An Overview of Current Challenges and Emerging Technologies to Facilitate Increased Energy Efficiency, Safety, and Sustainability of Railway Transport

Bondarenko I., Campisi T., Tesoriere G., Neduzha L.

The ability to assess the risks of the functional safety of railway tracks allows harmonizing characteristics of track elements under certain operating conditions under certain maintenance for the efficient use of the track structure throughout its life cycle. The concept of detailing conditions of the interaction of the rolling stock and railway track was used for the productive solution of tasks of infrastructure functional safety assessment. The paper formed an approach to the analytical solution of determination problems of deformability parameters over time using the elastic waves theory. The formation method of interconnections between the technical and economic aspects of the operation of railway infrastructure was proposed. The criteria of deformability work and intensity of use were utilized for the effective use of the track structure through its life cycle. The results of calculations are presented to assess changes in the deformability behaviour of the track elements and structure when the force and speed parameters of the operating conditions change, as well as the algorithm of the method for estimating the operation deformability of the railway track. Thus, the proposed approach can be adapted to optimize objects by railway functional safety assessment at the stage of object operation simulation.

Wang Z., Liu X.

Along with the increasing application of different cyber-physical systems (CPSs) to connect various components in the rail industry, rising connectivity through communication technologies has also introduced cyber threats against rail-CPSs, causing failures with huge consequences. Implementations of rail-CPSs demand proactive identification, clear-cut definition, and proper handling of their cyber security risks. In this paper, we formulate a risk management methodology for cyber security in rail-CPSs and conduct a retrospective case study on the Advanced Train Control System (ATCS) that has been deployed in many U.S. freight railways. The methodology provides two alternative approaches to fill knowledge gaps in contingency preparation, threat prevention, consequence analysis, and security risk mitigation. In the case study, we demonstrate two cyber threats of ATCS, using attack sequence modeling and consequence analysis, and provide recommendations for risk mitigation. By practicing the methodology with the case study, this work can be used as a general reference to conduct cyber risk management and cyber-robustness evaluations for other existing systems.

Gao S., Li M., Zheng Y., Zhao N., Dong H.

This paper presents a fuzzy adaptive protective control method for autonomous high-speed trains (HSTs) automatic operation using a new outstretched error feedback design approach. In order to stabilizing the error dynamics with respect to target position and speed profiles of controlled HSTs, nonlinear transformation in prescribed performance control methodology is used to convert running states subject to protective (constrained) information, imposed by automatic train protection (ATP) subsystem, to new coordinates in unconstrained form. By blending a new outstretched error feedback and fuzzy approximation, it is guaranteed above-mentioned errors are kept within regions characterized by error boundary (or prescribed performance) functions and control parameters simultaneously, which can be adjusted to arbitrarily small even without error decreasing boundary functions. Fuzzy approximation is used in compensating unknown running resistances. It is rigorously proved that the resulting closed-loop system is stable in sense of Lyapunov stability in the presence of unknown resistance, containing basis and aerodynamic resistances with uncertain parameters and piecewise continuously slope resistance over varying gradient profile. The proposed control is demonstrated to be effective by comparative simulations of train G1 running on Beijing-Shanghai railway line.

Tang R., De Donato L., Bes̆inović N., Flammini F., Goverde R.M., Lin Z., Liu R., Tang T., Vittorini V., Wang Z.

Nowadays it is widely accepted that Artificial Intelligence (AI) is significantly influencing a large number of domains, including railways. In this paper, we present a systematic literature review of the current state-of-the-art of AI in railway transport. In particular, we analysed and discussed papers from a holistic railway perspective, covering sub-domains such as maintenance and inspection, planning and management, safety and security, autonomous driving and control, revenue management, transport policy, and passenger mobility. This review makes an initial step towards shaping the role of AI in future railways and provides a summary of the current focuses of AI research connected to rail transport. We reviewed about 139 scientific papers covering the period from 2010 to December 2020. We found that the major research efforts have been put in AI for rail maintenance and inspection, while very limited or no research has been found on AI for rail transport policy and revenue management. The remaining sub-domains received mild to moderate attention. AI applications are promising and tend to act as a game-changer in tackling multiple railway challenges. However, at the moment, AI research in railways is still mostly at its early stages. Future research can be expected towards developing advanced combined AI applications (e.g. with optimization), using AI in decision making, dealing with uncertainty and tackling newly rising cybersecurity challenges. • A comprehensive survey on the state-of-the-art applications of AI in railways in a wide spectrum. • In-depth statistics and distributions of the surveyed papers are provided. • Gaps are identified and future directions are discussed.

