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Machines
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Artificial Intelligence in Rail Transportation
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Artificial Intelligence in Rail Transportation

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Vehicle Engineering".

Deadline for manuscript submissions: closed (1 December 2025) | Viewed by 1091

Special Issue Editors

Dr. Ahmad Radmehr

E-Mail Website
Guest Editor
Center for Vehicle Systems and Safety (CVeSS), Virginia Tech, Blacksburg, VA 24060, USA
Interests: autonomous train operations; automated inspection systems; data analytics and big data in rail transportation; real-time monitoring of wheel–rail contact; wear prediction models
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mehdi Ahmadian

E-Mail Website
Guest Editor
Center for Vehicle Systems and Safety (CVeSS), Virginia Tech, Blacksburg, VA 24060, USA
Interests: smart materials and systems; advanced materials for improving ground vehicle dynamic performance and control; energy harvesting; vehicle system dynamics and control; intelligent suspensions; magneto-rheological fluids; biodynamics; railroad systems; health monitoring systems for railroad application; rail vehicle dynamics; rolling stock dynamics and mechanics analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue on "Artificial Intelligence in Rail Transportation" seeks to bring together pioneering research and case studies that highlight the diverse applications of Artificial Intelligence (AI) in rail transportation. In recent years, the integration of AI into railroad systems has shown significant improvements across various aspects of the industry, including safety, operational efficiency, sustainability, and maintenance, addressing long-lasting challenges. This Special Issue aims to collect the latest innovations, applications, and practical implementations of AI technologies in rail systems encouraging collaboration between academia and industry to accelerate advancements in rail transportation.

Researchers and practitioners are invited to submit original research articles, reviews, and case studies that push the boundaries of knowledge in these areas. This Special Issue aims to foster scholarly discourse and contribute substantively to the evolution of modern railway transportation systems.

Suitable topics include, but are not limited to, the following:

  • Autonomous Train Operations;
  • Automated Inspection Systems;
  • Computer Vision Applications for Rail Safety;
  • AI-Driven Predictive Maintenance in Rail Systems;
  • Data Analytics and Big Data in Rail Transportation;
  • Real-Time Monitoring of Wheel-Rail Contact;
  • AI in Energy Management and Sustainability;
  • Wear Prediction Models.

Dr. Ahmad Radmehr
Prof. Dr. Mehdi Ahmadian
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • autonomous train operations
  • automated inspection systems
  • computer vision applications for rail safety
  • AI-driven predictive maintenance in rail systems
  • data analytics and big data in rail transportation
  • real-time monitoring of wheel-rail contact
  • AI in energy management and sustainability
  • wear prediction models

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

17 pages, 608 KB  
Article
Physics-Informed Bayesian Inference for Virtual Testing and Prediction of Train Performance
by Kian Sepahvand, Christoph Schwarz, Oliver Urspruch and Frank Guenther
Machines 2026, 14(2), 211; https://doi.org/10.3390/machines14020211 - 11 Feb 2026
Viewed by 431
Abstract
This paper proposes a physics-informed Bayesian framework for virtual testing and predictive modeling of train performance, specifically addressing stopping-distance prediction. The approach unifies physical simulation models with data-driven statistical inference to achieve uncertainty-aware predictions under limited or noisy measurements. By embedding governing equations [...] Read more.
This paper proposes a physics-informed Bayesian framework for virtual testing and predictive modeling of train performance, specifically addressing stopping-distance prediction. The approach unifies physical simulation models with data-driven statistical inference to achieve uncertainty-aware predictions under limited or noisy measurements. By embedding governing equations of motion into a hierarchical Bayesian structure, the method systematically accounts for both model-form and data uncertainty, allowing explicit decomposition into aleatoric and epistemic components. A Gaussian process surrogate is employed to efficiently emulate high-fidelity physics simulations while preserving key dynamic behaviors and parameter sensitivities. The Bayesian formulation enables probabilistic calibration and validation, providing predictive distributions and confidence bounds. As a representative application, the framework is applied to the virtual prediction of train stopping distances, demonstrating how the proposed methodology captures nonlinear braking dynamics and quantifies uncertainty in safety-relevant performance metrics directly compatible with statistical verification standards such as EN 16834. The results confirm that the physics-informed Bayesian approach enables accurate, interpretable, and standards-aligned virtual testing across a wide range of dynamical systems. Full article
(This article belongs to the Special Issue Artificial Intelligence in Rail Transportation)
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