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Accurately modeling fastener lateral dynamics and its parameters is crucial for predicting stick–slip vibrations on curved tracks and rail corrugation defects. Nonetheless, the complex lateral force transmission within fasteners, leading to nonlinear stiffness characteristics, poses a challenge to the accuracy of conventional linear spring-damper models like the Kelvin-Voigt model (KV model) in representing the true behavior of fasteners. Hence, this study presents a theoretical dynamic model that incorporates the nonlinear lateral stiffness characteristics of fasteners and investigates its impact on wheel-rail stick-slip vibrations. Using segmented fasteners employed in subway systems as a case study, a series of indoor experiments is conducted to evaluate the nonlinear lateral stiffness characteristics of the fastener system. Then through finite element simulations of lateral force transmission in fasteners, these characteristics are classified into three stages: shear, slip, and collision, with a particular focus on the influence of elastic pad friction coefficients at each stage. Subsequently, A theoretical dynamic model for fastener lateral stiffness is developed by combining the Kelvin-Voigt model with the Berg friction model. This model is integrated into a vehicle-track spatial coupling model to formulate a predictive model for wheel-rail stick-slip vibrations on curved track sections. Lastly, the model's accuracy is verified for a specific operational scenario involving a subway curve segment by comparing measured rail displacements with simulation outcomes. Furthermore, the study explores the impact of nonlinear lateral stiffness characteristics of fasteners on wheel-rail stick-slip vibrations. The findings indicate that the occurrence of lateral slip in fasteners markedly decreases lateral damping, leading to high-frequency oscillations in rail stick-slip forces. Consequently, it is recommended to prevent fastener lateral slip during service operations. [ABSTRACT FROM AUTHOR]

Researchers have proposed the dynamic heterogeneous redundancy (DHR) architecture, which integrates dynamic, heterogeneous, redundant, and closed-loop feedback elements into the system, to fortify the reliability of the railway passenger service system (RPSS). However, there are at least two weaknesses with the common DHR architectures: (1) they need system nodes with enough computing and storage resources; (2) they have hardly considered the reliability of DHR architecture. To this end, this paper proposes a double-layer DHR (DDHR) architecture to ensure the reliability of RPSS. This architecture introduces a set of algorithms, which are optimized co-computation and ruling weight optimization algorithms for the data processing flow of the DDHR architecture. This set improves the reliability of the DDHR architecture. For the evaluation of the reliability of DDHR architecture, this paper also proposes two metrics: (1) Dynamic available similarity metric. This metric does not rely on the overall similarity of the double-layer redundant executor sets but evaluates the similarity of their performance under the specified interaction paths within a single scheduling cycle. The smaller its similarity, the higher its reliability. (2) Scheduling cycle under dual-layer similarity threshold. This metric evaluates the reliability of the RPSS under actual conditions by setting the schedulable similarity thresholds between the same and different layers of the dual-layer redundant executives in the scheduling process. Finally, analog simulation experiments and prototype system building experiments are carried out, whose numerical experimental results show that the DDHR architecture outperforms the traditional DHR architecture in terms of reliability and performance under different redundancy and dynamically available similarity thresholds, while the algorithmic complexity and multi-tasking concurrency performance are slightly weaker than that of the DHR architecture, but can be applied to the main operations of the RPSS in general. [ABSTRACT FROM AUTHOR]

Dynamic interactions among the AC railway power supply network and power electronic converters feeding the trains can result in low-frequency oscillation (LFO) of the catenary voltage, leading to a power outage of the substation and the shutdown of train traffic. To determine the low-frequency stability of the railway traction power systems, the impedance of the power supply network and the total differential admittance of the trains are required. This paper addresses the development of an analytical small-signal model of the train input admittance. For this purpose, small-signal models of each dynamic element involved are obtained. Specifically, the small-signal vector transformation from the actual dq-frame to the estimated dq̂-frame is presented to model the dynamics due to errors in the coordinate rotation of the single-phase four-quadrant converter (4QC) control system. Furthermore, the quadrature signal generator second-order generalized integrator (QSG-SOGI) model is calculated in the synchronous frame. The developed admittance model is intended to accurately predict various types of instabilities and serve as a powerful tool for conducting sensitivity analyses. The validation of the proposed models will be carried out through numerical simulations involving the power supply network and train systems.