Shangguan W., Luo R., Song H., Sun J.

Resilience adjustment refers to the generation of a control strategy by evaluating the interaction between related factors. Tracking intervals of the high-speed train platoon change dynamically, which directly influences the operation safety and efficiency, and constrains the train operation trajectories. In China, the tracking interval is getting shorter. To ensure safety and improve efficiency, we research a dynamic interval resilience adjustment strategy based on the moving block system. Firstly, the optimal offline operation strategy is obtained by solving the multi-objective optimization model with the improved gravitational search algorithm (I-GSA). The resilience adjustment mechanism is developed to evaluate the tracking interval and choose the appropriate driving strategy to adjust operation states based on the resilience tracking interval model. Then, we study the relation between operation strategy and departure interval, and a seeker optimization algorithm (SOA) is used to obtain the optimal departure intervals and driving strategies. Simulations are conducted based on the sections between Chibi North station and Changsha South station in Wuhan-Guangzhou high-speed railway. The results indicate that the total operation time decreased by 191s and the operation safety can be ensured at any time.

Kour R., Patwardhan A., Thaduri A., Karim R.

Digitalisation is transforming the railway globally. One of the major considerations in digital transformation of any industry including the railway is the increased exposure to cyberattacks. The railway industry is vulnerable to these attacks because since the number of digital items and also number of interfaces between digital and physical components in the railway systems keep increasing. Increased number of items and interfaces require new frameworks, concepts and architectures to ensure the railway system’s resilience with respect to cybersecurity challenges, such as lack of proactiveness, lack of holistic perspective and obsolescence of safety systems exposed to current and future cyber threats landscape. To this date, there are several works carried out in the literature that studied the cybersecurity aspects and its application on railway infrastructure. However, to develop and implement an appropriate roadmap to cybersecurity in railways, there is a need of describing emerging challenges, and approaches to deal with these challenges and the possibilities and benefits of these. Hence, the objective of this paper is to provide a systematic review and outline cybersecurity emerging trends and approaches, and also to identify possible solutions by querying literature, academic and industrial, for future directions. The authors of this paper conducted separate searches through four popular databases, that is, Google Scholar, Scopus, Web of Science and IEEE explore. For the screening process, authors have used keywords with Boolean operators and database filters and identified 90 articles most relevant to the study domain. The analysis of 90 articles shows that majority of the cybersecurity studies lies within the railways are conceptual and lags in application of Artificial Intelligence (AI) based security. Like other industries, it is very important that railways should also follow latest security technologies, trends and train their workforce for cyber hygiene since railways are already in digitalization transition mode.

Chu W., Vicidomini M., Calise F., Duić N., Østergaard P.A., Wang Q., da Graça Carvalho M.

The COVID-19 pandemic has had a significant impact on the supply chains of traditional fossil fuels. According to a report by the International Energy Agency (IEA) from 2020, oil-refining activity fell by more than the IEA had anticipated. It was also assumed that the demand in 2021 would likely be 2.6 million bpd below the 2019 levels. However, renewable markets have shown strong resilience during the crisis. It was determined that renewables are on track to meet 80% of the growth in electricity demand over the next 10 years and that sustainable energy will act as the primary source of electricity production instead of coal. On the other hand, the report also emphasized that measures for reducing environmental pollution and CO2 emissions are still insufficient and that significant current investments should be further expanded. The Sustainable Development of Energy, Water and Environment Systems (SDEWES) conference series is dedicated to the advancement and dissemination of knowledge on methods, policies and technologies for improving the sustainability of development by decoupling growth from the use of natural resources. The 15th SDEWES conference was held online from 1–5 September 2020; more than 300 reports with 7 special sections were organized on the virtual conference platform. This paper presents the major achievements of the recommended papers in the Special Issue of Energies. Additionally, related studies connected to the above papers published in the SDEWES series are also introduced, including the four main research fields of energy saving and emission reduction, renewable energy applications, the development of district heating systems, and the economic assessment of sustainable energy.

