Hasil untuk "Railroad engineering and operation"

A. Johansson, Evan Weinberg, Christian Trott et al.

Since its inception in 1995, LAMMPS has grown to be a world-class molecular dynamics code, with thousands of users, over one million lines of code, and multi-scale simulation capabilities. We discuss how LAMMPS has adapted to the modern heterogeneous computing landscape by integrating the Kokkos performance portability library into the existing C++ code. We investigate performance portability of simple pairwise, many-body reactive, and machine-learned force-field interatomic potentials. We present results on GPUs across different vendors and generations, and analyze performance trends, probing FLOPS throughput, memory bandwidths, cache capabilities, and thread-atomic operation performance. Finally, we demonstrate strong scaling on three exascale machines – OLCF Frontier, ALCF Aurora, and NNSA El Capitan – as well as on the CSCS Alps supercomputer, for the three potentials.CCS Concepts• General and reference→Performance; • Software and its engineering→Ultra-large-scale systems; • Computing methodologies→Parallel algorithms; Massively parallel algorithms; Distributed algorithms; Machine learning; Molecular simulation.

Michał Krzysztof

Abstract: This article presents the world's first miniature MEMS scanning electron microscope. The device, thanks to its small size, low power consumption, and durable construction, can be used in previously inaccessible places, including space missions for imaging samples of cosmic dust, lunar, or Martian soil. Keywords: Miniature scanning electron microscope; Electron source; MEMS; Imaging

SU Ke, SU Yongzhang, LIU Yongqiang et al.

Aluminum alloy carbody technology for metro trains in China has developed through four stages: technology introduction, localization breakthrough, enterprise-level independent innovation, and national-level collaborative innovation. With the establishment of the Chinese standard metro train technology platform, China has achieved a leap forward from dependence on imports to the independent development of autonomous and controllable products. This paper presents a systematic review of China’s development journey in aluminum alloy carbody technology for metro trains, using the technology platform for the fourth-generation B-type cars developed by CRRC ZELC as a case study. It clarifies the technological evolution patterns and driving mechanisms throughout this process. The study shows that technological iterations have been synergistically driven by technological innovations, market demands, cost optimization, and policy guidance. Looking ahead, the technology in this field is anticipated to undergo in-depth development towards modularization, sustainability, and intelligence throughout the full life cycle of products, thereby enhancing China's international competitiveness in the rail transit equipment sector.

ZHUO Conglin, ZHU Yutong, YANG Xuesong et al.

For high-speed trains disconnected from external power supply due to faults in the overhead contact system (OCS) and other incidents, using the on-board energy storage devices as power sources to tow these trains to the nearest station is a common self-rescue method. Because on-board power sources have limited capacity, reducing energy consumption during emergency operation to preserve sufficient state of charge (SOC) can increase the success rate of self-rescue. This paper presents an emergency self-traction model for the emergency constant-speed cruise control mode based on the composition of energy consumption. The study focused on analyzing patterns in energy consumption variations with changes in auxiliary power level and speed grade, and identifying the optimal matching relationship between auxiliary power levels and speed grades. This paper also proposed an equivalent simplified analysis method, which was validated through fine simulation. The research results provide reference for the matching design between the target speed and train auxiliary power as part of technical specifications for emergency self-traction in the design stage of EMUs and support subsequent emergency operation decision-making.

Yining Hong, Christopher S. Timperley, Christian Kästner

Machine learning (ML) components are increasingly integrated into software products, yet their complexity and inherent uncertainty often lead to unintended and hazardous consequences, both for individuals and society at large. Despite these risks, practitioners seldom adopt proactive approaches to anticipate and mitigate hazards before they occur. Traditional safety engineering approaches, such as Failure Mode and Effects Analysis (FMEA) and System Theoretic Process Analysis (STPA), offer systematic frameworks for early risk identification but are rarely adopted. This position paper advocates for integrating hazard analysis into the development of any ML-powered software product and calls for greater support to make this process accessible to developers. By using large language models (LLMs) to partially automate a modified STPA process with human oversight at critical steps, we expect to address two key challenges: the heavy dependency on highly experienced safety engineering experts, and the time-consuming, labor-intensive nature of traditional hazard analysis, which often impedes its integration into real-world development workflows. We illustrate our approach with a running example, demonstrating that many seemingly unanticipated issues can, in fact, be anticipated.

Hashini Gunatilake, John Grundy, Rashina Hoda et al.