Abstract In the railway system, fasteners have the functions of damping, maintaining the track distance, and adjusting the track level. Therefore, routine maintenance and inspection of fasteners are important to ensure the safe operation of track lines. Currently, assessment methods for fastener tightness include manual observation, acoustic wave detection, and image detection. There are limitations such as low accuracy and efficiency, easy interference and misjudgment, and a lack of accurate, stable, and fast detection methods. Aiming at the small deformation characteristics and large elastic change of fasteners from full loosening to full tightening, this study proposes high-precision surface-structured light technology for fastener detection and fastener deformation feature extraction based on the center-line projection distance and a fastener tightness regression method based on neural networks. First, the method uses a 3D camera to obtain a fastener point cloud and then segments the elastic rod area based on the iterative closest point algorithm registration. Principal component analysis is used to calculate the normal vector of the segmented elastic rod surface and extract the point on the centerline of the elastic rod. The point is projected onto the upper surface of the bolt to calculate the projection distance. Subsequently, the mapping relationship between the projection distance sequence and fastener tightness is established, and the influence of each parameter on the fastener tightness prediction is analyzed. Finally, by setting up a fastener detection scene in the track experimental base, collecting data, and completing the algorithm verification, the results showed that the deviation between the fastener tightness regression value obtained after the algorithm processing and the actual measured value RMSE was 0.2196 mm, which significantly improved the effect compared with other tightness detection methods, and realized an effective fastener tightness regression.

In the railway system, fasteners have the functions of damping, maintaining the track distance, and adjusting the track level. Therefore, routine maintenance and inspection of fasteners are important to ensure the safe operation of track lines. Currently, assessment methods for fastener tightness include manual observation, acoustic wave detection, and image detection. There are limitations such as low accuracy and efficiency, easy interference and misjudgment, and a lack of accurate, stable, and fast detection methods. Aiming at the small deformation characteristics and large elastic change of fasteners from full loosening to full tightening, this study proposes high-precision surface-structured light technology for fastener detection and fastener deformation feature extraction based on the center-line projection distance and a fastener tightness regression method based on neural networks. First, the method uses a 3D camera to obtain a fastener point cloud and then segments the elastic rod area based on the iterative closest point algorithm registration. Principal component analysis is used to calculate the normal vector of the segmented elastic rod surface and extract the point on the centerline of the elastic rod. The point is projected onto the upper surface of the bolt to calculate the projection distance. Subsequently, the mapping relationship between the projection distance sequence and fastener tightness is established, and the influence of each parameter on the fastener tightness prediction is analyzed. Finally, by setting up a fastener detection scene in the track experimental base, collecting data, and completing the algorithm verification, the results showed that the deviation between the fastener tightness regression value obtained after the algorithm processing and the actual measured value RMSE was 0.2196 mm, which significantly improved the effect compared with other tightness detection methods, and realized an effective fastener tightness regression. [ABSTRACT FROM AUTHOR]

Railway infrastructure is generally classified as either fixed or movable infrastructure assets. Failure in any of the assets could lead to the complete shutdown and disruption of the entire system, economic loss, inconvenience to passengers and the train operating company(s), and can sometimes result in death or injury in the event of the derailment of the rolling stock. Considering the importance of the railway infrastructure assets, it is only necessary to continuously explore their behavior, reliability, and safety. In this paper, a proactive multi-criteria decision-making model that is based on an interval-valued intuitionistic fuzzy set and some reliability quantitative parameters has been proposed for the evaluation of the reliability of the infrastructure assets. Results from the evaluation show that the failure mode 'Broken and defective rails' has the most risk and reliability concerns. Hence, priority should be given to the failure mode to avoid a total system collapse. [ABSTRACT FROM AUTHOR]

Meteorological hazards pose a significant risk to the operation and safety of the Chinese railway system. As China Railway High-speed (CRH) has become a vital part of the Chinese transportation infrastructure, an improved understanding of the spatial pattern of multiple hazards across the railway network is urgently needed, especially in the context of accelerated climate change. This study provides a spatial analysis of the dominant meteorological hazards affecting the CRH network (i.e., gales, rainfall, and snow), in terms of both intensity and frequency, and identifies hazard zones according to CRH operating standards. Given that meteorological hazards can lead to speed limitation or disruption of CRH trains, a risk matrix model is developed based on hazard intensity and designated railway speed to comprehensively assess the risk level of CRH lines in China. Risk assessment indicates that CRH lines exposed to hazardous levels of gales, rainfall, and snow account for 6.0%, 29.3%, and 20.0% of the total line length, respectively, while 7.3% of CRH lines are exposed to multiple hazards. Meteorological risk maps for CRH operation reveal hotspot lines that require special adaptive or preventative measures, and emergency protocols to reduce potential losses and disruptions.