Logan K.G., Hastings A., Nelson J.D.

Cano M., Pastor J.L., Tomás R., Riquelme A., Asensio J.L.

Many bridges and other structures worldwide present a lack of maintenance or a need for rehabilitation. The first step in the rehabilitation process is to perform a bridge inspection to know the bridge′s current state. Routine bridge inspections are usually based only on visual recognition. In this paper, a methodology for bridge inspections in communication routes using images acquired by unmanned aerial vehicle (UAV) flights is proposed. This provides access to the upper parts of the structure safely and without traffic disruptions. Then, a standardized and systematized novel image acquisition protocol is applied for data acquisition. Afterwards, the images are studied by civil engineers for damage identification and description. Then, specific structural inspection forms are completed using the acquired information. Recommendations about the need of new and more detailed inspections should be included at this stage when needed. The suggested methodology was tested on two railway bridges in France. Image acquisition of these structures was performed using an UAV for its ability to provide an expert assessment of the damage level. The main advantage of this method is that it makes it possible to safely accurately identify diverse damages in structures without the need for a specialised engineer to go to the site. Moreover, the videos can be watched by as many engineers as needed with no personal movement. The main objective of this work is to describe the systematized methodology for the development of bridge inspection tasks using a UAV system. According to this proposal, the in situ inspection by a specialised engineer is replaced by images and videos obtained from an UAV flight by a trained flight operator. To this aim, a systematized image/videos acquisition method is defined for the study of the morphology and typology of the structural elements of the inspected bridges. Additionally, specific inspection forms are proposed for every type of structural element. The recorded information will allow structural engineers to perform a postanalysis of the damage affecting the bridges and to evaluate the subsequent recommendations.

Naseri F., Karimi S., Farjah E., Schaltz E.

Recent advances in energy storage systems have speeded up the development of new technologies such as electric vehicles and renewable energy systems. In this respect, supercapacitors have gained interest due to their unique features such as high power density, long lifespan, and wide operating range. To achieve the high-voltage levels required for vehicular or utility applications, a supercapacitor pack should contain hundreds of high-capacity series-parallel cells. The internal states of these cells cannot be obtained by direct measurements and these states are usually affected by operating conditions such as temperature and noise. In addition, due to the uncertainty in the manufacturing processes, the characteristics between different batches or even the same batch of supercapacitor cells will be unavoidably different, which will impose significant challenges in terms of uniformity, functional safety, and durability of the system. Therefore, the supercapacitor pack will require a management system to effectively monitor, control, and protect the cells along all performance boundaries. Based on a comprehensive review of the latest articles and achievements in the field, as well as some useful previous experiences of the authors, this paper provides an overview of the key technologies, functionalities, and requirements for Supercapacitor Management Systems (SMSs). To the best of the author's knowledge, this is the first survey that provides an inclusive collection of key requirements for the SMS, including issues related to the modeling, estimation, control, and protection of the supercapacitors. The supercapacitor is a relatively new technology and no international standard about SMSs and their functional requirements are available up to date. The present survey will perfectly fill these gaps. In the survey, the key SMS requirements are broadly divided into the software and hardware functions, and several key issues including modeling and state estimation functions, control and balancing circuits, etc. Are covered. A comprehensive review of the supercapacitor/SMS vehicular applications is also provided in this paper. • Supercapacitors can be used as power buffers in e-mobility applications. • Supercapacitor packs face serious challenges regarding performance and functional safety. • SMS can monitor and control the supercapacitor pack along all performance boundaries. • An effective SMS improves the performance and lifetime of supercapacitor packs. • SMS functional requirements are comprehensively reviewed in this paper.

