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At present, the mainstream studies on route selection optimization at the railway station rarely considered the overall punctuality of the operation plans and the seizing route resource between shunting operation and train running, which can endanger the running safety and reduce the efficiency at the station. Therefore, this paper proposes an optimization method for the route selection under the integration of dispatching and control at the railway station. Firstly, the station‐type data structure, the route occupation conflict, and the operation task order were defined. Then, a 0‐1 programming model was constructed to minimize the total delay time and shorten the total travel time of all operations. Finally, a two‐stage solution algorithm based on depth‐first search algorithm and genetic algorithm was designed, and two actual cases of a technical station in China were designed. The instance verification results show that the algorithm can find the satisfactory route scheme in 250 iterations; different delay factors and travel coefficients will get different route schemes, which can provide decision support for dispatchers and operators to select routes. Through comparative analysis of algorithms, it is found that the two‐stage algorithm has higher solving efficiency than the individual depth‐first search algorithm and individual genetic algorithm. [ABSTRACT FROM AUTHOR]

In recent years, longer and heavier trains have become more common, primarily driven by efficiency and cost‐saving measures in the railroad industry. Regulation of train length is currently under consideration in the United States at both the federal and state levels, because of concerns that longer trains may have a higher risk of derailment, but the relationship between train length and risk of derailment is not yet well understood. In this study, we use data on freight train accidents during the 2013–2022 period from the Federal Railroad Administration (FRA) Rail Equipment Accident and Highway‐Rail Grade Crossing Accident databases to estimate the relationship between freight train length and the risk of derailment. We determine that longer trains do have a greater risk of derailment. Based on our analysis, running 100‐car trains is associated with 1.11 (95% confidence interval: 1.10–1.12) times the derailment odds of running 50‐car trains (or a 11% increase), even accounting for the fact that only half as many 100‐car trains would need to run. For 200‐car trains, the odds increase by 24% (odds ratio 1.24, 95% confidence interval: 1.20–1.28), again accounting for the need for fewer trains. Understanding derailment risk is an important component for evaluating the overall safety of the rail system and for the future development and regulation of freight rail transportation. Given the limitations of the current data on freight train length, this study provides an important step toward such an understanding. [ABSTRACT FROM AUTHOR]

The UK rail network is subject to costly disruption due to the operational effects of adhesion variation between the wheel and rail. Causes of this are often environmental introduction of contaminants that require a wide‐scale approach to risk mitigation such as defensive driving or rail‐head maintenance. It remains an open problem to monitor the real‐time status of the network to optimise resources and approaches in response to adhesion problems. This article presents an on‐vehicle monitoring method designed to estimate the coefficient of friction by processing data from on‐board sensors of typical rail passenger vehicles. This approach uses a multi‐body physics analysis of a target vehicle to create estimators for both creep force and creep, allowing a curve fitting approach to estimate the coefficient for friction from the creep curves. [ABSTRACT FROM AUTHOR]

Train delays can significantly impact the punctuality and service quality of high‐speed trains, which also play a crucial role in affecting dispatchers with their decision‐making. In this study, a data‐driven train delay prediction framework was proposed and strengthened by considering the impact of dispatching commands and the mechanisms of train delay propagation using XGBoost. Four metaheuristic algorithms were utilized to fine‐tune its hyperparameters. A vast dataset comprising 1.9 million records spanning 38 months of train operation data was utilized for feature extraction and model training. The model's accuracy was evaluated using three statistical metrics, and a comparison of the four tuning frameworks was performed. To emphasize the model's interpretability and its practical guidance for train rescheduling, the relationship of dispatching commands, delay propagation and delay prediction was validated by combining the theory and practical results, and a SHAP (SHapley Additive exPlanations) analysis was used for a clearer model explanation. The results revealed that distinct XGBoost‐Metaheuristic models exhibit unique effects in different criteria, yet they all demonstrated high accuracy and low prediction errors, thereby revealing the potential of using machine learning for train delay prediction, which is valuable for decision‐making and rescheduling. [ABSTRACT FROM AUTHOR]

Copyright of Cotton Textile Technology is the property of Cotton Textile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The first four battery-powered trains from Siemens Mobility began regular service for regional rail transport on Network 8 (“Ortenau”) on 8 April 2024, marking a premiere in Germany. shows the Mireo Plus B train running without the pantograph lifted. The official start was celebrated by an exclusive inaugural trip from Offenburg to Oberkirch and back and was accompanied by Winfried Hermann, minister of transport for the state of Baden-Württemberg, together with numerous invited guests. [ABSTRACT FROM AUTHOR]

Abstract At present, the mainstream studies on route selection optimization at the railway station rarely considered the overall punctuality of the operation plans and the seizing route resource between shunting operation and train running, which can endanger the running safety and reduce the efficiency at the station. Therefore, this paper proposes an optimization method for the route selection under the integration of dispatching and control at the railway station. Firstly, the station‐type data structure, the route occupation conflict, and the operation task order were defined. Then, a 0‐1 programming model was constructed to minimize the total delay time and shorten the total travel time of all operations. Finally, a two‐stage solution algorithm based on depth‐first search algorithm and genetic algorithm was designed, and two actual cases of a technical station in China were designed. The instance verification results show that the algorithm can find the satisfactory route scheme in 250 iterations; different delay factors and travel coefficients will get different route schemes, which can provide decision support for dispatchers and operators to select routes. Through comparative analysis of algorithms, it is found that the two‐stage algorithm has higher solving efficiency than the individual depth‐first search algorithm and individual genetic algorithm.

