trains, seriously affecting the operation safety of trains. In this paper, a 20,000-ton heavy-haul train is taken as the research object, a train air brake system model is established by the parallel method, and the train longitudinal dynamics model is co-simulated to study the influence of braking characteristics on the longitudinal force of the train. The results indicate that the train is primarily subjected to compressive coupler forces during braking, with the maximum compressive force occurring at car 109. Compared to the maximum compressive coupler force observed under a 50 kPa reduction in brake pipe pressure, the maximum forces under 70 kPa and 100 kPa reductions increased by 16.8% and 36.8%, respectively. The controllable tail system influences the braking of middle and rear cars by supplying a braking source to the last car. When the delay time of the controllable tail system is set to 3 s, braking synchronization can be improved. Furthermore, compared to scenarios without last-car charging, the installation of a last-car charging device reduces the maximum tensile coupler force from 780 kN to 489 kN, representing a 37% decrease. The findings of this study provide theoretical insights for ensuring the safe operation of heavy-haul trains and contribute to enhancing their operational performance. Full article

The existing crew base assignment system in high-speed railway operations struggles to cope with the frequent deployment of additional and coupled trains under the “One-Day-One-Operation Plan” dynamic scheduling paradigm. This often results in unequal overtime distribution among crews, low scheduling efficiency, and limited operational adaptability. To address the above-mentioned application challenges, this study proposes a shift from the fixed crew-based system towards a fully flexible pool-based system. Specifically, we develop a novel integer programming model designed to optimize monthly crew schedules with the primary objective of balancing total working hours across all crew teams. In this model, crew teams are treated as unified entities but are no longer permanently tied to specific train services. Instead, they are dynamically allocated to all available train tasks within the network. Numerical results, based on a real-world case study from Shanghai, China, demonstrate that the proposed model effectively automates the scheduling process. It significantly enhances fairness in working hour distribution while fully complying with all operational rules. Furthermore, by enabling crews to undertake a diverse range of services, the model substantially improves the flexibility of human resource allocation and the overall robustness of the crew management system. This research provides an efficient and scientific decision-support tool for tackling crew scheduling difficulties in dynamic railway operations. Full article