Liu H., Ma J., Jia L., Cheng H., Gan Y., Qi Q.

Hydrogen has been advocated as a promising energy carrier for railway systems, but its limited energy density may introduce mileage anxiety into the transportation system. Motivated by nonstop power exchange design, this article proposes a novel facility planning model under energy transfer conditions and natural endowments, such as photovoltaic resources in geographical locations, to optimize the overall system benefit. The supply, consumption, and replenishment mechanisms of multienergy forms (hydrogen energy, photovoltaic, electric energy) are designed on both vehicle and ground sides of the power exchange system. The optimal facility location and allocation of photovoltaic hydrogen plants, movable tank shifting devices, and train cars are collaboratively decided. A Lhasa–Xining railway case study is applied by conducting sensitivity analyses on significant parameters. Various insights hold the promise to promote policies and strategies for integrating transportation and power systems in a real-world application. It is demonstrated that energy cost reduction, solar power generation improvement, and energy-carrier capacity expansion help solve the mileage anxiety problem in nonstop power exchange systems.

Burke A.F., Zhao J.

This paper is concerned with the development and performance of high-energy density electrochemical supercapacitors (ECCs) and their application in HEVs, PHEVs, and HFCVs. Detailed test data are shown for the Skeleton Technology 5000 F carbon/carbon EDLC device and the Aowei 9000 F hybrid (4 V) supercapacitor (HSC). The EDLC device had an energy density of 8.4 Wh/kg and the hybrid SC had an energy density between 30 and 65, depending on its rated voltage and the power of the discharge. These energy densities are significantly higher than previous ECCs tested. They indicate that good progress is being made in increasing the energy density of commercial ECCs. Vehicle applications of the advanced ECCs were evaluated based on Advisor simulations on city and highway driving cycles. Simulations were made for six vehicle types ranging from compact passenger cars to Class 8 long haul trucks. The fuel economy was calculated for each vehicle type using a lithium battery, the EDLC Skeleton Technology capacitor and the two Aowei hybrid capacitors as energy storage in the powertrain. The 4.1 V hybrid capacitor in all cases was lighter and smaller than the lithium battery. The fuel economies of the HEVs on the FUDS cycle were significantly higher (30–50%) than that of the corresponding ICE vehicle, except for the long haul truck, for which the fuel economy improvement was 20%. In almost all cases, the fuel economy improvement was highest when using the 4.1 V hybrid capacitor. Simulations were also run for fuel cell-powered vehicles. For the fuel cell vehicles, the fuel economies using the three energy storage technologies varied only slightly. For all the fuel cell vehicles simulated, the 4.1 V hybrid capacitor was the lightest and smallest of the energy storage options, and produced the best fuel economy. As in the case of HEVs, the hybrid capacitors appeared to be the best option for energy storage in fuel cell vehicle applications.

Landgraf M., Zeiner M., Knabl D., Corman F.

The production and provision of railway infrastructure causes environmental impacts for several environmental indicators. The goal of this study is a single-score evaluation for environmental impacts of turnouts by calculating associated environmental costs. The methodology includes the life cycle assessment using the CML-IA baseline method to calculate mid-point indicators. Monetary valuation models are investigated as a basis for environmental pricing. Turnouts equipped with USP result in lower annual environmental costs (EC) of €429 than turnouts with conventional concrete sleepers (€495). Of the assessed impact categories, global warming potential (GWP) is responsible for the vast majority of EC with values from 87% to 97%. The main uncertainties lie within the emission factors to calculate mid-point indicators and the deviation within the environmental pricing schemes. Steel and concrete production, circular economy, use of alternative propulsion systems for track work machinery, and efficient maintenance strategies are major potentials for mitigation.

Sresakoolchai J., Kaewunruen S.