Abstract Train delays can significantly impact the punctuality and service quality of high‐speed trains, which also play a crucial role in affecting dispatchers with their decision‐making. In this study, a data‐driven train delay prediction framework was proposed and strengthened by considering the impact of dispatching commands and the mechanisms of train delay propagation using XGBoost. Four metaheuristic algorithms were utilized to fine‐tune its hyperparameters. A vast dataset comprising 1.9 million records spanning 38 months of train operation data was utilized for feature extraction and model training. The model's accuracy was evaluated using three statistical metrics, and a comparison of the four tuning frameworks was performed. To emphasize the model's interpretability and its practical guidance for train rescheduling, the relationship of dispatching commands, delay propagation and delay prediction was validated by combining the theory and practical results, and a SHAP (SHapley Additive exPlanations) analysis was used for a clearer model explanation. The results revealed that distinct XGBoost‐Metaheuristic models exhibit unique effects in different criteria, yet they all demonstrated high accuracy and low prediction errors, thereby revealing the potential of using machine learning for train delay prediction, which is valuable for decision‐making and rescheduling.

Abstract The UK rail network is subject to costly disruption due to the operational effects of adhesion variation between the wheel and rail. Causes of this are often environmental introduction of contaminants that require a wide‐scale approach to risk mitigation such as defensive driving or rail‐head maintenance. It remains an open problem to monitor the real‐time status of the network to optimise resources and approaches in response to adhesion problems. This article presents an on‐vehicle monitoring method designed to estimate the coefficient of friction by processing data from on‐board sensors of typical rail passenger vehicles. This approach uses a multi‐body physics analysis of a target vehicle to create estimators for both creep force and creep, allowing a curve fitting approach to estimate the coefficient for friction from the creep curves.

Abstract With the expansion of the metropolitan area, the application of express/local mode is gradually increasing. In contrast to the normal mode, the express/local mode has advantages in reducing energy consumption and saving total travel time by having express trains skipping some stops. This paper aims to minimize the total energy consumption of express and local train throughout the day by optimizing the train operation strategy in the same power supply section and increasing the overlap time between train traction acceleration and train regenerative braking to obtain the optimal energy‐efficient timetable. As the consumed energy of a train is highly dependent on the rolling stock weight and the on‐board passengers’ weight. An integer programming model is proposed with on‐board passengers considered accurately, in which the dwell times, departure headway, and total turnaround time of express and local trains are determined. An improved grey wolf algorithm is designed by improving convergence factor and incorporating differential evolution to solve the proposed problem. The real data on Guangzhou Metro Line 18 is adopted for numerical studies. The results show that the optimized timetable increases the regenerative energy utilization rate by 21.37% and reduces the total energy consumption by 5.02% compared to the operational timetable.

With the expansion of the metropolitan area, the application of express/local mode is gradually increasing. In contrast to the normal mode, the express/local mode has advantages in reducing energy consumption and saving total travel time by having express trains skipping some stops. This paper aims to minimize the total energy consumption of express and local train throughout the day by optimizing the train operation strategy in the same power supply section and increasing the overlap time between train traction acceleration and train regenerative braking to obtain the optimal energy‐efficient timetable. As the consumed energy of a train is highly dependent on the rolling stock weight and the on‐board passengers' weight. An integer programming model is proposed with on‐board passengers considered accurately, in which the dwell times, departure headway, and total turnaround time of express and local trains are determined. An improved grey wolf algorithm is designed by improving convergence factor and incorporating differential evolution to solve the proposed problem. The real data on Guangzhou Metro Line 18 is adopted for numerical studies. The results show that the optimized timetable increases the regenerative energy utilization rate by 21.37% and reduces the total energy consumption by 5.02% compared to the operational timetable. This paper aims to minimize the total energy consumption of express and local train throughout the day by optimizing the train operation strategy in the same power supply section and increasing the overlap time between train traction acceleration and train regenerative braking to obtain the optimal energy‐efficient timetable. An integer programming model is proposed with on‐board passengers considered accurately, in which the dwell times, departure headway, and total turnaround time of express and local trains are determined. [ABSTRACT FROM AUTHOR]