Train delay prediction is a vital part of railway system, but due to uncertain factors such as the complexity of the railway system and spatial–temporal features, it is often difficult to predict train delay in practice. In this paper, we propose a Spatial–Temporal and Bi-directional Long Short-Term Memory (ST-BiLSTM) model to deal with the train delay prediction problem. The model contains spatial–temporal blocks to capture spatial and temporal features and a bi-directional Long Short-Term Memory (LSTM) block to introduce bi-directional information through an attention mechanism. Experiments demonstrate that ST-BiLSTM outperforms the existing baselines in two evaluation metrics.

The railway industry—traditionally a conservative industry with low adaption speed for innovation—is currently entering its digitization phase. The sector faces a challenge in integrating new technologies and approaches into the employed—often safety-critical—systems. Keeping the systems secure while conforming to the demanding safety norms creates previously unknown problems. In the last decades, the number of attacks on the railway system has increased. Furthermore, with standardized digital technologies, the attack surface will keep growing. Therefore, in this work, we look into the foreseeable future of the railway system and present 21 likely use cases. We analyze these use cases regarding possible threats, rate the severity of these threats, and deduce and rate necessary countermeasures. To this end, we model these use cases and the corresponding threats and countermeasures using Attack Graphs. We use a graphical solution for the risk and security analysis due to advantages over other methods, i.e., table-based solutions, like simplified presentation and an easier understanding of relationships, dependencies, and interactions between various elements. From these Attack Graphs, we extracted 14 commonly recurring attack strategies. After analyzing 49 countermeasures regarding their current maturity and further research and standardization demands, we identified 21 in need of further investigation. This implies that 21 necessary countermeasures to secure these future use cases require further research to apply to railway systems or require standardization. These results will help researchers focus on the necessary research and standardization and railway operators to ensure the security of their systems.

The railway industry—traditionally a conservative industry with low adaption speed for innovation—is currently entering its digitization phase. The sector faces a challenge in integrating new technologies and approaches into the employed—often safety-critical—systems. Keeping the systems secure while conforming to the demanding safety norms creates previously unknown problems. In the last decades, the number of attacks on the railway system has increased. Furthermore, with standardized digital technologies, the attack surface will keep growing. Therefore, in this work, we look into the foreseeable future of the railway system and present 21 likely use cases. We analyze these use cases regarding possible threats, rate the severity of these threats, and deduce and rate necessary countermeasures. To this end, we model these use cases and the corresponding threats and countermeasures using Attack Graphs. We use a graphical solution for the risk and security analysis due to advantages over other methods, i.e., table-based solutions, like simplified presentation and an easier understanding of relationships, dependencies, and interactions between various elements. From these Attack Graphs, we extracted 14 commonly recurring attack strategies. After analyzing 49 countermeasures regarding their current maturity and further research and standardization demands, we identified 21 in need of further investigation. This implies that 21 necessary countermeasures to secure these future use cases require further research to apply to railway systems or require standardization. These results will help researchers focus on the necessary research and standardization and railway operators to ensure the security of their systems. [ABSTRACT FROM AUTHOR]

Railway infrastructure is generally classified as either fixed or movable infrastructure assets. Failure in any of the assets could lead to the complete shutdown and disruption of the entire system, economic loss, inconvenience to passengers and the train operating company(s), and can sometimes result in death or injury in the event of the derailment of the rolling stock. Considering the importance of the railway infrastructure assets, it is only necessary to continuously explore their behavior, reliability, and safety. In this paper, a proactive multi-criteria decision-making model that is based on an interval-valued intuitionistic fuzzy set and some reliability quantitative parameters has been proposed for the evaluation of the reliability of the infrastructure assets. Results from the evaluation show that the failure mode ‘Broken and defective rails’ has the most risk and reliability concerns. Hence, priority should be given to the failure mode to avoid a total system collapse.

Carbon emission is one of the primary contributors to global warming. The global community is paying great attention to this negative impact. The goal of this study is to reduce the negative impact of railway maintenance by applying reinforcement learning (RL) by optimizing maintenance activities. Railway maintenance is a complex process that may not be efficient in terms of environmental aspect. This study is the world's first to use the potential of RL to reduce carbon emission from railway maintenance. The data used to create the RL model are gathered from the field data between 2016–019. The study section is 30 km long. Proximal Policy Optimization (PPO) is applied in the study to develop the RL model. The results demonstrate that using RL reduces carbon emission from railway maintenance by 48%, which generates a considerable amount of carbon emission reduction and reduces railway defects by 68%, which also improves maintenance efficiency significantly.