Building Information Modeling (BIM) has been used in various industries for a long time. The railway system is another industry where BIM plays an important role. Since BIM can contain project information in different stages, a pool of information is involved and included in BIM. To use this information efficiently, machine learning, as a branch of artificial intelligence, is one of the tools widely applied nowadays. However, integrating BIM and machine learning in the railway system is new. This study is thus the world’s first to integrate BIM and machine learning to localize defects in the railway infrastructure. In this study, wheelburns are used as case studies. Machine learning techniques used to localize defects are Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). From the study, the developed BIM model can be fully integrated with machine learning to localize defects in the railway infrastructure using the developed workflow. It is found that the CNN model provides the best outcome when Mean Absolute Error (MAE) is used as the main indicator. The MAE of the CNN model is 0.03 m and the Max Error (ME) is 0.3 m. The results of the study show that the integration of BIM and machine learning can be achieved and provide advantages to the railway industry. The developed machine learning models provide satisfactory performance and will be beneficial for the railway industry for better asset management and cost-effective maintenance.

Chehri A., Chaibi H., Saadane R., Ouafiq E.M., Slalmi A.

Manufacturing industry is continuously evolving since the very beginning of the industrial era. This modernization is undoubtedly the outcome of continuous new technology development in this field, which has kept the industries on the verge, looking for new methods for improving productivity enhancement and better operational efficiency. The advent of 5G will provide the world of industry to connect its infrastructures to digitize people and machines to optimize production flows. Narrow-band-IoT addresses “Massive IoT” type use cases, which involve deploying a large energy-efficient quantity. These low-complex objects do not need to communicate very frequently. 5G will provide the ability to develop new uses previously impossible or complex to implement. Consequently, it will complement the range of network solutions already in place in the company, giving it the keys to accelerating its transformation. This paper evaluates the 5G-NR-based IoT air interface with the FEC with industrial channel models. Low-density parity-check (LDPC), polar, turbo code, and TBCC are assumed.

Rakhmangulov A., Osintsev N., Mishkurov P.

Intelligent and information systems in transportation record and accumulate large volumes of raw data on dynamic transportation processes. However, these data are not fully utilized for forecasting, real-time planning, and transportation management. Spatio-temporal graphs allow describing simultaneously both the structure of transportation systems of different modes of transportation and the dynamics of transportation flows. Optimization of such graphs makes it possible to justify management decisions in real time, as well as to forecast the parameters of traffic flows and transportation processes. The purpose of the study is to identify trends in the use of spatio-temporal graphs for solving various problems in transportation, as well as the most common methods of optimization of such graphs. The sample papers studied include 114 publications from the Scopus database over 25 years, from 1999 to 2024. First, a bibliometric analysis was conducted to establish the increase in the number of publications, journals, countries, institutions, subject areas, articles, authors, and keyword matches, to understand the amount of literature generated. Secondly, a literature review was conducted based on content analysis to predict future research directions in the field. We have found that the development of deep learning methods and approaches for designing graph neural networks based on spatio-temporal graphs is a promising direction. Such methods are mostly used to solve the tasks of real-time control of urban transportation systems. There are fewer publications in areas that require in-depth knowledge of transportation technology, such as air, sea, and rail transportation. This study contributes to the expansion of scientific knowledge about methods of spatio-temporal optimization of transport systems based on bibliometric analysis.

Banic M., Ristic-Durrant D., Madic M., Klapper A., Trifunovic M., Simonovic M., Fischer S.