This paper evaluates rail transit within the context of the transit policies implemented in Lima, Peru. First it reviews the implementation of rapid transit, and bus reform. Secondly, it evaluates the outcomes of such policies by using Total Factor Productivity for policy effectiveness, Data Envelopment Analysis for rapid transit performance, and Generalized Cost of Travel for improvements. This paper finds that implementation failed in enforcing key requirements for rail transit regarding penetration of CBD and short transfers to bus transit; and that the basic assumptions of bus reform did not hold regarding bus oversupply, bus congestion or bus pollution. This paper also finds that outcomes of policies failed dramatically in achieving the planning goals; however, rail transit (Metro) shows high level of resilience in serving large ridership at high speed. On the other hand, bus reform was associated with a disproportionate increase of motorization, well over the effect of income growth or car attractiveness, and more related to the excessive reduction of bus transit capacity ill-advised from unproved bus reform assumptions. This paper recommends expanding rail rapid transit due to its intensive use of green renewable energy and its potential of demand growth if combined with modern Intelligent Transportation services, but this opportunity can be wasted without the proposed policy constraint to achieve lower Generalized Cost of Travel at any governmental intervention for bus reform, instead of just reducing bus transit capacity as implemented. Finally, this paper recommends government to government contracts to build rail transit and to enforce proper planning.

The need to reduce overall energy consumption motivates the search for ways to improve energy use by transportation. Electrification has helped to significantly reduce the energy intensity of rail transportation. On today’s high-speed lines, power flows between trains and traction transformers are generally transmitted through the 2x25 electrification system. Although most of the energy provided to the railway system is consumed by the moving trains, a part of the energy is lost in the electrification system itself. In this study, dynamic computer simulations of realistic high-speed rail operating scenarios are developed and the energy losses in the electrification system are evaluated. The results show that power transmission losses are significant, and their value depends on the operation scenario. Finally, a method for selecting the minimum transmission loss operating scenarios is proposed, without modifying the transportation capacity offered or altering the resources required for the operation of a high-speed rail.

Presents reviews for the following list of books, The Northeast Corridor—David Alff (Chicago, IL, USA: Univ. Chicago Press, 2024, 283 pp.). [ABSTRACT FROM AUTHOR]

Abstract This paper presents the development and validation of two artificial neural networks (ANN) models, utilising time and power‐based architectures, to accurately predict key parameters of a hydrogen hybrid train profile and its optimal trajectory. The research employs a hybrid train simulator (HTS) to authenticate the ANN models, which were trained using simulated trajectories from five unique hybrid trains on a designated route. The models’ performance was evaluated by computing the mean square normalisation error and mean absolute performance error, while the output's reliability was confirmed through the HTS. The results indicate that both ANN models proficiently predict a hybrid train's critical parameters and trajectory, with mean errors ranging from 0.19% to 0.21%. However, the cascade‐forward neural network (CFNN) topology in the time‐based architecture surpasses the feed‐forward neural network (FFNN) topology concerning mean squared error (MSE) and maximum error in the power‐based architecture. Specifically, the CFNN topology within the time‐based structure exhibits a slightly lower MSE and maximum error than its power‐based counterpart. Additionally, the study reveals the average percentage difference between the benchmark and FFNN/CNFN trajectories, highlighting that the time‐based architecture exhibits lower differences (0.18% and 0.85%) compared to the power‐based architecture (0.46% and 0.92%).

This paper presents the development and validation of two artificial neural networks (ANN) models, utilising time and power‐based architectures, to accurately predict key parameters of a hydrogen hybrid train profile and its optimal trajectory. The research employs a hybrid train simulator (HTS) to authenticate the ANN models, which were trained using simulated trajectories from five unique hybrid trains on a designated route. The models' performance was evaluated by computing the mean square normalisation error and mean absolute performance error, while the output's reliability was confirmed through the HTS. The results indicate that both ANN models proficiently predict a hybrid train's critical parameters and trajectory, with mean errors ranging from 0.19% to 0.21%. However, the cascade‐forward neural network (CFNN) topology in the time‐based architecture surpasses the feed‐forward neural network (FFNN) topology concerning mean squared error (MSE) and maximum error in the power‐based architecture. Specifically, the CFNN topology within the time‐based structure exhibits a slightly lower MSE and maximum error than its power‐based counterpart. Additionally, the study reveals the average percentage difference between the benchmark and FFNN/CNFN trajectories, highlighting that the time‐based architecture exhibits lower differences (0.18% and 0.85%) compared to the power‐based architecture (0.46% and 0.92%). [ABSTRACT FROM AUTHOR]

