Train Operation Research Articles

Analytical railway line capacity methods from graph-theoretical and polyhedral perspectives

ABSTRACT This paper rigorously addresses the consumed capacity underestimation caused by division-based methods in graph theory and polyhedral geometry. Graph-theoretically, such underestimation occurs because subgraphs in shorter sections approximate their combined graph. Our polyhedral study suggests that the capacity analysis is computationally ‘easy’ because its linear programme in the complete section has an orthogonal projection on the Euclidean plane containing five proper faces, one being the objective-defined one-dimensional optimal facet. Geometrically, we conclude that the polyhedra of the linear programmes in shorter and complete sections have parallel facets, with the former nested within the latter. Consequently, the optimal values of the former cannot exceed those of the latter. With these theoretical insights, we derive a train operation labelling algorithm that takes proven linear time. Beyond enabling faster capacity computation, the findings deepen our understanding of the connection between infrastructure capacity utilisation and timetable structure, ultimately leading to more efficient railway optimisation.

Research on Multi-Objective Optimization Methods of Urban Rail Train Automatic Driving Based on NSGA-II

In order to improve the control performance of automatic train operation (ATO) in urban rail trains, five typical operating sequences of urban rail trains were studied. Under the condition of meeting the safety and comfort principles of train operation, a train dynamics model was established to achieve the goals of low energy consumption, short running time, and high stopping accuracy in urban rail transit trains. In the process of finding a multi-objective solution to this problem, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was used with an elite retention strategy, and the optimal Pareto multi-objective solution set was sought. In the process of optimal solution weight assignment, the hierarchical analysis Mahalanobis distance method, which combines subjective and objective analysis, was used. Finally, taking the Beijing Yizhuang subway line as the background design example, the simulation verified the effectiveness and feasibility of the algorithm and obtained high-quality automatic train driving curves under various working conditions. This research has important reference significance for the actual operation of automatic driving in urban rail trains.

Lightweight Detection of Train Underframe Bolts Based on SFCA-YOLOv8s

Improving the accuracy and detection speed of bolt recognition under the complex background of the train underframe is crucial for the safety of train operation. To achieve efficient detection, a lightweight detection method based on SFCA-YOLOv8s is proposed. The underframe bolt images are captured by a self-designed track-based inspection robot, and a dataset is constructed by mixing simulated platform images with real train underframe bolt images. By combining the C2f module with ScConv lightweight convolution and replacing the Bottleneck structure with the Faster_Block structure, the SFC2f module is designed for feature extraction to improve detection accuracy and speed. It is compared with FasterNet, GhostNet, and MobileNetV3. Additionally, the CA attention mechanism is introduced, and MPDIoU is used as the loss function of YOLOv8s. LAMP scores are used to rank the model weight parameters, and unimportant weight parameters are pruned to achieve model compression. The compressed SFCA-YOLOv8s model is compared with models such as YOLOv5s, YOLOv7, and YOLOX-s in comparative experiments. The results indicate that the final model achieves an average detection accuracy of 93.3% on the mixed dataset, with a detection speed of 261 FPS. Compared with other classical deep learning models, the improved model demonstrates superior performance in detection effectiveness, robustness, and generalization. Even in the absence of sufficient real underframe bolt images, the algorithm enables the trained network to better adapt to real environments, improving bolt recognition accuracy and detection speed, thus providing technical references and theoretical support for subsequent related research.

Rapid and accurate identification of multiple metal ions using a bispyrene‐based fluorescent sensor array with aggregation‐induced enhanced emission property

AbstractMultiple metal ions are traditionally detected using inductively coupled plasma mass spectrometry (ICP‐MS) and atomic fluorescence spectrometry. Although these methods are sensitive and accurate, they depend on complex instruments and require highly trained operators, making low costly rapid detection challenging. It is urgent to develop a convenient, rapid and sensitive method to detect multiple metal ions. Herein, we designed a bispyrene derivative (BP) with aggregation‐induced enhanced emission (AIEE) property to construct a high fluorescent sensor array to realize the effective identification of four metal ions (Fe3+, Cu2+, Co2+ and Cd2+). The differential coordination capability between metal ions and BP with the aid of acetate ions resulted the possibility of array‐based sensing. The four heavy metal ions could be immediately classified in the concentration of 100 nM. The limit of detection (LOD) of Fe3+, Cu2+, Co2+, and Cd2+ were as low as 16.2, 21.8, 51.4, and 25.9 nM, respectively. Furthermore, the sensor array was applied for identification multiple heavy metal ions in environmental samples and iron ion in rat serums with identified of 100%. The sample consumption as low as 2 µL for each detection and the results could be extracted by smartphones under ultraviolet lamps. It provided a rapid, sensitive, low‐cost, and on‐site multiple metal ions detection method.