The ever-increasing demand for intercity travel, as well as competition among all modes of transportation, is an unavoidable reality that today's urban rail transit system must deal with. To meet this problem, urban railway companies must try to make better use of their existing plans and resources. Analytical approaches or simulation modeling can be used to develop or change a rail schedule to reflect the appropriate passenger demand. However, in the case of complex railway networks with several interlocking zones, analytical methods frequently have drawbacks. The goal of this article is to create a new simulation-based optimization model for the Bangkok railway system that takes into account the real assumptions and requirements in the railway system, such as uncertainty. The common particle swarm optimization (PSO) technique is combined with the developed simulation model to optimize the headways for each period in each day. Two different objective functions are incorporated into the models to consider both customer satisfaction by reducing the average waiting time and railway management satisfaction by reducing needed energy usage (e.g., reducing operating trains). The results obtained using a real dataset from the Bangkok railway system demonstrate that the simulation-based optimization approach for robust train service timetable scheduling, which incorporates both passenger waiting times and the number of operating trains as equally important objectives, successfully achieved an average waiting time of 11.02 min (with a standard deviation of 1.65 min) across all time intervals. [ABSTRACT FROM AUTHOR]

As the Indian railway network expands, the demand for reliable and efficient power systems, especially auxiliary converters supporting various on-board systems, grows. By reducing power losses and fuel consumption, it is more crucial to design an energy-efficient and economic auxiliary converter for the railway system. This is partially accomplished by using a 25KVA auxiliary (Aux.) converter, which can accept input electrical energy from a 110V DC battery at Self-Generation (SG) coaches. Biogeography Based Optimization (BBO) method of harmonic elimination for a 25KVA converter is described in this study. The paper explores the 120-degree mode of conduction and compares the existing technique with a proposed 7-switching selective odd harmonic removal method. Results from Matlab simulations demonstrate the effectiveness of the BBO technique in reducing Total Harmonic Distortion (THD). The output voltage waveforms with and without BBO are compared, showcasing a significant improvement in harmonic suppression. The paper concludes by emphasizing the practical implementation of the optimized switching angles using an ATmega2560R3 microcontroller. The online application of stored switching angles on the microcontroller is calculated offline. [ABSTRACT FROM AUTHOR]

This article presents an approach to the verification of the safety integrity level (SIL) of rail vehicle subassemblies in accordance with the applicable railway standards PN-EN ISO 50126-1, PN-EN ISO 50126:2, and PN-EN ISO 50129. Particular attention has been given to the calculation procedure related to the determination of the tolerable hazard rate and tolerable functional failure rate indicators in a situation where various reliability indicators have been declared for components of rail vehicles, such as MTTFD or B10D. In this case, the verification of the safety integrity level using the above-mentioned railway standards may be difficult, and it becomes necessary to use additional standards for safety systems based on electronic components. An example is the PN-EN ISO 13849-1:2006-01 standard, which contains a calculation method based on the transformation of the exponential model, which is useful for hazard and risk analyses of electronic systems containing components with different reliability indices. Another supplementary standard is the PN-EN 61025:2007 standard, which concerns fault tree analysis. Based on the above-mentioned standards, an algorithm was developed to verify the safety integrity level of the frequency converter control system. The obtained results allowed us to confirm the fulfillment of the functional safety requirements of the considered system.

A fastener is an important component used to fix the rail in railways. Defects in this component cause the rail and ballast to remain unstable. If the defective fasteners are not replaced in time, it is inevitable that the train will derail, and serious accidents will occur. Therefore, this component should be inspected periodically. Conventional image processing-based control systems are affected by noise and different lighting conditions in the real environment. In this study, it is aimed to determine the defects of fasteners with a deep learning-based hybrid approach. The YOLOv4-Tiny method is used for fastener detection and localization. This method gives accurate results, especially for the detection of small objects. After the fastener position is determined, a new lightweight convolutional neural network model is used for defect classification. The proposed convolutional neural network for classification has a small network structure because it uses depth-wise and pointwise convolution layers. When the experimental results are compared with other known transfer learning methods, better results were obtained in terms of training/test time and accuracy.