Urbanization places greater demand on the link between downtown areas and suburbs, due to commuters’ long-distance and diverse trips. As an emerging form of park-and-ride (PNR) services, remote PNR (RPR) facilities have proved to be more economical and environmentally friendly, allowing travelers to park in a suburban area and travel to a rail station via bus. In this regard, a generalized simulation-based bilevel model for optimizing the locations and capacities of RPR facilities is developed in this article. A hybrid algorithm integrating Bayesian optimization, branch and bound, and trust region sequential quadratic programming is proposed to achieve an optimal solution. The proposed integrated method balances the desired efficiency and accuracy through the combination of machine learning-based technology and mathematical optimization methodology. The validity of the proposed model is tested on a large-scale real-world transportation network in Halle, Germany. Modeling and analyzing RPR schemes using the proposed framework may provide new insights into improving social welfare. [ABSTRACT FROM AUTHOR]

Copyright of REUNIR: Revista de Administração, Contabilidade e Sustentabilidade is the property of REUNIR - Revista de Administracao, Contabilidade e Sustentabilidade and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Metra (Metropolitan Rail, the operator of commuter rail in Chicago area) is to receive the largest discretionary grant in its history: a US ${\$}$ 169.3 million federal Congestion Mitigation and Air Quality Improvement (CMAQ) grant to buy battery-powered, zero-emission trainsets. Metra will be among the first in the nation to operate the innovative green technology. The CMAQ funding was approved by the Chicago Metropolitan Agency for Planning and the Metropolitan Planning Organization Policy Committee in October 2023. [ABSTRACT FROM AUTHOR]

The Trans-Caspian International Transport Route (TITR) is one of the main transport corridors between China and Europe. It passes through the territory of Kazakhstan, as well as the countries of the South Caucasus, Turkey, and Iran, and contributes to the full realisation of transit and export opportunities of these countries. Each country participating in the development of TITR is implementing projects to increase the capacity of its transit infrastructure, but the potential of this route has not yet been fully realised. The reason for this is both high tariffs for sea and rail transport and insufficiently short transport times, which are dependent on the parameters and methods of using the infrastructure. This paper is devoted to the study of the TITR railway infrastructure located in Kazakhstan. The developed simulation model allows estimating the impact of infrastructure parameters of the railway track on the movement time of a container train from the PRC border to the ports of Aktau and Kuryk on the Caspian Sea coast.

Predicting the availability of measurement points provided by Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) poses a challenge due to a nonuniform distribution of Persistent Scatterers (PSs). This article introduces a novel method to estimate the availability of MT-InSAR results on buildings and infrastructure networks, eliminating the need for labor-intensive and time-consuming analyses of the entire SAR data stack. The method is based on an analysis of the interferometric coherence decay characteristics and data regarding buildings and transport infrastructure location as inputs to a convolutional neural network. Specifically, a U-Net architecture model was implemented and trained to predict the PS density of Sentinel-1 data. The methodology was applied to a regional-scale analysis of the Dutch infrastructure, resulting in a low 1.06$\pm$0.10 mean absolute error in the pixel-based PS count estimation on the test data split, with over 80% of predictions within $\pm$1 from the actual value. The model achieved high accuracy when applied to a previously unseen dataset, demonstrating strong generalization performance. The proposed workflow, with its notable ability to accurately predict areas lacking measurement points, offers stakeholders a tool to assess the feasibility of applying MT-InSAR for specific structures. Thereby, it enhances infrastructure reliability by addressing a critical need in decision-making processes and improving the applicability of MT-InSAR for structural health monitoring of infrastructure assets.

Modern electric railway vehicles are faced with the challenge of achieving electromagnetic compatibility (EMC) compliance with railway signaling systems. Currently, no established countermeasures or design methods that guarantee passing the EMC compliance test are available. Therefore, the enhancement of EMC management during vehicle manufacturing processes is imperative. This study devised a pre-accreditation method to be implemented at the vehicle completion stage, prior to the EMC compliance test. The proposed method entails the substitution of real ground signaling devices, whose parameters are difficult to obtain, with unified coils. The effectiveness of this approach is validated by observing whether the gain difference remains consistent when using both an exciting coil and a test train. To illustrate this method, we selected the platform screen door (PSD) balise as a representative signaling device, which is widely utilized in various railway stations. The unified coil employed in this study can be easily fabricated. First, we measured the gain difference (the ratio between the induced voltage of coils and the exciting coil current) between the PSD balise and unified coil. Next, we measured the unknown magnetic flux induced by an 8-car electric multiple unit test train by utilizing the PSD balise and unified coil. Finally, we calculated the measured difference between them. The gain and measured differences properly agreed (within 6 dB) across a wide frequency range of 200 kHz to 4 MHz.