Empathy, defined as the ability to understand and share others' perspectives and emotions, is essential in software engineering (SE), where developers often collaborate with diverse stakeholders. It is also considered as a vital competency in many professional fields such as medicine, healthcare, nursing, animal science, education, marketing, and project management. Despite its importance, empathy remains under-researched in SE. To further explore this, we conducted a socio-technical grounded theory (STGT) study through in-depth semi-structured interviews with 22 software developers and stakeholders. Our study explored the role of empathy in SE and how SE activities and processes can be improved by considering empathy. Through applying the systematic steps of STGT data analysis and theory development, we developed a theory that explains the role of empathy in SE. Our theory details the contexts in which empathy arises, the conditions that shape it, the causes and consequences of its presence and absence. We also identified contingencies for enhancing empathy or overcoming barriers to its expression. Our findings provide practical implications for SE practitioners and researchers, offering a deeper understanding of how to effectively integrate empathy into SE processes.

Roberto Verdecchia, Justus Bogner

From its first adoption in the late 80s, qualitative research has slowly but steadily made a name for itself in what was, and perhaps still is, the predominantly quantitative software engineering (SE) research landscape. As part of our regular column on empirical software engineering (ACM SIGSOFT SEN-ESE), we reflect on the state of qualitative SE research with a focus group of experts. Among other things, we discuss why qualitative SE research is important, how it evolved over time, common impediments faced while practicing it today, and what the future of qualitative SE research might look like. Joining the conversation are Rashina Hoda (Monash University, Australia), Carolyn Seaman (University of Maryland, United States), and Klaas Stol (University College Cork, Ireland). The content of this paper is a faithful account of our conversation from October 25, 2025, which we moderated and edited for our column.

Dipin Khati, Yijin Liu, David N. Palacio et al.

Applications of Large Language Models (LLMs) are rapidly growing in industry and academia for various software engineering (SE) tasks. As these models become more integral to critical processes, ensuring their reliability and trustworthiness becomes essential. Consequently, the concept of trust in these systems is becoming increasingly critical. Well-calibrated trust is important, as excessive trust can lead to security vulnerabilities, and risks, while insufficient trust can hinder innovation. However, the landscape of trust-related concepts in LLMs in SE is relatively unclear, with concepts such as trust, distrust, and trustworthiness lacking clear conceptualizations in the SE community. To bring clarity to the current research status and identify opportunities for future work, we conducted a comprehensive review of $88$ papers: a systematic literature review of $18$ papers focused on LLMs in SE, complemented by an analysis of 70 papers from broader trust literature. Additionally, we conducted a survey study with 25 domain experts to gain insights into practitioners' understanding of trust and identify gaps between existing literature and developers' perceptions. The result of our analysis serves as a roadmap that covers trust-related concepts in LLMs in SE and highlights areas for future exploration.

Kunigiri Ajay Kumar, H. Ateeq Ahmed

Railroad operations must be reliable, accessible, maintained, and safe (RAMS) for both passenger and freight transit. Railway station safety and risk incidents are a major safety concern for day-to-day operations in many metropolitan settings. Additionally, the incidents cause harm to the market's brand in addition to expenses and injuries to individuals. Higher demand is putting pressure on these stations, using up infrastructure and raising safety administration concerns. It is recommended to employ unsupervised topic modelling to better understand the factors that contribute to these extreme incidents in order to analyse them and use technology, such as artificial intelligence techniques, to improve safety. Latent Dirichlet Allocation (LDA) for fatality accidents at railway stations is optimised using textual data collected by RSSB, which includes 1000 incidents in UK railway stations. This study offers advanced analysis and explains how to improve safety and risk management in the stations by applying the machine learning topic technique for systematic spot accident characteristics. Through information mining, lessons learnt, and a thorough understanding of the danger posed by evaluating deaths in accidents on a broad and long-lasting scale, the study assesses the effectiveness of text. Predictive accuracy for important accident data, such the underlying reasons and the hot spots at train stations, is provided by this intelligent text analysis. Additionally, the advancement of big data analytics leads to a better understanding of the nature of accidents than would be feasible with a large safety history or with a restricted domain examination of accident reports. High precision and a new, advantageous era of AI applications in railway sector safety and other safety-related domains are provided by this technology.