Harmonic is an important factor affecting the quality of the power transmission system. caused by non-linear loads and power conversion devices. In this research, several train simulation samples of the city line Airport Rail Link (ARL) electric train were used to simulate the effects of harmonics on the power electric substation (ESS) by calculating the Total Harmonic Distortion of voltage (THDv) and current (THDi). Then compared with IEEE519-2014 standards to indicate the quality of power found that THDi 35th–50th exceeds the standard. In addition, the comparison of the harmonic reduction effect of the sample train from the single tuned passive filter designs of 3rd, 5th, 7th, and 11th showed that the 11th filter was able to keep the THD within the standard.

Sustainability scores can be used to assess manufacturing strategies, going one step beyond a standard economic assessment. This work uses a previously proposed methodology to evaluate two of the most common welding processes for aluminium alloys that are specifically used in the fabrication of components for the transport industry based on their advantages in generating lightweight and dimensionally efficient parts. For comparison and as proof of concept, two welding methods were selected: Friction Stir Welding (FSW) and Gas Tungsten Arc Welding (GTAW). FSW attained a higher overall sustainability score. Values were calculated for an existing aluminium product, which was part of the opening and closing system of an electric train door, and the final score was 0.78 from FSW compared to 0.69 from GTAW, which was 11% higher in FSW compared to the conventional arc welding process. The analysis carried out included economic, physical, social, and environmental impacts. Finally, an example pertinent to a current EV component is described and considered along with a plan to determine the best welding process for a particular application, and with the calculations, the score obtained for GTAW was 0.43 and 0.68 for FSW, which was 36% higher that the result for the conventional arc welding process. [ABSTRACT FROM AUTHOR]

As one of the most important modes of transportation, the safety of running trains and railway tracks is significant. It is essential to power sensors that detect and track health in remote areas. The vibration energy of the track structure is enormous, stable, and not limited by weather factors such as the sun and wind. A new type of arch beam piezoelectric stack energy harvester for railway systems is studied in this paper. Through simulation analyses and experimental verification of the energy harvester, the influences of external resistance, load, pre-stress, and load frequency on the energy harvesting performance of the piezoelectric energy harvester are discussed. When the frequency is less than 6 Hz, the energy capture efficiency is greatly affected by the frequency. When the frequency exceeds 6 Hz, the frequency has little effect and the load dramatically affects the energy capture efficiency. The pre-stress has little effect on the energy capture efficiency, but there is an optimal value at 4.5 kN. The energy harvester has an output power of 193 mW, a weight of 912 g, and the energy density can reach 211.8 μW/g. These results can provide a reference for subsequent experiments in the actual environment. [ABSTRACT FROM AUTHOR]

Collision is the main issue in safe transportation, including in the railway system. Sensor systems have been developed to detect obstacles to prevent a collision, such as using cameras. One disadvantage of the camera systems is that performance detection decreases in a not clean environment, like the target position behind the fogs. This paper discusses the development of frequency modulated continuous wave (FMCW) radar signal processing for high-speed railway collision avoidance. The development of radar signal processing combines a two-dimensional constant false alarm rate (2D-CFAR) and robust principal component analysis (RPCA) to detect moving targets under clutter. Cell average (CA) and Greatest of CA (GOCA) CFAR are evaluated under a cluttered wall environment along the railway track. From the experiment, the development of FMCW radar can detect stationary or moving obstacles around 675 meters in front of the locomotive. Combining 2D-CFAR and RPCA algorithm outperforms average background subtraction in extracting moving targets from strong clutter signals along the railway track.

This article presents an approach to the verification of the safety integrity level (SIL) of rail vehicle subassemblies in accordance with the applicable railway standards PN-EN ISO 50126-1, PN-EN ISO 50126:2, and PN-EN ISO 50129. Particular attention has been given to the calculation procedure related to the determination of the tolerable hazard rate and tolerable functional failure rate indicators in a situation where various reliability indicators have been declared for components of rail vehicles, such as MTTFD or B10D. In this case, the verification of the safety integrity level using the abovementioned railway standards may be difficult, and it becomes necessary to use additional standards for safety systems based on electronic components. An example is the PN-EN ISO 13849-1:2006-01 standard, which contains a calculation method based on the transformation of the exponential model, which is useful for hazard and risk analyses of electronic systems containing components with different reliability indices. Another supplementary standard is the PN-EN 61025:2007 standard, which concerns fault tree analysis. Based on the above-mentioned standards, an algorithm was developed to verify the safety integrity level of the frequency converter control system. The obtained results allowed us to confirm the fulfillment of the functional safety requirements of the considered system. [ABSTRACT FROM AUTHOR]