An Efficient Time‐Sensitive Networking Traffic Scheduling Method for Train Communication Network

As a critical system of rail train, the train communication network (TCN) is an integrated central platform that is used to realize the train operation control, condition monitoring, and data transmission. With the development of train intelligence, the scale of data flow in TCN continues to expand, and the control data and monitoring data must be real‐time transmitted to ensure the safe and reliable operation of the train. Time‐sensitive network (TSN) can be applied as the next‐generation TCN solution, which provides end‐to‐end data transmission with extremely low delay and high reliability based on Ethernet. For the application of TSN technology in TCN, a single frame simple scheduling method is proposed to effectively reduce the worst end‐to‐end delay of the entire network, which improve the real‐time performance of TSN‐based TCN. The network topology and data flow characteristics in TCN are analyzed, a series of scheduling constraints are constructed, and the satisfiability modulo theories (SMT) is used to obtain a gate control list (GCL) to ensure the real‐time performance of time‐trigger streams. Finally, the TCN simulation model is established using OMnet++ software, the experiment results show that the proposed method can effectively reduce the overall delay and jitter, and meet the real‐time requirement of TCN. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Numerical study on flow field of high-speed train passing through a new type of station

With the growing demand for efficient travel, tunnels are being lengthened and train speeds are being increased. High-speed subway brings new challenges to the flow environment of tunnel. Additionally, the emergence of express trains passing through stations without stopping has an enormous impact on the tunnel's and the station's flow field. This study focuses on a new type of station (NS) with overtaking and avoidance lines, allowing slow trains to stop for passengers to get on and off, while express trains pass through without stopping. The study analyzes pressure variation and wind speed when express trains pass through the NS without stopping. Different train speeds are taken into account. Based on this, the enhancement of station ventilation by utilizing the piston winds generated by express trains passing through NS is investigated. The air exchange effect and energy savings of the NS are explored. The results indicate that enhancing station air exchanges by fully utilizing piston wind saves about 190.68 kWh/day in mechanical ventilation energy consumption. This study contributes to the improvement of subway station construction and the advancement of subway train development. It can offer data assistance and theoretical direction for high-speed train operations.

The Evolution of Illicit-Drug Detection: From Conventional Approaches to Cutting-Edge Immunosensors-A Comprehensive Review.

The increasing use of illicit drugs has become a major global concern. Illicit drugs interact with the brain and the body altering an individual's mood and behavior. As the substance-of-abuse (SOA) crisis continues to spread across the world, in order to reduce trafficking and unlawful activity, it is important to use point-of-care devices like biosensors. Currently, there are certain conventional detection methods, which include gas chromatography (GC), mass spectrometry (MS), surface ionization, surface-enhanced Raman spectroscopy (SERS), surface plasmon resonance (SPR), electrochemiluminescence (ECL), high-performance liquid chromatography (HPLC), etc., for the detection of abused drugs. These methods have the advantage of high accuracy and sensitivity but are generally laborious, expensive, and require trained operators, along with high sample requirements, and they are not suitable for on-site drug detection scenarios. As a result, there is an urgent need for point-of-care technologies for a variety of drugs that can replace conventional techniques, such as a biosensor, specifically an immunosensor. An immunosensor is an analytical device that integrates an antibody-based recognition element with a transducer to detect specific molecules (antigens). In an immunosensor, the highly selective antigen-antibody interaction is used to identify and quantify the target analyte. The binding event between the antibody and antigen is converted by the transducer into a measurable signal, such as electrical, optical, or electrochemical, which corresponds to the presence and concentration of the analyte in the sample. This paper provides a comprehensive overview of various illicit drugs, the conventional methods employed for their detection, and the advantages of immunosensors over conventional techniques. It highlights the critical need for on-site detection and explores emerging point-of-care testing methods. The paper also outlines future research goals in this field, emphasizing the potential of advanced technologies to enhance the accuracy, efficiency, and convenience of drug detection.

