Special Issues
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Artificial Intelligence and Advanced Computation Technology in Railways

Submission Deadline: 28 February 2026 View: 841 Submit to Special Issue

Guest Editors

Prof. Dr. Won-Hee Park

Email: whpark@krri.re.kr

Affiliation: Korea Railroad Research Institute, Korea University of Science and Technology

Homepage:

Research Interests: Artificial Intelligent in railways, numerical and experimental technology for fire phenomena, optimization technique, etc.

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Dr. Su-hwan Yun

Email: shyun@krri.re.kr

Affiliation: Korea Railroad Research Institute

Homepage:

Research Interests: Artificial Intelligence in Railway Systems, Optimization Design, Numerical Analysis, and Safety.

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Prof. Dr. Hong-Lae Jang

Email: hjang@ut.ac.kr 

Affiliation: Korea National University of Transportation

Homepage:

Research Interests: Computational Mechanics, Design Optimization, Railway crashworthiness, Railway Safety.

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Dr. Min-kyeong Kim

Email: mkkim15@krri.re.kr

Affiliation: Korea Railroad Research Institute

Homepage:

Research Interests: Artificial Intelligent in railways, Data analysis of drone and lidar technology applications, Environmental impact assessment, Research using brain waves etc.

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Summary

The railway industry is undergoing a transformative shift driven by advancements in Artificial Intelligence (AI) and advanced computation technologies. These technologies are revolutionizing how railways are designed, managed, and operated, leading to significant improvements in safety, efficiency, and passenger experience. To explore these developments and foster collaboration among researchers, practitioners, and industry experts, we propose a special session dedicated to "Artificial Intelligence and Advanced Computation Technology in Railways" as part of our upcoming paper collection initiative.


The primary objectives of this special session are to:

· Provide a platform for researchers and professionals to present their latest findings, innovations, and case studies related to AI and computation technology in the railway sector.

· Facilitate discussions on the challenges, opportunities, and future directions of AI applications in railways.

· Encourage cross-disciplinary collaboration between academia and industry to accelerate the adoption of advanced technologies in railway systems.

· Highlight the potential of AI and computation technologies to enhance railway operations, safety, and passenger experience.


The special session will cover a wide range of topics, including but not limited to:

· Highlight the potential of AI and computation technologies, including AI vision data construction, to enhance railway operations, safety, and passenger experience.

· Advanced Computation in Railways: High-performance computing, simulation techniques, and digital twins for railway infrastructure and operation optimization.

· Safety and Security Enhancements: AI-based solutions for railway safety, accident prediction, risk assessment, and cybersecurity measures.

· Passenger Experience: Enhancing passenger services through AI-driven solutions such as personalized travel recommendations, real-time information, and smart ticketing.

· Data Analytics and IoT in Railways: Leveraging big data, IoT, and sensor networks for improved asset management, operational efficiency, and real-time monitoring.

· Case Studies and Real-world Applications: Presentations on successful implementations and pilot projects demonstrating the impact of AI and computation technologies in railways.


Keywords

Artificial Intelligence (AI), AI Vision Data Construction, Internet of Things (IoT) in Transportation, Big Data Analytics in Railways, Advanced Computation in Railways

Published Papers


  • Open Access

    ARTICLE

    Pyramid–MixNet: Integrate Attention into Encoder-Decoder Transformer Framework for Automatic Railway Surface Damage Segmentation

    Hui Luo, Wenqing Li, Wei Zeng
    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1567-1580, 2025, DOI:10.32604/cmc.2025.062949
    (This article belongs to the Special Issue: Artificial Intelligence and Advanced Computation Technology in Railways)
    Abstract Rail surface damage is a critical component of high-speed railway infrastructure, directly affecting train operational stability and safety. Existing methods face limitations in accuracy and speed for small-sample, multi-category, and multi-scale target segmentation tasks. To address these challenges, this paper proposes Pyramid-MixNet, an intelligent segmentation model for high-speed rail surface damage, leveraging dataset construction and expansion alongside a feature pyramid-based encoder-decoder network with multi-attention mechanisms. The encoding network integrates Spatial Reduction Masked Multi-Head Attention (SRMMHA) to enhance global feature extraction while reducing trainable parameters. The decoding network incorporates Mix-Attention (MA), enabling multi-scale structural understanding and More >

  • Open Access

    ARTICLE

    Optimization of an Artificial Intelligence Database and Camera Installation for Recognition of Risky Passenger Behavior in Railway Vehicles

    Min-kyeong Kim, Yeong Geol Lee, Won-Hee Park, Su-hwan Yun, Tae-Soon Kwon, Duckhee Lee
    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1277-1293, 2025, DOI:10.32604/cmc.2024.058386
    (This article belongs to the Special Issue: Artificial Intelligence and Advanced Computation Technology in Railways)
    Abstract Urban railways are vital means of public transportation in Korea. More than 30% of metropolitan residents use the railways, and this proportion is expected to increase. To enhance safety, the government has mandated the installation of closed-circuit televisions in all carriages by 2024. However, cameras still monitored humans. To address this limitation, we developed a dataset of risk factors and a smart detection system that enables an immediate response to any abnormal behavior and intensive monitoring thereof. We created an innovative learning dataset that takes into account seven unique risk factors specific to Korean railway More >

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