Railroad structural behaviour is a significant factor for safety and comfort during high-speed train operation. Intending to improve railway performance, increase its bearing capacity, and reduce vibrations induced by train passage, asphalt concrete has become a material to be integrated into railway construction. Despite several studies evaluating asphalt concrete effect on the railway mechanical behaviour, its impact assessment remains poorly understood. This study investigates the impact of asphalt concrete material in the geogrid-reinforced-pile-supported embankment structure subjected to high-speed train moving. A 3D nonlinear finite element model was developed to simulate Harbin (Dalian (China) instrumented railroad test section accurately. The high-speed train moving load was modelled as a transitory dynamic load via a user-defined subroutine 3D load in which the track irregularity is incorporated. The established model was effectively validated by the vibration acceleration and stress measured in the field test section. The asphalt concrete viscoelasticity behaviour was incorporated into the 3D geogrid-reinforced-pile-supported embankment finite element model through the Prony series to characterise its mechanical response better. The impact of asphalt concrete material in maintaining a low and constant structural vibration, regardless of train weight level, moving speed variations, and weather conditions were investigated, analysed and discussed.

Dynamic interactions among the AC railway power supply network and power electronic converters feeding the trains can result in low-frequency oscillation (LFO) of the catenary voltage, leading to a power outage of the substation and the shutdown of train traffic. To determine the low-frequency stability of the railway traction power systems, the impedance of the power supply network and the total differential admittance of the trains are required. This paper addresses the development of an analytical small-signal model of the train input admittance. For this purpose, small-signal models of each dynamic element involved are obtained. Specifically, the small-signal vector transformation from the actual d q -frame to the estimated d q ̂ -frame is presented to model the dynamics due to errors in the coordinate rotation of the single-phase four-quadrant converter (4QC) control system. Furthermore, the quadrature signal generator second-order generalized integrator (QSG-SOGI) model is calculated in the synchronous frame. The developed admittance model is intended to accurately predict various types of instabilities and serve as a powerful tool for conducting sensitivity analyses. The validation of the proposed models will be carried out through numerical simulations involving the power supply network and train systems. • Modeling the dynamics due to errors in the coordinate rotations of the single-phase 4QC control system. • Modeling QSG-SOGI transfer function in the synchronous d q -frame. • Calculation of small-signal model of train input admittance at low frequencies. • Stability analysis of low-frequency oscillation phenomenon. • Sensitivity analysis of electrical and control train parameters that impact low-frequency stability. [ABSTRACT FROM AUTHOR]

Dynamic interactions among the AC railway traction network and power electronics converters feeding the trains have been reported to cause low-frequency oscillations (LFO) of the catenary voltage and current. This can result in railway system instability, eventually leading to a power outage and the shutdown of the train traffic. To avoid LFO, control of train power electronic converters must be properly designed and tuned. Experimental verification of control performance regarding the LFO phenomenon in the railway traction network is not easy. Alternatively, the railway traction network can be emulated using a power electronic converter, which would feed the train power converter under test. This paper addresses the design of a network emulator able to reproduce the dynamic behavior of the actual network at low frequencies, including LFO. Three different options will be considered for the network emulator. Their performance will be studied first by means of simulations. Finally, the selected solution will be verified on a downscale test bench.

Water scarcity is a current problem in many parts of the planet and there is a worldwide concern about water availability to meet future water demand. In countries like Brazil, where most of the electricity is produced by hydroelectric power plants, water scarcity directly impacts energy production. The water–energy nexus is directly related and impacted by CO2 emissions and its climate consequences, which calls to a broader approach: energy–carbon–water nexus. In this context, the Sustainable Water and Energy Consumption (SWEC) Program was developed to mitigate water and energy supply problems in a railway company in Brazil. The actions took place in four main areas: (a) users conscientization, (b) consumption diagnosis, (c) indicators for evaluating water and energy consumption, and (d) evaluation of implementing alternative cleaner water and energy sources. The per capita consumption of water and energy were reduced by 10% and 19%, respectively. Permanent results were achieved by the SWEC Program, such as the acquisition of two photovoltaic systems with a total capacity of 96.5 kWp. The investment made provided an average monthly reduction in energy consumption of 56% in 2022. This work contributed to the UN Agenda 2030 and the findings may help companies and industries, and other institutions, such as universities and schools, to improve their water and electricity consumption. [ABSTRACT FROM AUTHOR]