Using three turbulence simulation methods for modeling the flying of high-speed railway tunnel lining blocks under piston airflow

The detached tunnel lining concrete blocks poses a serious threat to the safety of moving high-speed train. Understanding the flight characteristics of detached blocks from the tunnel soffit under train piston airflow is essential for devising appropriate measures to mitigate such risks during train operation. Computational Fluid Dynamics (CFD) simulation methods, known for their high efficiency and good repeatability, are effective approaches to study this subject. Large Eddy Simulation (LES), Improved Delayed Detached Eddy Simulation (IDDES), and Unsteady Reynolds-Averaged Navier-Stokes (URANS) are three commonly used turbulence simulation methods in the CFD simulation of train/tunnel aerodynamics. In this study, a three-dimensional CFD simulation model based on the three turbulence simulation methods is established for the airflow-tunnel-train-detached block system, and an experiment is conducted for validating the CFD model. Using the established models, the influence of different turbulence simulation methods on the flight trajectory and aerodynamic coefficients of detached lining blocks under train-induced airflows is compared. By visualizing the macroscopic flow field inside the tunnel and the local flow field near the detached block, the flow mechanisms of detached blocks under different turbulence simulation method conditions are revealed. The results showed that in the 1st stage (BTT stage), the flight of the detached block is influenced by the airflow near the train body, while in the 2nd stage (ATT stage), it is mainly affected by the train wake vortex. Compared with experimental results, the errors of LES and IDDES in simulating the longitudinal direction displacement of the block are only 2.3% and 5.5%, respectively, while the error of URANS is 10.6%. The simulation error of URANS for the longitudinal direction flight speed reached 8.8% in the ATT stage. In the BTT stage, LES and IDDES predicates more and larger leeward side vortex structures of the detached block, while URANS predicates fewer and smaller vortex structures. Compared with URANS method, the dissipation rate of the vortex structures simulated by LES and IDDES in the ATT stage is slower. These are the main reasons explaining why the flight characteristics of detached lining blocks obtained by URANS method are different from the LES and IDDES methods.

Endoscopic Removal of Uncovered Metallic Airway Stents.

Self-expandable uncovered metallic stents (SEUMS) have been used in benign tracheobronchial stenosis. Stent complications may require risky removal due to SEUMS integration in the tracheobronchial wall. Our study aims to report techniques, including a novel one, and outcomes of SEUMS removal by rigid bronchoscopy. We studied a case series of 7 patients from a tertiary medical center, who underwent SEUMS removal from 2017 to 2022. SEUMS removals were performed through rigid bronchoscopy. We used a new technique with bronchoscopic hook scissors to gradually dissect the stent from the airway wall. Nine SEUMS were removed from 7 patients. The mean duration of stenting was 7 years (5 to 12y). Indications for stent removal included mostly recurrent pulmonary infections, obstructive granulation tissue, and stents' fractures. We used the usually described technique in the literature known as the "piecemeal fashion" without dissection in 4 SEUMS removals and a new dissection technique in 5 procedures. We reported one major intraoperative complication using the former technique in the form of tracheal tear with mediastinal breach, while we did not experience any major complications with the later one. One patient died 8 days after surgery from respiratory failure. Dissection and "piecemeal fashion techniques" can be used to remove SEUMS by rigid bronchoscopy even after long-term stenting. SEUMS removal is a very risky procedure and must be performed by a trained operator. Patients should be aware of possible severe complications.