Jon Bokrantz, Mukund Subramaniyan, A. Skoogh

Abstract To support manufacturing firms in realising the value of Artificial Intelligence (AI), we embarked on a six-year process of research and practice to enhance the popular and widely used CRISP-DM methodology. We extend CRISP-DM into a continuous, active, and iterative life-cycle of AI solutions by adding the phase of ‘Operation and Maintenance’ as well as embedding a task-based framework for linking tasks to skills. Our key findings relate to the difficult trade-offs and hidden costs of operating and maintaining AI solutions and managing AI drift, as well as ensuring the presence of domain, data science, and data engineering competence throughout the CRISP-DM phases. Further, we show how data engineering is an essential but often neglected part of the AI workflow, provide novel insights into the trajectory of involvement of the three competences, and illustrate how the enhanced CRISP-DM methodology can be used as a management tool in AI projects.

Ian Germoglio Barbosa, A. Lima, J. Edwards et al.

The primary role of the US Department of Transportation (USDOT) Federal Railroad Administration (FRA) is ensuring the safe operation of railway rolling stock and infrastructure by way of regulatory oversight. FRA regulations require US railroads to conduct visual track inspections as often as twice per week depending on a specific track segment’s FRA track class, which also governs maximum train operating speed. Such inspections are often subjective due to the inherent limitations of human visual inspection and cognition. Additionally, human visual inspections require some level of risk given the need for inspectors to be on track while also consuming valuable network capacity. As a result, and the desire to collect objective data to improve both safety and maintenance planning, railroads are pursuing new means and methods to assess track condition and evaluate track component health. This paper presents a numerical method to define track component health using field data collected on the High Tonnage Loop (HTL) at the Transportation Technology Center (TTC) in Pueblo, Colorado, USA. Line scan laser and image data of the track were captured using a 3D Laser Triangulation system and were subsequently processed using Deep Convolutional Neural Networks (DCNNs). The track heath quantification method proposed establishes benchmarks that were developed based on the understanding of railway track mechanics, high axle load (HAL) railroad engineering instructions, and FRA regulations. The novel metrics presented are referred to as Track Component Heath Indices (TCHIs) and are quantitative values that objectively assess track condition and provide a means to monitor condition change with time and tonnage. These data can be used in conjunction with traditional track geometry and other forms of track heath data (e.g. GPR and rail profile) to more holistically assess the condition of the track structure and its components and ultimately predict its future state.

Shilong Zhang, Changyong Liu, Kailun Feng et al.

PurposeThe swivel construction method is a specially designed process used to build bridges that cross rivers, valleys, railroads and other obstacles. To carry out this construction method safely, real-time monitoring of the bridge rotation process is required to ensure a smooth swivel operation without collisions. However, the traditional means of monitoring using Electronic Total Station tools cannot realize real-time monitoring, and monitoring using motion sensors or GPS is cumbersome to use.Design/methodology/approachThis study proposes a monitoring method based on a series of computer vision (CV) technologies, which can monitor the rotation angle, velocity and inclination angle of the swivel construction in real-time. First, three proposed CV algorithms was developed in a laboratory environment. The experimental tests were carried out on a bridge scale model to select the outperformed algorithms for rotation, velocity and inclination monitor, respectively, as the final monitoring method in proposed method. Then, the selected method was implemented to monitor an actual bridge during its swivel construction to verify the applicability.FindingsIn the laboratory study, the monitoring data measured with the selected monitoring algorithms was compared with those measured by an Electronic Total Station and the errors in terms of rotation angle, velocity and inclination angle, were 0.040%, 0.040%, and −0.454%, respectively, thus validating the accuracy of the proposed method. In the pilot actual application, the method was shown to be feasible in a real construction application.Originality/valueIn a well-controlled laboratory the optimal algorithms for bridge swivel construction are identified and in an actual project the proposed method is verified. The proposed CV method is complementary to the use of Electronic Total Station tools, motion sensors, and GPS for safety monitoring of swivel construction of bridges. It also contributes to being a possible approach without data-driven model training. Its principal advantages are that it both provides real-time monitoring and is easy to deploy in real construction applications.