Capturing vibration energy of track structures using piezoelectric energy harvesters has attracted increasing attention for powering wireless sensor networks along railway line. To better use the vertical space below steel rail, a kind of piezoelectric tube stack energy harvesters is proposed in this paper, which can be placed at the bottom of the steel rail to harvest the vibration energy of the vertical displacement induced by the moving train. The harvester consists of a displacement transmission rod, a compression spring, a force transmission metal tube, a piezoelectric tube stack, a whole metal shell, screw bolts, and a wire hole. The advantage of this design is to fully combine the heights of the compression spring, the force transmission metal tube and the piezoelectric tube stack, which helps reduce the vertical height of the device and enables its wide application to a variety of tracks, including, ballast track, ballastless track, and steel-spring floating slab track. Energy harvesting performance of the developed piezoelectric tube stack energy harvester is investigated experimentally, which is consistent with the theoretical results of the simplified model of such a harvester reported. Effects of displacement amplitude, displacement frequency, spring stiffness, resistance, and key parameters of piezoelectric tube stack on the energy harvesting performance of the harvester are also discussed. The present study provides a new design concept for developing piezoelectric energy harvesters used in railway systems with the smaller vertical space below steel rail. [ABSTRACT FROM AUTHOR]

Meteorological hazards pose a significant risk to the operation and safety of the Chinese railway system. As China Railway High-speed (CRH) has become a vital part of the Chinese transportation infrastructure, an improved understanding of the spatial pattern of multiple hazards across the railway network is urgently needed, especially in the context of accelerated climate change. This study provides a spatial analysis of the dominant meteorological hazards affecting the CRH network (i.e., gales, rainfall, and snow), in terms of both intensity and frequency, and identifies hazard zones according to CRH operating standards. Given that meteorological hazards can lead to speed limitation or disruption of CRH trains, a risk matrix model is developed based on hazard intensity and designated railway speed to comprehensively assess the risk level of CRH lines in China. Risk assessment indicates that CRH lines exposed to hazardous levels of gales, rainfall, and snow account for 6.0%, 29.3%, and 20.0% of the total line length, respectively, while 7.3% of CRH lines are exposed to multiple hazards. Meteorological risk maps for CRH operation reveal hotspot lines that require special adaptive or preventative measures, and emergency protocols to reduce potential losses and disruptions.

The railways were boosted by foreign capital and promoted by the Argentine liberal State. In the 20th Century, with the changes in the development model, the system went into decline and its current flourishing is proposed to decrease territorial inequalities. Since then, many planning strategies have emerged at national and supranational level. However, the dynamics of the plans and projects is tied to the future of the Chinese economy, as this country is the main commercial partner and infrastructure investor in Latin America. In consequence, the regional policies are, again, subordinated to those exogenous logics. With a qualitative methodological approach, the article analyses some official documents and is divided into three parts: a historical exploration of the railway, a critical study of the stage of greater territorial planning called neo-developmentalist and a review of these processes in light of the reestablishment of neoliberal policies. [ABSTRACT FROM AUTHOR]

Trains have a key role in transporting people and goods with the option of moving from source to destinations by passing through several stations, with time-based features like date scheduling and known arrival times, which makes time a critical factor. The main challenge here, is to ensure that the train trip or train schedules are not affected or delayed in any way during the whole train trip; by giving the control unit in the railway system, the required time to process requests regarding all collected data. This an NP-hard problem with an optimal solution of handling all collected data and all service requests by the control unit of the railway system. Operational research will be used to solve this problem by developing many heuristics to deal with tasks of real-time systems, to produce a significant time optimization in the railway systems. To solve this problem, the proposed approach employs optimization by adapting 22 heuristics based on two categories of algorithms, the separated blocks category algorithm and the blocks interference category algorithm. The proposed approach receives data from many different sources at the same time, then collects the received data and save it to a data base in the railway system control unit. Experimental results showed the effectiveness of the developed heuristics, more over the proposed approach minimized the maximum completion time that was elapsed in handling the received requests. [ABSTRACT FROM AUTHOR]

This article deals with the problem of new threats that appear in areas that have not been affected by them so far. The considerations concern asymmetric threats and the railway system. The analysis of this issue was carried out on the basis of general information on the problem of asymmetric threats and the knowledge of the railway system, its operation and identified threats that can be attributed to the characteristics of asymmetric threats. The aim of the article is to draw attention to new phenomena that are beginning to affect transport on a global basis, including rail transport.