Analysis of the influence of brake pipe pressure gradient on heavy haul combined train and optimisation of operation strategy

In this paper, based on the theory of gas flow, the action logic of the control valve, and the principle of multi-rigid-body dynamics, a simulation model of the 20,000 t heavy haul combined train is established to investigate the influence of brake pipe pressure gradient on the braking performance, longitudinal dynamics, braking and release characteristics of trains. Utilizing real railway line conditions, locomotive and vehicle parameters, and train operation monitoring equipment (LKJ) record data of Shuozhou-Huanghua Railway, the cyclic braking condition of the train on the long and steep downhill is simulated, and the control strategy of reducing the coupler force during the cyclic braking is proposed. The results indicate that a larger brake pipe pressure gradient before braking leads to weaker braking capability, and higher coupler forces during braking, but smaller coupler forces during release. As the brake pipe pressure gradient before braking increases, braking synchronicity decreases, while release synchronicity slightly improves. By adjusting the locomotive’s electric braking force during the cyclic braking, it is possible to appropriately increase the brake pipe pressure gradient before braking, effectively reducing coupler forces during release and reducing the number of braking cycles, thus lowering the operational difficulty of the train.

Effects of canyon wind speed and angle on the aerodynamic performance of a high-speed train passing through canyon tunnel–bridge–tunnel infrastructure

A significant airflow acceleration effect generated by canyon terrain and the bridge poses a serious threat to the safety of train operation in the canyon wind zone. The scale-resolving hybrid turbulence model and overset mesh technology have been employed to investigate the aerodynamic performance of the train traversing a tunnel–bridge–tunnel infrastructure under the canyon wind. Meanwhile, the mechanism of aerodynamic load variation is explored in combination with the characteristics of wind field distribution. The results indicate that the wind speed reaching the windward track of the bridge is about twice the wind speed of far-field inflow. The air within both tunnels will be sucked toward the center of the canyon. The accelerated flow area outside the tunnel portal leads to sudden changes in the lateral force and overturning moment of the train, with the most significant occurring in the head car. The peak of the lateral force and overturning moment coefficients are the highest at wind angles of approximately 60° and 120°, while smaller at 90°, exhibiting an overall approximate “M-shaped” variation pattern. The peak of the sudden change in lift coefficient is later than that of the lateral force coefficient, indicating a lag phenomenon. The direction of vortex shedding is roughly the same as the direction of the composite velocity of train-induced wind and canyon wind, except at the tunnel portal. The research results can provide a reference for the safety of train operation and the design of wind barrier facilities in canyon areas.

Entropy Stacked Autoencoder based Diffusion Model (ESADM) and Fuzzy Clustering with Semi Supervised Fuzzy Graph Convolutional Network (FC-SSFGCN) for Rail Surface Defect Detection

Rails, fasteners, and other parts of railway track lines eventually develop flaws due to continuous strain from train operations and direct exposure to the environment; these faults directly affect the safety of train operations. Detecting defects on rail surfaces presents a formidable challenge due to the wide array of possible flaws and their unpredictable nature. However, Defect identification errors, massive variances, inadequate training sample availability, and weak contrast between faults and the surrounding background all contribute to the complexity of this procedure. In this work, rail surface and fastener flaws may be detected non-destructively using a multi-crack detection approach based on the Entropy Stacked Autoencoder Diffusion Model (ESADM). A novel approach, ESAFM, has been developed, combining a rail surface image encoder with a multi-layer, Stacked Autoencoder to extract latent materials from images showcasing different types of cracks. This integration reduces the need for significant processing resources by integrating easily into the conventional physically-based image workflow. Additionally, the Zero Shot-Semi Supervised Fuzzy Class Knowledge Graph (ZS-SSFCKG) method proposes Class Knowledge Graph Construction (CKGC), which constructs a CKG to elucidate the connection between defects and non-defects. Class features are learned by using a Fuzzy Clustering with Semi Supervised Fuzzy Graph Convolutional Network (FC-SSFGCN). The method employs a transformer encoder to capture distant relationships, allowing for the extraction of features from defect samples. This facilitates the acquisition of distinct defect image characteristics, as industrial defects vary in shape and size. The experimental results on the public Railway Track Fault Detection (RTFD)and Rail Surface Defect Datasets (RSDDs) with rail surface defects are collected from rail tracks surface defect detection.