Ming Zhang

The selection of tunnel construction program for intercity railroad tunnel under the airport must consider the limitations of environmental conditions and the scale of tunnel construction, and the impact of construction on the airport must be controlled within safe limits. Combined with the intercity rail transit tunnel project through the airport area surrounding environment and engineering geological conditions, the use of theoretical analysis, numerical simulation and other methods, comprehensive navigation, technology, construction period, cost, construction impacts and other aspects of the comparative analysis of the tunnel construction method of open excavation method and shield method, the results show that: (1) Due to the soft soil and groundwater in the tunnel excavation area, the risk of open excavation is greater, while the shield method is more suitable for longer silty soil tunnels without affecting the navigation, accordingly, the shield tunnel construction is more suitable for the tunnel construction under the airport. (2) Open excavation method has the shortest construction period to meet the requirements of rail transit, the unit cost is between 1 and 2 shield machines, and the total cost is close to 2 shield machines; for lower total cost, choose 1 shield machine, for shortening the construction period, choose 2 shield machines. (3) The maximum ground settlement caused by shield tunneling construction is 20mm, the maximum vertical differential settlement perpendicular to the tunnel is 2.8cm, and the maximum differential settlement is 0.93 ‰. On the one hand, it meets the requirements of airport settlement control, and on the other hand, it has little impact on the vertical connecting road and west station apron, and will not affect the opening of the airport runway and the operation of the terminal.

SONG Wensheng, ZHANG Sihui, YE Cunxin et al.

With the rapid development and technological maturity of China's rail transit electric traction system, its reliability assessment and intelligent monitoring technology have gradually attracted attention. Digital twin, as a virtual characterization technique for actual physical systems based on data and machine learning, can simulate the behavioral characteristics and monitor parameters of actual physical systems. Therefore, introducing digital twin technology into the rail transit electric traction system field can provide development ideas and technical means for the digital and intelligent monitoring and operation & maintenance of these systems. This paper first provides an overview of the current application of digital twin technology in electric traction systems, and then lists the key technologies required in the construction process of digital twin in electric traction systems and the development of these technologies. In the electric traction system field, digital twin and related technologies are still in the theoretical research stage, with only exploratory research on some subsystems available, and the complete system modeling and intelligent monitoring system has not yet formed. Finally, this paper looks forward to the engineering implementation prospects of digital twin technology in the electric traction system field, and discusses the internal technical problems and external objective challenges it may face when applied in engineering, for providing a reference for the subsequent technical research and practice.

Ebube Alor, Ahmad Abdellatif, SayedHassan Khatoonabadi et al.

Software engineering (SE) chatbots are increasingly gaining attention for their role in enhancing development processes. At the core of chatbots are Natural Language Understanding platforms (NLUs), which enable them to comprehend user queries but require labeled data for training. However, acquiring such labeled data for SE chatbots is challenging due to the scarcity of high-quality datasets, as training requires specialized vocabulary and phrases not found in typical language datasets. Consequently, developers often resort to manually annotating user queries -- a time-consuming and resource-intensive process. Previous approaches require human intervention to generate rules, called labeling functions (LFs), that categorize queries based on specific patterns. To address this issue, we propose an approach to automatically generate LFs by extracting patterns from labeled user queries. We evaluate our approach on four SE datasets and measure performance improvement from training NLUs on queries labeled by the generated LFs. The generated LFs effectively label data with AUC scores up to 85.3% and NLU performance improvements up to 27.2%. Furthermore, our results show that the number of LFs affects labeling performance. We believe that our approach can save time and resources in labeling users' queries, allowing practitioners to focus on core chatbot functionalities rather than manually labeling queries.

Shumei Tao, Liang Ma, Wentao Guo

Railcars arriving at the railroad technical station include transfer and local railcars. Local railcars are loaded, unloaded, and inspected through pick-up and delivery operations before being marshalled into departure trains. The technical station wagon-flow allocation problem, as the core issue of its transportation scheduling, is essentially the process of selecting and matching various wagon-flow, which has profound impact on the overall transportation capacity of the station. Therefore, this article first establishes a constraint programming model to focus on the coordinated optimization problem of wagon-flow allocation and pick-up and delivery operations at the railway technical station. Constraints such operational sequencing, wagon-flow continuity, locomotive usage, and full axle capacity are considered. The primary objective is to maximize the total priority of successfully assembled departure trains. The other objectives include maximizing shunting and track utilization rates and minimizing the number of sources of successful assembled wagon-flow. Additionally, a fast iterative solution algorithm with initial solutions is designed based on constraint propagation and constructive search strategy, achieving hierarchical iterative solution of the entire model. Finally, using actual data from specific district station as a case study, the verification results show that the method proposed can solve the coordination among pick-up, delivery, disassembly and assembly operations. It can achieve comprehensive utilization of wagon-flow and coordinated wagon-flow allocation of departure trains based on decision-making on pick-up and delivery order, and the algorithm's solving efficiency meets the requirements of on-site timeliness.