Alstom signed a contract for more than €500 million with the Royal Commission for AlUla for AlUla’s pioneering battery-powered tramway, the world’s longest catenary-free line. The fully integrated Alstom tramway system will feature 20 state-of-the-art Citadis B battery trams, as shown in. The 22.4-km line will encapsulate richness, history, and green mobility like no other, linking 17 strategically located stations, offering unmatched access to AlUla’s five core historical districts, including UNESCO World Heritage sites, such as AlUla Old Town (District 1), Dadan (District 2), Jabal Ikmah (District 3), Nabataean Horizon (District 4), and Hegra Historical City (District 5). [ABSTRACT FROM AUTHOR]

Rail transportation plays a crucial role in facilitating efficient and sustainable urban development, particularly in densely populated areas. This study aims to evaluate customer satisfaction with the public transport system in Malaysia, with a focus on rail transportation. The research examines various factors that influence customer satisfaction, including service quality, punctuality, convenience, safety, and cleanliness. The study utilizes a mixed-methods approach, combining secondary data analysis and primary data collection. Secondary data analysis involves a comprehensive review of existing literature, including academic papers, government reports, and industry publications. Primary data is collected through a structured questionnaire administered to a sample of rail transport users in the Klang Valley region. The findings highlight the importance of service quality in shaping customer satisfaction. Factors such as reliable and punctual train arrivals, convenient and comfortable facilities, and a clean and safe environment emerge as key determinants of customer satisfaction. The study also identifies areas for improvement, including enhancing the efficiency of ticketing systems, increasing seating capacity during peak hours, and addressing cleanliness issues at stations and on trains. The research findings have implications for policymakers, transport authorities, and rail operators. By understanding the factors that influence customer satisfaction, stakeholders can design strategies to enhance the quality of rail transportation services. Improving customer satisfaction levels can lead to increased ridership, reduced road congestion, and a more sustainable transport system. The findings can also inform future infrastructure development plans and guide decision-making processes in the public transport sector. Overall, this study contributes to the growing body of knowledge on customer satisfaction in rail transportation and provides valuable insights for improving the quality and effectiveness of Malaysia's public transport system, specifically in the context of rail transportation.

With the rapid growth of the national economy, people's demand for transportation is becoming increasingly strong. The rail transit business is booming in large and medium-sized cities, and the education management of urban rail transit students needs further reform. At the same time, digital information technology is widely used in various fields, and digital management of education has become one of the major development directions of education reform. The study proposes a specific construction path based on the analysis of the necessity of digital management of education for urban rail transportation majors, and then optimizes the K-medoids algorithm in the clustering algorithm and validates its education digital management effect. The outcomes show that the clustering precision of the upgraded K-medoids algorithm in the selected dataset is up to 92.68%, and the running time is all below 5s, with the lowest value being 3.9s; In the digital management of urban rail transit majors in universities, the precision obtained by the algorithm is all maintained at around 95%, and the satisfaction rate is all higher than 90%. The effectiveness of the proposed method has been verified, providing a new method for the management of digital education systems for urban rail transit students. It can better understand the needs and characteristics of students, help improve their learning effectiveness and educational quality, and achieve more targeted allocation of educational resources. [ABSTRACT FROM AUTHOR]

With the development of high-speed rail transportation, the automatic train operation (ATO) of high-speed trains (HSTs) has attracted considerable attention in the fields of both theoretical research and engineering practice. The core task of ATO is trajectory tracking. As an intelligent control method that imitates human learning behavior, iterative learning control (ILC) has been widely applied to various trajectory tracking problems. Owing to the remarkable repetitiveness of HST operation and distinct characteristics of ILC, ILC is regarded as a promising and potential method to track the desired speed trajectory of HSTs. Therefore, various ILC schemes have been designed for HSTs. This article presents a survey on ILC for HSTs and provides an overview of related research results. First, we provide a brief background on ATO and ILC and explain why the ILC application is considered for the ATO of HSTs. Then, we divide the existing ILC schemes for HSTs into two categories, namely, model based and model free, according to whether they are based on the train dynamic model. Based on these schemes, we introduce model-based and model-free ILC, respectively. Next, considering that HST operation is a complex process that may encounter various issues, we introduce extensions of ILC for HSTs to address these issues. The extensions are summarized from three aspects: accurate dynamic models, safety, and diversified control objectives. Finally, we present promising directions for future research. We believe that the results of this survey can provide a general concept of the current progress in applying ILC to HSTs. [ABSTRACT FROM AUTHOR]

As the informatization, digitization, and intelligence of railways continues to progress, new advantages, such as massive data resources and rich application scenarios, promote the generation of the autonomous intelligent high-speed railway system (AIHSRS). Based on an analysis of the current state of autonomous transportation system research, this article proposes an overall architecture of the AIHSRS, with its connotations and characteristics. Specifically, the fundamental platform of the AIHSRS consists of an entity model layer, data fusion layer, mechanism model layer, and application interface layer. As a result, the AIHSRS supports implementations of autonomous infrastructure, autonomous mobile equipment, autonomous safety and early warnings, autonomous transportation organization, and autonomous transportation service. Finally, the development road map of the AIHSRS is given. [ABSTRACT FROM AUTHOR]