Satellite data have the potential to significantly enhance railway operations and drive the digitization of the rail sector. In the context of railways, satellite data primarily refers to the use of Global Navigation Satellite System (GNSS) data for applications such as navigation, positioning, and signalling. However, remote sensing data from Earth Observation (EO) satellites remain comparatively underutilized in railway applications. While the use of GNSS data in railways is well documented in the literature, research on EO-based remote sensing methods remains relatively limited. This paper aims to bridge this gap as it presents a comprehensive review of the use of satellite data in railway applications, with a particular focus on the underexplored potential of EO data. It provides the first in-depth analysis of EO techniques, primarily examining the use of synthetic aperture radar (SAR) and optical satellite data for key applications for infrastructure managers and railway operators, such as assessing track stability, detecting deformations, and monitoring surrounding environmental conditions. The goal of this review is to explore the diverse range of EO-based applications in railways and to identify emerging trends, including the integration of thermal EO data and the novel use of SAR for dynamic and predictive analyses. By synthesizing existing research and addressing knowledge gaps, the presented review underscores the potential of EO data to transform railway infrastructure management. Enhanced spatial resolution, frequent revisit cycles, and advanced AI-driven analytics are highlighted as key enablers for safer, more reliable, and cost-effective solutions. This review provides a framework for leveraging EO data to drive innovation and improve railway monitoring practices.

Sim J., Kim S., Cho I.

Battery modules in eco-friendly mobility are composed of series and parallel connections of multiple lithium-ion battery cells. As the number of lithium-ion cells in the battery module increases, the cell connection configuration becomes a critical factor affecting the module’s usable capacity efficiency. Therefore, careful consideration of this factor is essential in battery module design. Various design elements have been studied to optimize the performance of battery modules. Among these elements, the method of terminal connection affects the distribution of resistance components in each cell, causing DOD (Depth of Discharge) variation. Previous research has focused on determining the optimal terminal placement and cell connection method to minimize DOD variation between cells. However, these studies did not consider temperature effects. Since temperature acts as a major variable affecting the DOD of each cell, comprehensive research that includes this factor is necessary. This research performed 3D thermal flow analysis using Ansys Fluent 2024 R2 and validated the simulation environment by comparing actual experimental and simulation results for a single cell. Based on the validated simulation environment, this research analyzed the impact of temperature distribution on cell performance in a 4S3P module and proposed a method of terminal connection, which achieved a 70% reduction in SOC deviation compared to conventional methods. Additionally, this research suggests that when the module configuration changes, a new design approach specific to that configuration is necessary to minimize SOC deviation.

Bulakh M.

This paper presents an evaluation and reduction of energy consumption during railway train movement on a straight track section with reduced freight wagon mass. A theoretical model was developed to simulate energy consumption based on input parameters, including train speed, track gradient, section length, travel time, and train mass. The results indicate that energy consumption increases by 18.9% as speed rises to 90 km/h and as gradients increase to 2.0‰, while energy consumption decreases by 14.5% on a descending gradient of 1.5‰, which corresponds to the expected dynamics of railway trains. These results are supported by experiments showing that the MAPE error does not exceed 1.9%, which can confirm the accuracy of the developed model. A comprehensive analysis of the potential reduction in energy consumption with reduced freight wagon mass was also conducted. Using a freight wagon design with a reduced mass of 2.3% allows for a reduction in energy consumption by 8–89 kW·h, depending on the length of the section and the speed of movement.

Nowotarski P., Gajzler M.

Railways play a key role in sustainable development, being one of the most ecological means of transport. The article discusses the challenges and opportunities related to the maintenance of the railway station infrastructure in Poland, in the context of modern predictive technologies. The Eurail FP3 project, implemented under a European Union grant, focuses on the development of the modern solutions for the maintenance of railway infrastructure, including the possibility of using a modern approach to monitoring the technical condition of buildings in real time, which allows for the prediction of faults and the optimization of the maintenance work. The authors of the article analyze the data obtained from the main manager of the railway infrastructure in Poland regarding the station maintenance procedures and present a new approach for a maintenance procedure, which assumes the inclusion of predictive technologies. Thanks to this, it is possible to optimize the maintenance processes of the station infrastructure, which in the long term will affect the possibility of the current access to data on the condition of buildings in real time and will affect the operating costs related to the maintenance of the station facilities, as well as limiting the negative impact on the environment. The analysis carried out as part of the completed works has also revealed the threats and difficulties related to the costs and technological limitations related to the implementation of the maintenance policy, while indicating the directions of further works to ensure the proper efficiency of the railway infrastructure.