D. M B

An IoT-based railway track crack and object discovery framework is an innovative arrangement for guaranteeing the security of railroad frameworks. The framework employs a combination of sensors and cameras that are introduced along the railroad track to screen it for any signs of harm or hindrances. The sensors can identify vibrations and changes within the track, which can show the nearness of a break or other sort of harm. The cameras can capture pictures and video of the track, such as fallen branches, flotsam, and jetsam, or indeed individuals or creatures. Once the framework recognizes an issue, it can send an alarm to the railroad administrators or upkeep workforce, permitting them to rapidly address the issue sometime soon if it causes any mischances or delays. An IOT-based railroad track break and protest location framework can incredibly move forward the security and proficiency of railroad operations. Key Words: Embedded system, Internet of things, ultrasonic sensor, IR detector, Arduino Uno, GPS

S. Chandana

For both passenger and freight transportation, railroad operations must be dependable, accessible, maintained, and safe (RAMS). In many urban areas, railway stations risk and safety accidents represent an essential safety concern for daily operations. Moreover, the accidents lead to damage to market reputation, including injuries and anxiety among the people and costs. This stations under pressure caused by higher demand which consuming infrastructure and raised the safety administration consideration. To analysing these accidents and utilising the technology such AI methods to enhance safety, it is suggested to use unsupervised topic modelling for better understand the contributors to these extreme accidents. It is conducted to optimise Latent Dirichlet Allocation (LDA) for fatality accidents in the railway stations from textual data gathered RSSB including 1000 accidents in the UK railway station. This research describes using the machine learning topic method for systematic spot accident characteristics to enhance safety and risk management in the stations and provides advanced analysing. The study evaluates the efficacy of text by mining from accident history, gaining information, lesson learned and deeply coherent of the risk caused by assessing fatalities accidents for large and enduring scale. This Intelligent Text Analysis presents predictive accuracy for valuable accident information such as root causes and the hot spots in the railway stations. Further, the big data analytics ’ improvement results in an understanding of the accidents’ nature in ways not possible if a considerable amount of safety history and not through narrow domain analysis of the accident reports. This technology renders stand with high accuracy and a beneficial and extensive new era of AI applications in railway industry safety and other fields for safety applications.

Kenedy Greyson, Georgia Rugumira, A. Yusufu

The recent development of High-Speed Trains (HST) operations requires real-time information transfer, high network capacity, and reliable communication for train signalling and control purposes. Rail operators face challenges as the high-speed railroad traffic grows. In a high-speed driving profile, the communication must be able to overcome fast handover, large Doppler shift, high penetration losses, limited visibility in tunnels, and the harsh electromagnetic environment. Global System for Mobile Communications for Railway (GSM-R) is still a standard-bearer for railway signalling communications. The link capacity and coverage prediction of the predefined Base Transceiver Station (BTS) locations are simulated using the Radio Planner RF planning tool and Matlab®. The terrain map and the signal levels along the railway corridor are observed. The analysis of the GSM-R for a fast-moving train is performed. Signal level coverage prediction, Doppler-shift effect and the received carrier frequencies due to Doppler shift along Dar–Pugu stations have been presented. The critical sites (locations) with weak signals of the first three BTSs have been identified. It is also observed that the Doppler effect may limit the proposed speed.

Rudianto Surbakti

Analysis of the soil's bearing capacity for the loads acting on the railway tracks is one of the most important parts of planning the structure of the railway to be built. Considering that train activity is very intense and imposes a significant live load on the soil layer beneath it, it is necessary to ensure that the soil layer under the railroad track is able to withstand the entire load. This analysis aims to ensure that the subgrade is able to support the entire load acting on it by calculating the safety factor for the layers of soil and ballast on the railroad track. The calculation method used to obtain the load value is analytical, while the calculation of safety factors and soil settlement is carried out using the finite element method (FEM) calculation concept. From the results of analysis using the finite element method (FEM) using Plaxis 2D, it can be concluded that at the operational stage the maximum drop in the vertical direction was -5.96 mm while the maximum movement in the horizontal direction was 1.12 cm. This is smaller than the maximum drop permitted by Minister of Transportation Regulation No.  60 of 2012, namely 10 cm. The safety factor during train operations is 2.902, so the railroad construction can be declared safe.