On 15 July, Tobu Railway Co., Ltd. began operating Hitachi Rail’s new Series N100, nicknamed SPACIA X, as shown in. The SPACIA X could reduce CO2 emissions by up to 40% compared to the current SPACIA, and would virtually replace all of the electricity used for operations with electricity from renewable sources, making CO2 emissions net zero. [ABSTRACT FROM AUTHOR]

Abstract Station parking accuracy is an important indicator for the automatic control of high‐speed trains; however, it is subject to many influencing factors, such as the characteristics of high nonlinearity and large time delays in the train control model, time‐variant humidity, and uncertain weather conditions, leading to unsatisfying performance with existing feedback control algorithms. This study first proposes an intelligent train stopping control approach based on deep learning for high‐speed railways. By collecting a large amount of historical train operation data from the Beijing–Shenyang high‐speed railway, three data‐driven models are developed for train stopping control. The first model is based on a deep‐layered feedforward neural network (DNN), which predicts the exact train stopping position with dynamic train running states (position, velocity etc.) as input. Taking advantage of the physical train control models used in practice, the DNN to a convolutional neural network (CNN) is then improved by customizing the convolutional layers of the CNN. To overcome the issues arising from the incompleteness of data samples, a few‐shot convolutional neural network (FSCNN) is further developed to enhance the prediction performance of the CNN. Compared with that of the current method used in practice, the simulation experiments show that the train station parking error can be decreased by 38.6%, 42.8%, and 49.2% by our developed DNN, CNN, and FSCNN, respectively.

Abstract Inspection of the railway tracks requires relying on a precise positioning system that accurately determines the location of potential failures and deficiencies. Therefore, measuring the speed and precise location of rail vehicles is essential. Many methods and approaches have been proposed, but most of them lack several conditions that do not allow them to be optimal for this task. In this paper, the authors present a novel method for measuring the speed of a train by means of the time correlation approach, by using 2‐mm wave radar sensors. Additionally, it is also an accurate positioning system because the authors prove that the track has a unique profile, sufficiently characteristic and stable over time, that serves as a distinctive signature, which can be used to locate the vehicle at any point on that track. As a result, this new methodology, combined with a laser profilometer device, can be used as an effective inspection system for cracks or deficiencies in the rails, sleepers, fastenings, bolts etc., locating them precisely on the railway track.

Once a freight train is delayed on a busy railway under a quasi-moving-block signaling system, the speed of its following trains may fluctuate, without proper adjustment. Updating the reference speed profile of the delayed freight train is an urgent task to improve the train operation performance and rail utilization ratio. To this end, this article proposes a speed profile optimization method for following delayed trains that is based on the idea of model predictive control (MPC). The state change time of the block section ahead is predicted for each following train according to the speed profile of its preceding train, based on which the distance between the following train and each speed protection curve (DTC) is predicted. The DTC is taken as a key optimization index for the speed profile optimization, and the other optimization indices are train delay and energy consumption. A two-step hierarchical optimization algorithm is proposed in this article. In the upper level, each prediction step is defined as the traveling time of the preceding train in each block section ahead, and the train target cruising speed sequence is calculated, with the dimension decided by the prediction horizon, using particle swarm optimization. In the lower level, the optimal speed profile is calculated based on the rolling optimization algorithm of the train speed curve in the prediction horizon. The proposed algorithm repeats the optimization process with updated train information after each control horizon. Three simulations are presented, which consider a downhill scenario, steep uphill scenario, and temporary speed limit, respectively. Then, the MPC parameters are analyzed and the optimized speed profiles are compared with the other algorithm. [ABSTRACT FROM AUTHOR]

Railway point machines are the key equipment that controls the train route and affects the safety of train operation. Complex and harsh working environments lead to frequent failures, accounting for 40% of the total failures of the railway signaling system. Thus, it is an urgent task to present an intelligent fault diagnosis approach. Considering the easy acquisition and anti-interference characteristics of vibration signals, this article develops a vibration signal-based diagnosis approach. First, variational mode decomposition (VMD) is utilized for nonstationary vibration signal preprocessing, which is verified as a more effective tool than empirical mode decomposition. Then, to comprehensively characterize nonlinear fault characteristics, five kinds of entropy are extracted. To eliminate the redundant information of high-dimensional features, kernel principal component analysis is utilized for multientropy feature fusion. Experiment comparisons demonstrate the superiority of the proposed VMD preprocessing and multientropy fusion method. The diagnosis accuracies of normal-to-reverse and reverse-to-normal switching directions reach 96.57% and 99.43%, respectively, which provides theoretical support for onsite operation and maintenance staff. [ABSTRACT FROM AUTHOR]