Selected applications of satellite technologies in rail transport

Global Navigation Satellite Systems (GNSS) are increasingly being used in various modes of transport, including rail transport. When this technology is applied to railway traffic control, it is essential to ensure a high level of safety and reliability. The approval of a railway traffic control system requires a safety analysis, which includes hazard analysis and risk analysis. This also includes GNSS-based solutions in terms of their compliance with safety integrity requirements, i.e. THR (Tolerable Hazard Rate) and SIL (Safety Integrity Level) parameters, as defined in normative documents. In the case of railway traffic control systems, the level of dependability of the determined train position, referred to as position integrity, is very important in ensuring safety. Position integrity is affected by many factors, including: errors due to SIS (Signal-In-Space) propagation, multipath errors, signal interference or GNSS receiver errors. In order to improve position integrity, among other things, new data processing methods can be used to improve the accuracy and reliability of measurements. The paper presents the concept of satellite signal processing for precise determination of the position of objects in selected railway systems. The Kalman filter based model of satellite signal filtration and its selected applications, which were tested in the real condition, was presented. The application of Kalman filtration indicated in the paper is a universal method that improves the estimated measurement parameters and can be used in many applications of satellite systems for railway tasks. The applicability of satellite systems to automatic train operation, defect positioning in automatic and manual flaw detection tests, determining track spatial orientation and train integrity control have been considered. The conducted tests confirmed the correctness of the adopted concept and the model of satellite signals filtration developed for this purpose. According to the authors, the described methods can also be used in many other tasks related to rail transport.

Reinforced performance control for automatic train operation with speed measurement deviation: a recollection observer approach

Reinforced performance control for automatic train operation with speed measurement deviation: a recollection observer approach

Research on damage characteristics and contact interface evolution behavior of double-block ballastless track considering tunnel floor heave

Excessive railway tunnel floor heave (TFH) will reduce the durability of the track structure and jeopardize train operation safety. The TFH characteristics were fitted into cosine and bilinear curves according to the monitoring data. A nonlinear failure analysis model of double-block ballastless track under TFH was established. The deformation transfer law, structural damage mechanism and the interlayer bonding interface failure evolution of the track structure under different TFH characteristics were explored. The results show that the deformation of TFH can be well mapped to the track. The amplitude transfer ratio is less than 100 %. The maximum wavelength transfer ratio under the cosine and bilinear TFH is 129.3 % and 127.5 %, respectively. To avoid the damage of track structure, when the wavelength is 10 m, the amplitude of cosine and bilinear TFH should be controlled at 2.5 mm and 0.5 mm respectively. When the wavelength is greater than 10 m, the amplitude can be appropriately increased. To avoid interlayer bonding cracking, the cosine and bilinear amplitudes with a wavelength of 10 m should be controlled at 5 mm and 1.5 mm, respectively. The track-tunnel interlayer debonding failure under the cosine curve occurs at the edge of the TFH, while the bilinear curve occurs at the center and edge of the TFH. The gaps under the cosine and bilinear TFH exhibit double-peak and multi-peak shapes, respectively. This study can provide theoretical guidance for controlling the performance degradation of track structure caused by TFH.

Decentralized learning control for high-speed trains with unknown time-varying speed delays

Treating the multi-point-mass dynamic model of high-speed trains as an interconnected system, this study proposes a decentralized iterative learning control scheme for high-speed trains to achieve the trajectory tracking goal. By making reasonable estimates of the interaction term and compensating for it, the proposed control scheme utilizes only local information from each carriage and does not need any inter-carriage information exchange. The zero-error tracking of the desired trajectory is guaranteed even in a restricted communication environment. Considering unknown time-varying speed delays in the actual high-speed train operations, a modified decentralized iterative learning control scheme is also provided to address the negative impact of speed delays. The convergence of tracking errors is strictly proven by constructing appropriate composite energy functions. Numerical simulations further verify the theoretical results.