Wei D., Zhang W., Li H., Jiang Y., Xian Y., Deng J.

To lighten the workload of train drivers and enhance railway transportation safety, a novel and intelligent method for railway turnout identification is investigated based on semantic segmentation. More specifically, a railway turnout scene perception (RTSP) dataset is constructed and annotated manually in this paper, wherein the innovative concept of side rails is introduced as part of the labeling process. After that, based on the work of Deeplabv3+, combined with a lightweight design and an attention mechanism, a railway turnout identification network (RTINet) is proposed. Firstly, in consideration of the need for rapid response in the deployment of the identification model on high-speed trains, this paper selects the MobileNetV2 network, renowned for its suitability for lightweight deployment, as the backbone of the RTINet model. Secondly, to reduce the computational load of the model while ensuring accuracy, depth-separable convolutions are employed to replace the standard convolutions within the network architecture. Thirdly, the bottleneck attention module (BAM) is integrated into the model to enhance position and feature information perception, bolster the robustness and quality of the segmentation masks generated, and ensure that the outcomes are characterized by precision and reliability. Finally, to address the issue of foreground and background imbalance in turnout recognition, the Dice loss function is incorporated into the network training procedure. Both the quantitative and qualitative experimental results demonstrate that the proposed method is feasible for railway turnout identification, and it outperformed the compared baseline models. In particular, the RTINet was able to achieve a remarkable mIoU of 85.94%, coupled with an inference speed of 78 fps on the customized dataset. Furthermore, the effectiveness of each optimized component of the proposed RTINet is verified by an additional ablation study.

Anwer I., Javid M.A., Yousuf M.I., Farooq M., Ali N., Suparp S., Hussain Q.

This paper focuses on the perspectives of passengers who were railway users and how railways as a service can be uplifted with technological advancements through the introduction of information and communication technologies (ICTs). For this purpose, a questionnaire was designed comprised of six sections related to information on socio-economic-demographics, travel, station facilities, train facilities, customer care, and familiarity with and benefits of ICTs. A total of 800 respondents were recruited on trains and in railway stations to collect data through a random sampling technique. Data were analyzed through descriptive statistics, factor analysis, bivariate correlation analysis, and ordered logistic regression analysis. The three hypotheses tested showed that (i) there is a correlation between socio-demographic factors, train frequency, and satisfaction levels, (ii) satisfaction with station and train facilities and customer care impacts users’ travel likelihood with the train service, and (iii) users’ familiarity with perceived benefits of ICTs influences passengers’ travel likelihood with the train service. The results indicate that the users’ satisfaction with attributes of station facilities, train facilities, and customer care and perceptions about ICTs significantly influences their travel frequency with the train service. This study is useful for multiple stakeholders, especially for railway management authorities, to provide inclusive services to passengers and to plan for future transportation, which should be well-equipped with ICTs, well-integrated with other transport modes, and well-connected with optimum stops.

Mittal M., Alsalami Z., Kumar R., Boob N.S., Verma V., Sangeeta K.

Guo Y., Yu S., Xu Y.

Riabov I., Goolak S., Neduzha L.

The method of improving a two-section mainline diesel locomotive by using energy storage in the traction system is considered. A mathematical model was developed to study the movement of a diesel locomotive based on the recommendations and provisions of the theory of locomotive traction. For this purpose, the movement of a diesel locomotive as part of a train along a given section of a track was studied. It was determined that the use of an energy storage device on a diesel locomotive will allow up to 64% of the energy spent on train traction to accumulate. The use of energy storage in the accumulator during electrodynamic braking ensured a reduction in fuel consumption by about 50%, regardless of the options for equipping the traction system of the diesel locomotive with an energy accumulator. It is established that regardless of the options for equipping the traction system of the diesel locomotive with an energy storage device, the indicators characterizing the degree of use of the diesel engine do not change. These research results can be used in works devoted to the improvement of the control system of energy exchange between the accumulator and traction engines of diesel locomotives.