Thanks to the vigorous development of artificial intelligence, urban rail transit (URT) is undergoing a new round of intelligent upgrades. While its intelligence level is improving, URT suffers from a weak trust foundation, high data sharing costs, and low collaboration efficiency. Driven by outstanding features of decentralization, resilience against tampering, and traceability, blockchain can provide a safe and efficient value-trust exchange infrastructure for URT. This article focuses on the current prospects, case studies, and future challenges of blockchain-empowered URT. We first introduce blockchain fundamentals and mainstream blockchain platforms, comparing the technology’s advantages and highlighting the motivation of using it in URT. The prospects of using blockchain in the lifecycle of URT, which includes planning and construction, operation and management, control and security, and upgrading and transformation, are explored. Furthermore, a concrete case study of using blockchain in a distributed authentication scheme for URT is described. Extensive testing results show that the proposed blockchain-based distributed authentication scheme can enhance the security of the train control system without sacrificing communication performance. Finally, we summarize the challenges and problems when using blockchain in future URT systems. [ABSTRACT FROM AUTHOR]

Virtual coupling allows multiple trains to run synchronously in a virtually coupled formation via train-to-train (T2T) wireless communication. The distance between virtual coupling trains can be reduced greatly to improve the line capacity. However, the time delay in T2T communication cannot be avoided, and the delay may fluctuate due to the uncertainties in the train operation, which will negatively impact the virtual coupling trains. In this article, a particle swarm optimization (PSO)-based online optimization approach for virtual coupling that considers the nondeterministic delay in T2T communication is presented. The optimization objectives include energy consumption, ride comfort, operation speed, and the distance between trains. The PSO algorithm is adopted to solve the problem online so that the train can be controlled according to the optimized traction/braking acceleration. The simulation was conducted with data from the existing Beijing subway line, and the results show that the proposed method reduces the impact of nondeterministic delay on virtual coupling significantly. The speed error of the follower train is below 0.3 m/s, and the tracking distance error is below 0.8 m. Moreover, the requirements of real-time control and ride comfort are satisfied. [ABSTRACT FROM AUTHOR]

Accurately predicting delays for high-speed railways (HSRs) is a challenging yet significant task. The historical operation data of the HSRs, implicating delay derivation rules under the dispatchers’ rescheduling strategies, have sparsity characteristics, resulting in heterogeneous prediction performances under different scenarios. This article proposes a Gaussian noise data augmentation-based delay prediction method to cope with the sparsity. Specifically, the Gaussian noise is added to the original data based on the train operation data characteristics. Then, the delay data rather than the full-state dataset are selected as the training data for different designed machine learning prediction models. Numerous studies based on real HSR operational data from the Beijing Railway Bureau show that the proposed method could significantly improve the prediction accuracy under different scenarios with different machine learning models, verifying the effectiveness of the performance improvement. The relevant results could be significantly helpful for real-time train rescheduling and passenger management, thus improving the emergency response capabilities of HSRs. [ABSTRACT FROM AUTHOR]

Station parking accuracy is an important indicator for the automatic control of high‐speed trains; however, it is subject to many influencing factors, such as the characteristics of high nonlinearity and large time delays in the train control model, time‐variant humidity, and uncertain weather conditions, leading to unsatisfying performance with existing feedback control algorithms. This study first proposes an intelligent train stopping control approach based on deep learning for high‐speed railways. By collecting a large amount of historical train operation data from the Beijing–Shenyang high‐speed railway, three data‐driven models are developed for train stopping control. The first model is based on a deep‐layered feedforward neural network (DNN), which predicts the exact train stopping position with dynamic train running states (position, velocity etc.) as input. Taking advantage of the physical train control models used in practice, the DNN to a convolutional neural network (CNN) is then improved by customizing the convolutional layers of the CNN. To overcome the issues arising from the incompleteness of data samples, a few‐shot convolutional neural network (FSCNN) is further developed to enhance the prediction performance of the CNN. Compared with that of the current method used in practice, the simulation experiments show that the train station parking error can be decreased by 38.6%, 42.8%, and 49.2% by our developed DNN, CNN, and FSCNN, respectively. [ABSTRACT FROM AUTHOR]

Rail transportation plays a crucial role in facilitating efficient and sustainable urban development, particularly in densely populated areas. This study aims to evaluate customer satisfaction with the public transport system in Malaysia, with a focus on rail transportation. The research examines various factors that influence customer satisfaction, including service quality, punctuality, convenience, safety, and cleanliness. The study utilizes a mixedmethods approach, combining secondary data analysis and primary data collection. Secondary data analysis involves a comprehensive review of existing literature, including academic papers, government reports, and industry publications. Primary data is collected through a structured questionnaire administered to a sample of rail transport users in the Klang Valley region. The findings highlight the importance of service quality in shaping customer satisfaction. Factors such as reliable and punctual train arrivals, convenient and comfortable facilities, and a clean and safe environment emerge as key determinants of customer satisfaction. The study also identifies areas for improvement, including enhancing the efficiency of ticketing systems, increasing seating capacity during peak hours, and addressing cleanliness issues at stations and on trains. The research findings have implications for policymakers, transport authorities, and rail operators. By understanding the factors that influence customer satisfaction, stakeholders can design strategies to enhance the quality of rail transportation services. Improving customer satisfaction levels can lead to increased ridership, reduced road congestion, and a more sustainable transport system. The findings can also inform future infrastructure development plans and guide decision-making processes in the public transport sector. Overall, this study contributes to the growing body of knowledge on customer satisfaction in rail transportation and provides valuable insights for improving the quality and effectiveness of Malaysia's public transport system, specifically in the context of rail transportation. [ABSTRACT FROM AUTHOR]