EFFECTS OF CROSSWINDS ON THE FLOW STRUCTURE AROUND A NEXT-GENERATION HIGH-SPEED TRAIN EXITING A TUNNEL

This study investigates the effect of crosswind on the aerodynamic behavior of the Next-Generation High-Speed Train (NG-HST) model when exiting a tunnel. Utilizing a 1/25th scale model, three distinct train exit positions and four crosswind yaw angles (15°, 30°, 45°, and 60°) were considered. Computational Fluid Dynamics (CFD) simulation with the k-ε turbulence model was used to simulate the flow structure around the train at the crosswind velocity of 25 m/s. A mesh independence study was performed to ensure that the results were not influenced by mesh sizing. Furthermore, existing wind tunnel experiment data was used to validate the mesh setup. The results showed that the crosswind angle had a significant effect on the aerodynamic flow behavior of the train as it exited the tunnel. The airflow pattern on the train surface exhibited significant changes, and variations in pressure distribution were observed on both the leeward and windward sides of the train. Moreover, the cars outside the tunnel experienced greater pressure than the cars inside the tunnel. As the last car of the train emerged from the tunnel, the strong crosswind altered the vortex structure, particularly around the head car, emphasizing its critical role in ensuring safe train operation. The results can be used to improve the design of wind barriers and other infrastructure along the railway tracks to improve the operational stability and safety of high-speed trains.

A novel optimal resonance band selection method for wheelset-bearing fault diagnosis based on tunable-Q wavelet transform

The detection of faults in the wheelset-bearing system is crucial for guaranteeing the safety of train operations. The core is to extract the optimal resonance band (ORB) and repetitive transient impact signals from the collected axle box acceleration signals. Accordingly, a novel automatic fault detection method called the bidirectional iterative merging multi-Q tunable-Q wavelet transform (BIMMQTQWT) is proposed to address the issue that existing methods are vulnerable to background noise and irrelevant components. First, a series of band-pass filters with almost constant bandwidth are constructed by the improved multi-Q tunable-Q wavelet transform (IMQTQWT) derived from the fault characteristics. Second, the fault information contained in each sub-band coefficient is preliminarily estimated using the correlative envelope comprehensive indicator (CECI). Third, the ORBs are automatically selected using the maximum CECI based on a strategy called bidirectionally merging adjacent frequency bands (BIMFBs). Finally, Envelope demodulation based on the ORB is executed followed by identifying bearing faults. The effectiveness in detecting multiple wheelset-bearing faults of the proposed method is validated through simulation and bench experiment signals. And the superior performance of the proposed method is exhibited compared with the existing average infogram and resonance sparse decomposition.

Picking up railway track vibration energy using a novel doughnut-shaped piezoelectric energy harvester

Railway transportation is essential for modern infrastructure, and ensuring safety through real-time monitoring of track conditions is critical. However, providing cost-effective and stable power for sensors in remote areas remains a challenge. This paper presents a novel doughnut-shaped piezoelectric energy harvester designed to address this issue. Under a sinusoidal excitation of 50N and 110Hz, the harvester achieves an output power of 0.047 mW, which increases linearly with load. At optimal resistance (230 kΩ) and a load of 3 kN at 7Hz, the output voltage reaches 37.61V, resulting in an output power of 7.23 mW. In track vibration conditions, the harvester produces an output power of 207.67 mW, making it suitable for Internet of Things (IoT) applications, such as powering acceleration and speed sensors to monitor train operations and ensure track safety.

Industry 4.0 for passenger railway companies: A maturity model proposal for technology management

The railway sector is a relevant segment for the transportation of people and goods, and its efficiency and sustainability are crucial for fostering economic development and minimizing environmental impact. This research proposes an Industry 4.0 maturity model for passenger railway companies to manage emerging technologies, aiming to enhance them holistically to leverage the internal and external business dimensions. An exploratory and qualitative approach was employed, involving semi-structured interviews with experts in Brazilian railway transport. Based on the opportunities and Key Performance Indicators identified within the sector, we developed an Industry 4.0 maturity model for companies and subsequently tested it in collaboration with a transport operator. The findings suggest that the proposed Industry 4.0 maturity model can effectively guide practitioners in applying advanced technologies for robust and efficient train operations, prescriptive maintenance, strong supply chain management, and improved passenger experience and worker performance. This research provides a noteworthy and substantial contribution by introducing unprecedented frameworks that offer a holistic view of Industry 4.0 in the context of passenger railways. The study's practical impact is aiding passenger railway companies to navigate their Industry 4.0 journey. Academically, the research contributes to advancing the holistic understanding of Industry 4.0 as an ongoing phenomenon, enriching the academic discourse by discussing published works and presenting empirical data from railway companies.