Artificial Intelligence for High-Quality Railway Development

Journal
Railway Sciences

Railways form the backbone of integrated national transport systems worldwide. The sector is undergoing a profound, industry-wide transformation driven by digitalisation and intelligent technologies. With countries worldwide pressing ahead with the modernisation of rail infrastructure to improve mobility, cut carbon emissions and bolster economic resilience, artificial intelligence (AI) has emerged as the defining enabling technology of the fourth industrial revolution.

This CFP aims to establish a premier international forum for interdisciplinary collaboration and knowledge exchange at the intersection of artificial intelligence and railway engineering. It will be published as a continuous collection and we cordially invite submissions from academics, researchers, engineers, and industry practitioners worldwide, presenting original research findings, practical case studies, and critical forward-looking analyses. By fostering global dialogue and cross-border knowledge sharing, we seek to collectively advance the state of the art in intelligent railway technologies and accelerate the transition towards a more efficient, safe, and connected rail network.

Submissions should centre on the theme of “artificial intelligence enabling high-quality railway development”, with an emphasis on original research, technological innovation, practical implementation, and future outlooks. 

We welcome contributions addressing, but not limited to, the following areas:

AI for Railway Safety

  • Intelligent inspection, defect identification, and early risk warning for railway infrastructure (tracks, bridges, tunnels, catenary systems, etc.), including computer vision-based defect detection and AI-driven safety systems.
  • Intelligent monitoring and management of train operational safety, covering fault identification for critical train components, scheduling optimisation under abnormal operating conditions, and dynamic assessment of operational safety risks.
  • Intelligent perception and emergency response to railway hazards (geological disasters, foreign object intrusion, extreme weather, etc.), and integrated management of construction safety on operating lines.
  • AI-driven railway emergency response, including smart emergency dispatching, emergency robotics, and digital simulation of emergency drills.  

AI for Railway Engineering Construction

  • Intelligent railway survey and design, including GIS and big data-enabled route selection, automated generation and intelligent evaluation of design alternatives, and end-to-end application of digital twin technology in railway construction.
  • Intelligent construction technologies, featuring the development and deployment of autonomous tunnelling, bridge erection, and maintenance robots, as well as prefabricated construction and intelligent production of ballastless track slabs.
  • Intelligent quality control for railway engineering, including material traceability, automated management of test samples, and precise construction process monitoring (e.g., blockchain and AIoT-integrated quality management systems).

AI for Railway Operation and Maintenance

  • Intelligent transport organisation, including integrated intelligent dispatching, automated train timetable generation, and dispatching optimisation for high-density, mixed-speed cross-line operations.
  • Intelligent equipment maintenance, covering predictive maintenance for EMUs, freight wagons, and track systems, and deployment of intelligent inspection robots (drones, underwater inspection robots, etc.).
  • Intelligent operation and maintenance technologies for heavy-haul, intercity, and suburban railways, as well as intelligent collaborative operation of multi-modal rail transit networks.
  • Application of railway computing power networks and knowledge graphs to operations and maintenance, enabling the shift from reactive to proactive equipment maintenance.

AI for Railway Passenger and Freight Services

  • Intelligent passenger services, including smart ticketing, automated security screening, in-station navigation, AI-powered customer service, and end-to-end journey services based on MaaS (Mobility as a Service) and 5G technology.
  • Intelligent freight system upgrades, covering automated freight collection and distribution, multimodal transport data sharing, real-time cargo tracking and traceability, and accurate freight demand forecasting.
  • Customer profiling and personalised service delivery, as well as intelligent services for cross-border rail transport such as the China-Europe Railway Express.

AI and Emerging Technology Integration in Railways

  • Convergence of artificial intelligence with BeiDou Navigation Satellite System (BDS), 5G, IoT, cloud computing, and blockchain to build a trusted intelligent railway ecosystem.
  • Development and application of railway-specific large language models (LLMs) and multimodal interaction technologies, including domain-specific knowledge question-answering and intelligent interactive services.
  • AI-enabled workforce training, such as VR-based simulation training and intelligent teaching assistance systems.
  • Research on technical standards, ethical frameworks, and safety assurance systems for railway AI, along with solutions to key challenges in technology deployment.

Submission Guidelines

We welcome submissions in three categories, all of which will undergo rigorous double-blind peer review:

  1. Research paper: Original, high-quality research presenting novel findings with clear academic and practical significance. Submissions must demonstrate well-structured arguments, robust evidence, reliable data, and rigorous logic, and must not have been previously published or be under consideration elsewhere.
  2. Case study: In-depth accounts of real-world AI implementation projects in the railway sector. Submissions should comprehensively cover the project context, technical approach, implementation process, measurable outcomes, and lessons learned that can be replicated.
  3. Review paper: Critical syntheses of the state-of-the-art, research hotspots, challenges, and future directions in a specific subfield of AI and railways. Reviews must offer original perspectives and in-depth analysis, rather than simply descriptive summaries. 

Author guidelines must be strictly followed. Please see:

Author guidelines

  • Manuscripts should follow APA style (reference, in-text citations, structured abstract formatting, captions, etc.). Please upload a short biography with a professional photo of the corresponding author along with the submission. All references must be accurate, complete, and formatted consistently in accordance with Emerald Publishing standards. Tables and figures should be uploaded in a separate file with a credit line below each item (e.g., "Source: author’s own work" or original source). Minimum 4,500 words in English (MS Word format), with at least 15 references is required.
  • Submissions must be original, unpublished works, and authors must warrant that they hold exclusive copyright. Plagiarism, self-plagiarism, and simultaneous submission to multiple journals are strictly prohibited. Authors bear full responsibility for any copyright infringement claims.
  • Please include the phrase “AI for railways” or related notes in the manuscript title upon submission.
  • For manuscripts with multiple authors, please clearly identify the corresponding author and provide their primary contact details. 

Submissions of full manuscripts are made using ScholarOne Manuscripts. Registration and access are available here:

Submit via ScholarOne
 
For inquiries please contact: [email protected]