The integrated application of building information modeling (BIM) and big data (BD) has received widespread attention, and has been involved in smart construction sites, construction project management, and construction project budgeting. Nevertheless, research on the integrated implementation of BIM and BD in China mainly concentrates on a project stage or profession, the exploration of technology integration mostly focuses on the theoretical level, and the research distribution is scattered. As such, the intention of this paper is to reveal the research history of BIM and BD in China, as well as to study the research methodologies and research fields for a more thorough knowledge of the BIM and BD research and development status in the Chinese construction sector, which adopts a mixed research method that uses a quantitative research via two analytical software tools, i.e., CiteSpace version of 6.1.R6 and the Statistical Analysis Toolkit on line edition for Informetrics software packages, to conduct a macro bibliometric analysis on BIM and BD research in the China National Knowledge Infrastructure database, and provides a follow-up micro qualitative research with content analysis. To ensure the comprehensiveness of the research, the core articles of the topic in the web of science database have been sorted out and analyzed for fully understanding the development of BIM and BD in the field of Chinese construction, resulting in identifying the current research hotspots and trends in China. The results suggest that the popular research keywords on BIM and BD in China since the year 2015 have mainly been focused on informatization, the internet of things, and rail transportation. Three fruitful research themes in BIM and BD research in China have been identified, including smart construction, smart operation, and bridge informatization. [ABSTRACT FROM AUTHOR]

Reports on public rail transportation projects that are being developed and launched around the world. [ABSTRACT FROM AUTHOR]

Abstract The subjectivity of selecting training parameters is an important factor affecting the accuracy of short‐term passenger flow prediction of rail transit by long short‐term memory (LSTM) neural network. In order to improve the prediction accuracy, an improved particle swarm optimization (IPSO) algorithm is proposed to optimize the LSTM. The size of the learning factor of the particle swarm optimization (PSO) algorithm is controlled by dynamic adjustment method to improve the global optimization and convergence ability of the algorithm. The number of hidden layer nodes, learning rate and iteration times of the LSTM are optimized by IPSO. The passenger flow data of Dongjiekou station of Fuzhou Metro Line 1 are selected for verification, and the proposed model is compared with the traditional prediction model. The results show that the LSTM optimized by the improved particle swarm optimization algorithm can effectively predict the short‐term passenger flow of rail transit. Compared with the PSO‐LSTM model, the root mean square error predicted by the IPSO‐LSTM model decreases by 10.87% in the peak period and by 26% in the off‐peak period. The results can provide theoretical and technical support for the optimization of rail transit operation scheme.

Background. The paper is devoted to the study of trends in the transformation of the transport services market under the influence of international sanctions. It is shown that the functioning of the Russian transport industry is significantly influenced by external factors - constraints that determine the reorientation of logistics chains. The experience of other countries whose economies operate under international sanctions has been studied. Materials and/or research methods. Materials from open Internet sources, methods of system analysis, theory of synergetics, process approach were used. Results. The main directions and results of the transformation of the market of transport services and, in particular, rail transportation are presented. The classification of international sanctions is considered. Conclusion. According to the results of the study, it was found that the trends in the development of the transport services market that contribute to overcoming barriers to its path are: the development of the PL services market; collaboration of services; the departure of “gray” players; market consolidation (consolidation of carriers); reorientation of carriers to the domestic market and the countries of the Asia-Pacific region; development the North-South corridor (China and Kazakhstan as transit countries).

Train vibrations can travel through the track, tunnel structure, and surrounding strata as well as into the structure of buildings near tunnels, where they can be radiated as low-frequency vibration waves and noises inside the building. Low-frequency vibration reduction is a major challenge for vibration control technology. Herein, the new phononic-like crystal metaconcrete material was invented to make the track bed for decreasing vibration induced by trains because it can open low-frequency bandgaps to reduce the low-frequency vibration in actual subway engineering. First, the original phononic-like crystal model was optimized to improve the vibration reduction effect for low-frequency bandgaps. Second, the metaconcrete track beds were conducted according to the new phononic-like crystal model based on the vibration reduction need. Finally, the metaconcrete track beds were installed in the Ning-Ju line of Nanjing Metro in Nanjing, China, and the vibration reduction effect was monitored. The monitoring results in the Ning-Ju line demonstrate that the new phononic-like crystal structure metaconcrete track bed has excellent vibration reduction performance for rail transportation. [ABSTRACT FROM AUTHOR]