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: 28 February 2025 | Viewed by 127
Special Issue Editors
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
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 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 100 words) can be sent to the Editorial Office for announcement on this website.
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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