• An analytical framework is to assess the impact of tornado hazards on railway network. • The mechanism of the interaction between the random field of tornado hazards and the dense railway network was modeled. • Capturing the correlation among tornado parameters using the C-vine copula model. • The risk ranking of railway segments within the study area has been obtained. The Yangtze River Delta region in China is characterized by a highly dense railway network, coupled with the frequent occurrence of tornado hazards. While tornadoes pose operational risks to railway networks, there was a noticeable dearth of research in this area. To bridge this gap, we propose an analytical framework. Initially, within regions covered by an extensive railway network, we utilize historical data to construct the random field of tornado hazards. For establishing this random field, we recommend adopting the C-vine copula model to capture the correlations among tornado parameters. Subsequently, in the risk analysis of the railway system, three representative disaster scenarios were formulated to comprehensively depict the influence of tornadoes on train operations. Methodologies were presented for calculating the probabilities of these scenarios and the damage measures for train disruption and deceleration. Finally, the physical and functional risk of the railway segments are calculated, and a comprehensive risk assessment is derived. The results reveal that the Angting–Zhengjiang line faces a notably high functional risk, leading to an annual average increase of 0.27 days in the total travel time of the rail network. Both the Angting–Zhengjiang and Yangzhou–Haian lines share the top position in comprehensive risk, emphasizing the need for focused attention. [ABSTRACT FROM AUTHOR]

As one of the most important modes of transportation, the safety of running trains and railway tracks is significant. It is essential to power sensors that detect and track health in remote areas. The vibration energy of the track structure is enormous, stable, and not limited by weather factors such as the sun and wind. A new type of arch beam piezoelectric stack energy harvester for railway systems is studied in this paper. Through simulation analyses and experimental verification of the energy harvester, the influences of external resistance, load, pre-stress, and load frequency on the energy harvesting performance of the piezoelectric energy harvester are discussed. When the frequency is less than 6 Hz, the energy capture efficiency is greatly affected by the frequency. When the frequency exceeds 6 Hz, the frequency has little effect and the load dramatically affects the energy capture efficiency. The pre-stress has little effect on the energy capture efficiency, but there is an optimal value at 4.5 kN. The energy harvester has an output power of 193 mW, a weight of 912 g, and the energy density can reach 211.8 μW/g. These results can provide a reference for subsequent experiments in the actual environment.

Railroad structural behaviour is a significant factor for safety and comfort during high-speed train operation. Intending to improve railway performance, increase its bearing capacity, and reduce vibrations induced by train passage, asphalt concrete has become a material to be integrated into railway construction. Despite several studies evaluating asphalt concrete effect on the railway mechanical behaviour, its impact assessment remains poorly understood. This study investigates the impact of asphalt concrete material in the geogrid-reinforced-pile-supported embankment structure subjected to high-speed train moving. A 3D nonlinear finite element model was developed to simulate Harbin (Dalian (China) instrumented railroad test section accurately. The high-speed train moving load was modelled as a transitory dynamic load via a user-defined subroutine 3D load in which the track irregularity is incorporated. The established model was effectively validated by the vibration acceleration and stress measured in the field test section. The asphalt concrete viscoelasticity behaviour was incorporated into the 3D geogrid-reinforcedpile-supported embankment finite element model through the Prony series to characterise its mechanical response better. The impact of asphalt concrete material in maintaining a low and constant structural vibration, regardless of train weight level, moving speed variations, and weather conditions were investigated, analysed and discussed. [ABSTRACT FROM AUTHOR]

Defect detection of trains is of great significance for operation safety and maintenance efficiency for railway maintenance. Nowadays, China railway system utilizes high-speed line scan cameras to capture images of critical parts of moving trains. The visual inspection on the images still heavily relies on manual interpretation. To reduce the labor requirements, we propose a novel two-stage deep learning based framework for component defect detection of moving trains. The proposed framework is composed of two major successive stages: detecting train components by using our proposed hierarchical object detection scheme (HOD), and detecting component defects based on multiple neural networks and image processing methods. Our proposed HOD can effectively detect and localize train components from large to small in a hierarchical way. Furthermore, a gated feature fusion method that can extract and combine the hierarchical contextual features and spatial contexts is also proposed to improve the performance. To the best of our knowledge, it is the first time in the literature that component defect detection of moving trains is systematically analyzed. Extensive experiments on real images from China railway system have demonstrated that our framework outperforms the state-of-the-art baselines significantly. [ABSTRACT FROM AUTHOR]